cough-ml-Usama.ipynb

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  "metadata": {
    "colab": {
      "name": "Virufy_predictions(usama)_v1.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "3aGoA97aAj4f"
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      "toc_visible": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "accelerator": "TPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmJShC36KU4_",
        "colab_type": "text"
      },
      "source": [
        "#Testing Experiments\n",
        "> The portion from Data preprocessing till final checking is on the Data online Available\n",
        "\n",
        "> Final Trial on real Data  portions contain Expermient on Real Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZO8Uc-EVOMa1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfC-HOV3OX3w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from  sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import librosa \n",
        "import librosa.display as dis\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import numpy as np\n",
        "from tensorflow.keras import * "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJXL-A0XK_Cg",
        "colab_type": "text"
      },
      "source": [
        "#Data preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sH_lw9IQUnL4",
        "colab_type": "text"
      },
      "source": [
        "#Experiments on Online Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1roJRLnbQrUI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#loading data\n",
        "\n",
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "df = []\n",
        "for folder in [\"/content/drive/My Drive/Colab Notebooks/continuous_wavelet_transform/continuous_wavelet_transform/train/sick/**\",\n",
        "             \n",
        "               ]:\n",
        "  for fname in glob.iglob(folder):\n",
        "    if os.path.exists(fname):\n",
        "      label = os.path.basename(fname).split(\".\")[1]\n",
        "      if label  in [\"jpg\"]:\n",
        "      \n",
        "        df.append(\n",
        "              {\n",
        "                  \"filename \" : fname,\n",
        "               \"label\" : \"sick\" ,\n",
        "               \n",
        "              })\n",
        "      \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Sz80zZanKnT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "\n",
        "for folder in [\"/content/drive/My Drive/Colab Notebooks/continuous_wavelet_transform/continuous_wavelet_transform/train/not_sick/**\",\n",
        "             \n",
        "               ]:\n",
        "  for fname in glob.iglob(folder):\n",
        "    if os.path.exists(fname):\n",
        "      label = os.path.basename(fname).split(\".\")[1]\n",
        "      if label  in [\"jpg\"]:\n",
        "        df.append(\n",
        "              {\n",
        "                  \"filename \" : fname,\n",
        "               \"label\" : \"not_sick\" \n",
        "              })\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doPknOv-_Zi0",
        "colab_type": "code",
        "outputId": "c6a588d6-7e72-4995-dc8f-c932c9b82ce2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MR3nyz0LR130",
        "colab_type": "code",
        "outputId": "f2f14b9d-26ee-4960-f0fc-941e94a5ae1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "\n",
        "data=pd.DataFrame(df)\n",
        "\n",
        "print(data.count())\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "filename     4945\n",
            "label        4945\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           filename  label\n",
              "0  /content/drive/My Drive/Colab Notebooks/contin...  sick\n",
              "1  /content/drive/My Drive/Colab Notebooks/contin...  sick\n",
              "2  /content/drive/My Drive/Colab Notebooks/contin...  sick\n",
              "3  /content/drive/My Drive/Colab Notebooks/contin...  sick\n",
              "4  /content/drive/My Drive/Colab Notebooks/contin...  sick"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-i6H5J8WmcO0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "data['label'].value_counts().plot(kind = 'bar' , title = ' Distribution' )\n",
        "plt.show()\n",
        "print(data['label'].value_counts())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JiXc8vBMuxgf",
        "colab_type": "code",
        "outputId": "8401fc54-7b37-42b5-efae-1cbb6d24bd40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "from sklearn.utils import shuffle\n",
        "data =shuffle(data, random_state =48)\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1387</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2728</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>not_sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>759</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1049</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4288</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/contin...</td>\n",
              "      <td>not_sick</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              filename      label\n",
              "1387  /content/drive/My Drive/Colab Notebooks/contin...      sick\n",
              "2728  /content/drive/My Drive/Colab Notebooks/contin...  not_sick\n",
              "759   /content/drive/My Drive/Colab Notebooks/contin...      sick\n",
              "1049  /content/drive/My Drive/Colab Notebooks/contin...      sick\n",
              "4288  /content/drive/My Drive/Colab Notebooks/contin...  not_sick"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETtFrun12MfU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def duration_file(path):\n",
        "  y,sr = librosa.load(path)\n",
        "  dur = librosa.core.get_duration(y)\n",
        "  return dur\n",
        "\n",
        "\n",
        "from tqdm import tqdm\n",
        "duration= [ ]\n",
        "\n",
        "train = data.rename(columns={'filename ': 'filename'})\n",
        "for i in tqdm(range(len(train))):\n",
        "  duration.append(duration_file(train.filename.iloc[i]))\n",
        "  \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JV5YoPwuIY9w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "#val = []\n",
        "for folder in [\"/content/drive/My Drive/Colab Notebooks/continuous_wavelet_transform/continuous_wavelet_transform/validation/sick/**\",\n",
        "             \n",
        "               ]:\n",
        "  for fname in glob.iglob(folder):\n",
        "    if os.path.exists(fname):\n",
        "      label = os.path.basename(fname).split(\".\")[1]\n",
        "      if label  in [\"jpg\"]:\n",
        "        df.append(\n",
        "              {\n",
        "                  \"filename \" : fname,\n",
        "               \"label\" : \"sick\" \n",
        "              })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwNLJ9EaIh7F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "\n",
        "for folder in [\"/content/drive/My Drive/Colab Notebooks/continuous_wavelet_transform/continuous_wavelet_transform/validation/not_sick/**\",\n",
        "             \n",
        "               ]:\n",
        "  for fname in glob.iglob(folder):\n",
        "    if os.path.exists(fname):\n",
        "      label = os.path.basename(fname).split(\".\")[1]\n",
        "      if label  in [\"jpg\"]:\n",
        "        df.append(\n",
        "              {\n",
        "                  \"filename \" : fname,\n",
        "               \"label\" : \"not_sick\" \n",
        "              })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhQTUyxoItX4",
        "colab_type": "code",
        "outputId": "385112f4-02cf-4615-9cec-fb6ad493de66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "validation_data=pd.DataFrame(val)\n",
        "\n",
        "print(validation_data.count())\n",
        "validation_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "filename     1219\n",
            "label        1219\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>label</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                           filename  label\n",
              "0  /content/drive/My Drive/Colab Notebooks/Audio ...  sick\n",
              "1  /content/drive/My Drive/Colab Notebooks/Audio ...  sick\n",
              "2  /content/drive/My Drive/Colab Notebooks/Audio ...  sick\n",
              "3  /content/drive/My Drive/Colab Notebooks/Audio ...  sick\n",
              "4  /content/drive/My Drive/Colab Notebooks/Audio ...  sick"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFUVYmnKI482",
        "colab_type": "code",
        "outputId": "559aa131-cecc-47b3-b5c3-1ff08dccb8b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "validation_data['label'].value_counts().plot(kind = 'bar' , title = ' Distribution' )\n",
        "plt.show()\n",
        "print(validation_data['label'].value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "not_sick    752\n",
            "sick        467\n",
            "Name: label, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BNLNmh0I8A9",
        "colab_type": "code",
        "outputId": "ed256a53-7b14-4778-90f7-6ed7ee34cf3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "from sklearn.utils import shuffle\n",
        "validation_data =shuffle(data, random_state =40)\n",
        "validation_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2069</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>not_sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2892</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>not_sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1768</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>not_sick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1132</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>sick</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              filename      label\n",
              "2069  /content/drive/My Drive/Colab Notebooks/Audio ...  not_sick\n",
              "2892  /content/drive/My Drive/Colab Notebooks/Audio ...  not_sick\n",
              "126   /content/drive/My Drive/Colab Notebooks/Audio ...      sick\n",
              "1768  /content/drive/My Drive/Colab Notebooks/Audio ...  not_sick\n",
              "1132  /content/drive/My Drive/Colab Notebooks/Audio ...      sick"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtJUjyuSY6at",
        "colab_type": "text"
      },
      "source": [
        "#features extracting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TOqP1muDM3Y",
        "colab_type": "text"
      },
      "source": [
        "**Filter Design**\n",
        "* Low Pass\n",
        "* High Pass\n",
        "*Freq domain\n",
        "* Butterworth filter\n",
        "\n",
        "   **Ongoing**\n",
        "\n",
        "* Chebyshev filters\n",
        "*Kalman filters\n",
        "*Autoregressive filters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-n6X-KoIR_N1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from numpy.fft import *\n",
        "from scipy import fftpack\n",
        "\n",
        "def low_pass(path, threshold=1e6):\n",
        "    s,sr = librosa.load(path)\n",
        "    fourier = rfft(s)\n",
        "    frequencies = rfftfreq(s.size, d=2e-2/s.size)\n",
        "    fourier[frequencies > threshold] = 0\n",
        "    return irfft(fourier)\n",
        "\n",
        "def high_pass(s, threshold=1e7):\n",
        "    fourier = rfft(s)\n",
        "    frequencies = rfftfreq(s.size, d=2e-2/s.size)\n",
        "    fourier[frequencies < threshold] = 0\n",
        "    return irfft(fourier)  \n",
        "\n",
        "def freqdomain(s):\n",
        "  X = fftpack.fft(s,n=400)\n",
        "  freqs = fftpack.fftfreq(n=400,d=2e-2/s.size)\n",
        "  return freqs  \n",
        "\n",
        "import scipy.signal\n",
        "\n",
        "def butter_bandpass(lowcut, highcut, fs, order=5):\n",
        "    nyq= 0.5 * fs\n",
        "    low = lowcut / nyq\n",
        "    high = highcut / nyq\n",
        "    b, a = scipy.signal.butter(order, [low, high], btype='band')\n",
        "    return b, a\n",
        "def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
        "    b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
        "    y = scipy.signal.lfilter(b, a, data)\n",
        "    return y\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bs2FBw6TTeVY",
        "colab_type": "code",
        "outputId": "3d62d127-6fb6-4fc8-f020-4897657cba91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "import librosa\n",
        "import librosa.display\n",
        "path = '/content/neg-3.wav'\n",
        "x,sr = librosa.load('/content/neg-3.wav')\n",
        "librosa.display.waveplot(x,sr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PolyCollection at 0x7f49afd38ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLr_5KuZTylA",
        "colab_type": "code",
        "outputId": "24c51f6b-ae46-480b-80c9-ac40cd5d400f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "y1 = low_pass(path)\n",
        "librosa.display.waveplot(y1,sr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PolyCollection at 0x7f49afd0ecc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcXzpB4YUTNz",
        "colab_type": "code",
        "outputId": "3b786d16-b8a4-42f4-8c5f-9c7c555a27f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "y2 = butter_bandpass_filter(x,5000,10000,fs = sr,order=4)\n",
        "librosa.display.waveplot(y2,sr)  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PolyCollection at 0x7f49afdebef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHRoBXLwVYrW",
        "colab_type": "code",
        "outputId": "7e8660a7-a78c-4f8d-a430-cf293bdbd0e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "freq = freqdomain(x)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.magnitude_spectrum(freq,sr)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaSfqeSrkHaT",
        "colab_type": "code",
        "outputId": "2fc2125f-89d2-4e15-b835-8c327bfae04a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "import librosa\n",
        "import librosa.display\n",
        "path = '/content/neg-3.wav'\n",
        "signal,sr =librosa.load(path)\n",
        "librosa.display.waveplot(signal)\n",
        "trimmed, index = librosa.effects.trim(y2, top_db=20, frame_length=512, hop_length=64)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boK3Ga4JO-8Y",
        "colab_type": "text"
      },
      "source": [
        "**The Filtered and Trimmed Wave**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyrJ2ZJZlZhq",
        "colab_type": "code",
        "outputId": "42287f6c-05c0-4e60-8385-f28224c0fc91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "librosa.display.waveplot(trimmed)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PolyCollection at 0x7f49b05fc978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jm6YNMWSdNHq",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Filter features\n",
        "low_p=[]\n",
        "high_p=[]\n",
        "freqdom=[]\n",
        "freq_low=[]\n",
        "freq_high =[]\n",
        "def filter_features(path):\n",
        "  f,samp_rate = librosa.load(path=path,duration=4 )\n",
        "  \n",
        "  low_pass1=np.array(low_pass(f))\n",
        "  low_p.append(low_pass1)\n",
        "  \n",
        "  high_pass1=np.array(high_pass(f))\n",
        "  high_p.append(high_pass1)\n",
        "  \n",
        "  freq=np.array(freqdomain(f))\n",
        "  freqdom.append(freq)\n",
        "\n",
        "  freq_low=np.array(freqdomain(low_pass))\n",
        "  freq_low.append(freq_low)\n",
        "  \n",
        "  freq_high=np.array(freqdomain(high_pass))\n",
        "  freq_high.append(freq_high)\n",
        "\n",
        "  return low_p,high_p,freqdom,freq_high,freq_low\n",
        "  \n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YONLkl4awEm",
        "colab_type": "text"
      },
      "source": [
        "**RESEARCHING** \n",
        ">Neural Network Approach by inputing the noising into \n",
        "CNN network signal and then calculating the MSE loss "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbOrvROybEb8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJSxJuaTQNqT",
        "colab_type": "text"
      },
      "source": [
        "#Features Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0jGxn73CQURg"
      },
      "source": [
        "I  have Extract different Features down below\n",
        "* rms_energy \n",
        "* zcr \n",
        "* spectral_energy \n",
        "* centroid\n",
        "* mfcc\n",
        "* rolloff\n",
        "*pitch\n",
        "*melspectrogram\n",
        "*Constant-Q chromagram\n",
        "*DWT "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBo2LS-tVidg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "rms = []\n",
        "zcr = []\n",
        "spectral_energy = []\n",
        "centroid=[]\n",
        "mfcc=[]\n",
        "rolloff=[]\n",
        "pitch=[]\n",
        "mel=[]\n",
        "chroma1 = []\n",
        "\n",
        "dr =10 \n",
        "\n",
        "def features1(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.spectral_centroid(x,sr)\n",
        "  return values\n",
        "\n",
        "def features2(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values = librosa.core.zero_crossings(x)\n",
        "  return values\n",
        "\n",
        "def features3(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.spectral_rolloff(y=x,sr=sr)\n",
        "  return values\n",
        "\n",
        "def features4(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values, magnitudes = librosa.piptrack(y=x, sr=sr)\n",
        "  return values\n",
        "\n",
        "def features5(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.mfcc(x,sr,n_mfcc=25)\n",
        "  return values\n",
        "\n",
        "\n",
        "def features6(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.rms(y=x)\n",
        "\n",
        "def features7(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.melspectrogram(y=x,sr=sr)\n",
        "  return values\n",
        "\n",
        "\n",
        "def features8(path):\n",
        "  x,sr = librosa.load(path)\n",
        "  values =librosa.get_duration(y=x,sr=sr)\n",
        "  return values\n",
        "def melfeatures(path):\n",
        "  f,samp_rate = librosa.load(path=path,duration = dr)\n",
        "  mel = librosa.feature.melspectrogram(y=f ,sr = samp_rate)\n",
        "  return mel\n",
        "def chroma(path):\n",
        "  f,samp_rate = librosa.load(path=path,duration = 10)\n",
        "  mfccs = librosa.feature.chroma_cqt(y=f,sr=samp_rate)\n",
        "  return mfccs \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QR23CqlXQCYm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import librosa\n",
        "train = data.rename(columns={'filename ': 'filename'})\n",
        "for i in tqdm(range(len(train))):\n",
        "  rms.append(features6(train.filename.iloc[i]))\n",
        "  zcr.append(features2(train.filename.iloc[i])) \n",
        "  mfcc.append(features5(train.filename.iloc[i])) \n",
        "  centroid.append(features1(train.filename.iloc[i])) \n",
        "  spectral_energy.append(features5(train.filename.iloc[i]))\n",
        "  rolloff.append(features3(train.filename.iloc[i])) \n",
        "  pitch.append(features4(train.filename.iloc[i]))\n",
        "  mel.append(melfeatures(train.filename.iloc[i]))\n",
        "  chroma1.append(chroma(train.filename.iloc[i]))\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UH7q1xGMxqCK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "def melfeatures(path):\n",
        "  f,samp_rate = librosa.load(path=path,duration = 4)\n",
        "  mel = librosa.feature.melspectrogram(y=f ,sr = samp_rate)\n",
        "  return mel\n",
        "def pitch_features(path):\n",
        "  y, sr = librosa.load(path ,duration = 4)\n",
        "  pitches, magnitudes = librosa.core.piptrack(y, sr)\n",
        "  return pitches\n",
        "\n",
        "def mfcc_features(path):\n",
        "\n",
        "  f,samp_rate = librosa.load(path=path,duration=4 )\n",
        "  mfccs = librosa.feature.mfcc(y=f,sr=samp_rate,n_mfcc=12)\n",
        "  return mfccs\n",
        "\n",
        "\n",
        " \n",
        "\n",
        "\n",
        "def features(path):\n",
        "\n",
        "  f,samp_rate = librosa.load(path=path,duration = 4)\n",
        "  mfccs = librosa.feature.chroma_cqt(y=f,sr=samp_rate)\n",
        "  return mfccs   \n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkffvXjZ6oR7",
        "colab_type": "code",
        "outputId": "b7b76962-516f-41b4-cba4-c2415e494254",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "meltrain = np.array(meltrain)\n",
        "meltrain.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3716, 128, 173)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71gjqq3baMYE",
        "colab_type": "code",
        "outputId": "56d1762e-5088-457d-8a01-8b405cb0c930",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "concat1 = np.concatenate((mfcc,meltrain),axis =1)\n",
        "concat1.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3716, 168, 173)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kX4IkoxcZE2t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dwt_features(path):\n",
        "  f,samp_rate = librosa.load(path=path)\n",
        "  A,D=  pywt.dwt()\n",
        "  return D  \n",
        "  \n",
        "import cv2\n",
        "data_cwt=[]\n",
        "labels=[]\n",
        "from tqdm import tqdm\n",
        "\n",
        "for f in tqdm(df):\n",
        "  file = list(f.values())\n",
        "  file_path = file[0]\n",
        "  label = file[1]\n",
        "  image = cv2.imread(file_path)\n",
        "  #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "  #image = cv2.resize(image, (227, 227))\n",
        "  \n",
        "  data_cwt.append(image)\n",
        "  labels.append(label)\n",
        "\n",
        "data_cwt = np.array(data_cwt)\n",
        "labels = np.array(labels)\n",
        "\n",
        "import numpy as np \n",
        "data_cwt = np.array(data_cwt)\n",
        "labels = np.array(labels)\n",
        "data_cwt.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-m21VUuV-lqs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "import librosa.core\n",
        "y, sr = librosa.load('/content/drive/My Drive/Colab Notebooks/Audio Data/train/sick/audioset_-J60og_pQtQ_0_5.wav',duration =5)\n",
        "pitches, magnitudes = librosa.core.piptrack(y, sr)\n",
        "\n",
        "pitches1 = pitches[magnitudes > np.median(magnitudes)]\n",
        "pitches2 = librosa.pitch_tuning(pitches1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tt22GGdASXrK",
        "colab_type": "code",
        "outputId": "e9a32b31-f2d5-4454-d666-593ecd11f8fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "import librosa.display\n",
        "librosa.display.waveplot(pitches1,sr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PolyCollection at 0x7f4fa70e2710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRHhkpM3WU9h",
        "colab_type": "code",
        "outputId": "58242d8d-54cd-44fa-8875-b05bde5b66ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "import librosa.display\n",
        "mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)\n",
        "librosa.display.specshow(mfcc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f6f486be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Q4sBnh7W0_4",
        "colab_type": "code",
        "outputId": "3b576494-b412-4bd9-a071-bac705ffd2d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "import librosa.display\n",
        "chroma = librosa.feature.chroma_cqt(y=y, sr=sr)\n",
        "librosa.display.specshow(chroma)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f6f3e8d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8WWRl-EW_4X",
        "colab_type": "code",
        "outputId": "9bf09ae6-b252-4840-c556-842062d7fbcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "import librosa.display\n",
        "chroma = librosa.feature.melspectrogram(y=y, sr=sr)\n",
        "librosa.display.specshow(chroma)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f6f3ab198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvA3MY6J-vO2",
        "colab_type": "code",
        "outputId": "4e48e168-2d5e-40ed-ffab-67648136236e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from tqdm import tqdm\n",
        "pitchfeatures = []\n",
        "train = data.rename(columns={'filename ': 'filename'})\n",
        "for i in tqdm(range(len(train))):\n",
        "  pitchfeatures.append(pitch_features(train.filename.iloc[i]))  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3716/3716 [33:06<00:00,  1.87it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6DYifbAG9K4",
        "colab_type": "code",
        "outputId": "2ec15af5-8073-49ea-8862-4f881194b165",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pitchfeatures=np.array(pitchfeatures)\n",
        "pitchfeatures.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3716, 1025, 173)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LKKESBIoBs9C",
        "colab_type": "text"
      },
      "source": [
        "#Data split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxXrkpGDBx0W",
        "colab_type": "code",
        "outputId": "eb821073-2b72-41ed-8ae3-f27f356ffc9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from keras.utils import to_categorical\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "lb = LabelBinarizer()\n",
        "labels = lb.fit_transform(data.label)\n",
        "labelss = to_categorical(labels)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0juBiJJF9yHu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "(x_train, xtest, ytrain, ytest) = train_test_split(pitchfeatures, labels,\n",
        "\ttest_size=0.40, stratify=labels, random_state=40)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prZWZklgCAbq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train =Xtrain.reshape(Xtrain.shape[0],Xtrain.shape[1],Xtrain.shape[2],1)\n",
        "xtest =xtest.reshape(xtest.shape[0],xtest.shape[1],xtest.shape[2],1)\n",
        "print(ytrain.shape)\n",
        "x_train.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8eldIxX8Bb4v",
        "colab_type": "text"
      },
      "source": [
        "#Deep Learning Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIxbe1anBhFf",
        "colab_type": "code",
        "outputId": "945509c8-c18c-41ad-b166-d2ce2bd2cae9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "from keras.layers import *\n",
        "model = Sequential()\n",
        "model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu'),input_shape=(x_train.shape[1],x_train.shape[2],x_train.shape[3]) ))\n",
        "model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu')))\n",
        "model.add(TimeDistributed(Dropout(0.5)))\n",
        "model.add(TimeDistributed(MaxPooling1D(pool_size=2)))\n",
        "model.add(TimeDistributed(Flatten()))\n",
        "model.add(LSTM(100,return_sequences=True))\n",
        "model.add(LSTM(100,return_sequences=True))\n",
        "model.add(LSTM(100))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dense(2, activation='softmax'))\n",
        "model.summary()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_10\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "time_distributed_2 (TimeDist (None, 2, 10, 64)         33280     \n",
            "_________________________________________________________________\n",
            "time_distributed_3 (TimeDist (None, 2, 8, 64)          12352     \n",
            "_________________________________________________________________\n",
            "time_distributed_4 (TimeDist (None, 2, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "time_distributed_5 (TimeDist (None, 2, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "time_distributed_6 (TimeDist (None, 2, 256)            0         \n",
            "_________________________________________________________________\n",
            "lstm_9 (LSTM)                (None, 2, 100)            142800    \n",
            "_________________________________________________________________\n",
            "lstm_10 (LSTM)               (None, 2, 100)            80400     \n",
            "_________________________________________________________________\n",
            "lstm_11 (LSTM)               (None, 100)               80400     \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_30 (Dense)             (None, 2)                 202       \n",
            "=================================================================\n",
            "Total params: 359,534\n",
            "Trainable params: 359,534\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwGI2Ht3lIPT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Layer\n",
        "import keras.backend as K\n",
        "class attention(Layer):\n",
        "    def __init__(self,**kwargs):\n",
        "        super(attention,self).__init__(**kwargs)\n",
        "\n",
        "    def build(self,input_shape):\n",
        "        self.W=self.add_weight(name=\"attention_weight\",shape=(input_shape[-1],1),initializer=\"normal\")\n",
        "        self.b=self.add_weight(name=\"attention_bias\",shape=(input_shape[1],1),initializer=\"zeros\")        \n",
        "        super(attention, self).build(input_shape)\n",
        "\n",
        "    def call(self,x):\n",
        "        out=K.squeeze(K.tanh(K.dot(x,self.W)+self.b),axis=-1)\n",
        "        at=K.softmax(out)\n",
        "        at=K.expand_dims(at,axis=-1)\n",
        "        output=x*at\n",
        "        return K.sum(output,axis=1)\n",
        "\n",
        "    def compute_output_shape(self,input_shape):\n",
        "        return (input_shape[0],input_shape[-1])\n",
        "\n",
        "    def get_config(self):\n",
        "        return super(attention,self).get_config()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUGgjX28BnRc",
        "colab_type": "code",
        "outputId": "78a09275-9aa3-41a4-97fa-ba8f3586e763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "from keras import *\n",
        "from keras.layers import *\n",
        "model = Sequential()\n",
        "model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu'),input_shape=(227,227,3) ))\n",
        "model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu')))\n",
        "model.add(TimeDistributed(Dropout(0.5)))\n",
        "model.add(TimeDistributed(MaxPooling1D(pool_size=2)))\n",
        "model.add(TimeDistributed(Flatten()))\n",
        "model.add(LSTM(100,return_sequences=True))\n",
        "model.add(LSTM(100,return_sequences=True,name = 'lstm2'))\n",
        "layer_name = 'lstm2'\n",
        "\n",
        "out1 =model.get_layer(layer_name).output\n",
        "attention = attention()(out1)\n",
        "out1 = (Dense(100, activation='relu'))(attention)\n",
        "out2 = (Dense(2, activation='softmax'))(out1)\n",
        "final_model=Model(inputs = model.input,outputs = out2)\n",
        "final_model.summary()\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "time_distributed_21_input (I (None, 227, 227, 3)       0         \n",
            "_________________________________________________________________\n",
            "time_distributed_21 (TimeDis (None, 227, 225, 64)      640       \n",
            "_________________________________________________________________\n",
            "time_distributed_22 (TimeDis (None, 227, 223, 64)      12352     \n",
            "_________________________________________________________________\n",
            "time_distributed_23 (TimeDis (None, 227, 223, 64)      0         \n",
            "_________________________________________________________________\n",
            "time_distributed_24 (TimeDis (None, 227, 111, 64)      0         \n",
            "_________________________________________________________________\n",
            "time_distributed_25 (TimeDis (None, 227, 7104)         0         \n",
            "_________________________________________________________________\n",
            "lstm_7 (LSTM)                (None, 227, 100)          2882000   \n",
            "_________________________________________________________________\n",
            "lstm2 (LSTM)                 (None, 227, 100)          80400     \n",
            "_________________________________________________________________\n",
            "attention_2 (attention)      (None, 100)               327       \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 2)                 202       \n",
            "=================================================================\n",
            "Total params: 2,986,021\n",
            "Trainable params: 2,986,021\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1xfb9p1bV-J",
        "colab_type": "code",
        "outputId": "02a4c351-7fa4-40df-c608-17f90a465625",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "from keras import *\n",
        "from keras.layers import *\n",
        "model = Sequential()\n",
        "model.add(Conv1D(filters=64, kernel_size=3, activation='relu',input_shape=(1308, 431 )) )\n",
        "model.add((Conv1D(filters=64, kernel_size=3, activation='relu')))\n",
        "model.add((Conv1D(filters=64, kernel_size=3, activation='relu')))\n",
        "\n",
        "model.add((Dropout(0.5)))\n",
        "model.add((MaxPooling1D(pool_size=2)))\n",
        "#model.add((Flatten()))\n",
        "model.add(LSTM(100,return_sequences=True))\n",
        "model.add(LSTM(100,return_sequences=True,name = 'lstm2'))\n",
        "model.add(LSTM(100,return_sequences=True,name = 'ls3'))\n",
        "layer_name = 'lstm2'\n",
        "\n",
        "out1 =model.get_layer(layer_name).output\n",
        "\n",
        "#attention = attention()(out1)\n",
        "out1 = Flatten()(out1)\n",
        "out1 = (Dense(100, activation='relu'))(out1)\n",
        "out1 = (Dense(100, activation='relu'))(out1)\n",
        "out1 = (Dense(100, activation='relu'))(out1)\n",
        "out2 = (Dense(2, activation='softmax'))(out1)\n",
        "finalmodel_1=Model(inputs = model.input,outputs = out2)\n",
        "finalmodel_1.summary()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_7_input (InputLayer)  (None, 1308, 431)         0         \n",
            "_________________________________________________________________\n",
            "conv1d_7 (Conv1D)            (None, 1306, 64)          82816     \n",
            "_________________________________________________________________\n",
            "conv1d_8 (Conv1D)            (None, 1304, 64)          12352     \n",
            "_________________________________________________________________\n",
            "conv1d_9 (Conv1D)            (None, 1302, 64)          12352     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 1302, 64)          0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_2 (MaxPooling1 (None, 651, 64)           0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 651, 100)          66000     \n",
            "_________________________________________________________________\n",
            "lstm2 (LSTM)                 (None, 651, 100)          80400     \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 65100)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               6510100   \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 2)                 202       \n",
            "=================================================================\n",
            "Total params: 6,784,422\n",
            "Trainable params: 6,784,422\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ambd4NUDpPKi",
        "colab_type": "code",
        "outputId": "69a56872-df7a-4e78-e028-2d88db4d1d47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "from keras import *\n",
        "from keras.layers import *\n",
        "model = Sequential()\n",
        "model.add(Conv1D(filters=64, kernel_size=3,kernel_regularizer=regularizers.l2(0.001), activation='relu',input_shape=(Xtrain.shape[1],Xtrain.shape[2] )) )\n",
        "model.add((Conv1D(filters=64, kernel_size=3, kernel_regularizer=regularizers.l2(0.001),activation='relu')))\n",
        "model.add(BatchNormalization(momentum=0.99))\n",
        "model.add((Dropout(0.5)))\n",
        "model.add((MaxPooling1D(pool_size=2)))\n",
        "#model.add((Flatten()))\n",
        "model.add(LSTM(100,return_sequences=True))\n",
        "model.add(LSTM(100,return_sequences=True,name = 'lstm2'))\n",
        "layer_name = 'lstm2'\n",
        "\n",
        "out1 =model.get_layer(layer_name).output\n",
        "\n",
        "attention = attention()(out1)\n",
        "out1 = (Dense(100, activation='relu',kernel_regularizer=regularizers.l2(0.001)))(attention)\n",
        "out2 = (Dense(2, activation='softmax'))(out1)\n",
        "final_model2=Model(inputs = model.input,outputs = out2)\n",
        "final_model2.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d_3_input (InputLayer)  (None, 68, 159)           0         \n",
            "_________________________________________________________________\n",
            "conv1d_3 (Conv1D)            (None, 66, 64)            30592     \n",
            "_________________________________________________________________\n",
            "conv1d_4 (Conv1D)            (None, 64, 64)            12352     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 64, 64)            256       \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64, 64)            0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_2 (MaxPooling1 (None, 32, 64)            0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 32, 100)           66000     \n",
            "_________________________________________________________________\n",
            "lstm2 (LSTM)                 (None, 32, 100)           80400     \n",
            "_________________________________________________________________\n",
            "attention_1 (attention)      (None, 100)               132       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 2)                 202       \n",
            "=================================================================\n",
            "Total params: 200,034\n",
            "Trainable params: 199,906\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UILG_pQzjbLO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import *\n",
        "from keras import *\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters=16, kernel_size=2, input_shape=(x_train.shape[1],x_train.shape[2],3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=2))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=2, activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=2))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=2, activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=2))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv2D(filters=128, kernel_size=2, activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=2))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(GlobalAveragePooling2D())\n",
        "\n",
        "\n",
        "model.add(Dense(2, activation='softmax'))\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Skn5cOAEh0w",
        "colab_type": "text"
      },
      "source": [
        "#Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVX9RTptEkh1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer='adam')\n",
        "\n",
        "history = model.fit(x_train, ytrain,\n",
        "              batch_size=100,\n",
        "            epochs=10,\n",
        "              validation_data=(xtest,ytest),\n",
        "              shuffle=True,\n",
        "              verbose=1\n",
        "            )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AUWUqTLpxMX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "adam=keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False, clipnorm=1.0)\n",
        "final_model2.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer=adam)\n",
        "\n",
        "history = final_model2.fit(x_train, ytrain,\n",
        "              batch_size=10,\n",
        "            epochs=40,\n",
        "              validation_data=(xtest,ytest),\n",
        "              shuffle=True,\n",
        "              verbose=1\n",
        "            )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SCQZ0DnwqrD",
        "colab_type": "code",
        "outputId": "5ad5270f-1374-4ace-da60-d6bc99518df2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "plt.plot(epochs, acc, 'b', color='red',label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b',color='blue', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, '',color='red', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b',color='blue', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVj7OGOyK3rP",
        "colab_type": "text"
      },
      "source": [
        "# Test Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsd9nkthpVBd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "mfcctest = []\n",
        "\n",
        "validation_data = validation_data.rename(columns={'filename ': 'filename'})\n",
        "for i in tqdm(range(len(validation_data))):\n",
        "  mfcctest.append(features(validation_data.filename.iloc[i]))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxPpAw2WMJ2p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "meltest = []\n",
        "validation_data =validation_data.rename(columns={'filename ': 'filename'})\n",
        "for i in tqdm(range(len(validation_data))):\n",
        "  meltest.append(newfeatures(validation_data.filename.iloc[i]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FNOrp46Miq2",
        "colab_type": "code",
        "outputId": "0def9912-220d-42f6-ae11-b739a723b556",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "meltest = np.array(meltest)\n",
        "print(meltest.shape)\n",
        "\n",
        "mfcctest = np.load('/content/drive/My Drive/Colab Notebooks/Audio Data/mfcctest.npy')\n",
        "print(mfcctest.shape)\n",
        "concat1_test = np.concatenate((mfcctest,meltest),axis =1)\n",
        "print(concat1_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1219, 128, 173)\n",
            "(1219, 40, 173)\n",
            "(1219, 168, 173)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yE7q0HdiuyCX",
        "colab_type": "code",
        "outputId": "4489bc0e-5a8a-4cbf-b041-0b4f8be9e52f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "labels.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1219, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ic0P1yGxszH6",
        "colab_type": "code",
        "outputId": "8985f4f6-2eb6-439c-86e6-3663ce616a21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Increasing Training data\n",
        "new_train=(np.concatenate((concat1,concat1_test),axis =0))\n",
        "print(new_train.shape)\n",
        "new_label=(np.concatenate((labels,label_test),axis =0))\n",
        "print(new_label.shape)\n",
        "(x_train, xtest, ytrain, ytest) = train_test_split(new_train, new_label,\n",
        "\ttest_size=0.20, random_state=32)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4935, 168, 173)\n",
            "(4935, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkzs13stM79X",
        "colab_type": "code",
        "outputId": "433924df-685b-4dfd-dcd9-939f6a6ca67c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "lb = LabelBinarizer()\n",
        "labels1 = lb.fit_transform(validation_data.label)\n",
        "label_test = to_categorical(labels1)\n",
        "test_data = concat1_test\n",
        "test_data = test_data.reshape(1219,168,173)\n",
        "print(label_test.shape )\n",
        "test_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1219, 2)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1219, 168, 173)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UponfMEcZUyr",
        "colab_type": "code",
        "outputId": "c1cfa712-2220-4656-bef4-5161fa73f31c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "scores = final_model2.evaluate(test_data, label_test, verbose=1)\n",
        "\n",
        "print('Test accuracy:', scores[1]*100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1219/1219 [==============================] - 2s 2ms/step\n",
            "Test accuracy: 71.6160774230957\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-D6o4EpElu-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred = model.predict(test_data, batch_size=100)\n",
        "pred = np.argmax(pred, axis=1)\n",
        "from sklearn.metrics import accuracy_score\n",
        "acc = accuracy_score(label_test,pred)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_E28US8XjkK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "acc = accuracy_score(label_test,pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_y2ghYGWdD1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(classification_report(label_test.argmax(axis=1), pred,\n",
        "\ttarget_names=lb.classes_))\n",
        "cm = confusion_matrix(label_test.argmax(axis=1), pred)\n",
        "total = sum(sum(cm))\n",
        "acc = (cm[0, 0] + cm[1, 1]) / total\n",
        "sensitivity = cm[0, 0] / (cm[0, 0] + cm[0, 1])\n",
        "specificity = cm[1, 1] / (cm[1, 0] + cm[1, 1])\n",
        "print(cm)\n",
        "print(\"acc:\" ,acc*100)\n",
        "print(\"sensitivity: {:.4f}\".format(sensitivity))\n",
        "print(\"specificity: {:.4f}\".format(specificity))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVL5wkK4D4lL",
        "colab_type": "text"
      },
      "source": [
        "**Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dtwVeFxxds6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combined_features[np.isnan(combined_features)]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2L-qD5uaNWOG",
        "colab_type": "code",
        "outputId": "34469608-23ca-4648-bd42-7cbee8548093",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train.max()\n",
        "combined_features = np.reshape(features,(3716,-1))\n",
        "combined_features.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3716, 26296)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WctGWFqkGoh",
        "colab_type": "code",
        "outputId": "b2f0f7b3-dcbf-4033-d601-b58492c7ae91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scalar = MinMaxScaler()\n",
        "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "imp_mean.fit(combined_features)\n",
        "combined_features = imp_mean.transform(combined_features)\n",
        "scalar.fit(combined_features)\n",
        "combined_features = scalar.transform(combined_features)\n",
        "combined_features.max()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xIUY_bcqfqt",
        "colab_type": "code",
        "outputId": "cc23cad4-c54b-488d-dd01-c7abbb1e4553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "scalar.fit(combined_features)\n",
        "combined_features = scalar.transform(combined_features)\n",
        "combined_features.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3716, 26296)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3aGoA97aAj4f",
        "colab_type": "text"
      },
      "source": [
        "#Final Checking"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKla-lQHAmha",
        "colab_type": "code",
        "outputId": "8c11f450-1d58-48d4-8760-2198dd6ee962",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import librosa\n",
        "path= '/content/positiveimprove3.wav'\n",
        "testing1 = features(path)\n",
        "testing2= melfeatures(path)\n",
        "testing3=mfcc_features(path)\n",
        "print(testing1.shape)\n",
        "print(testing2.shape)\n",
        "print(testing3.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12, 173)\n",
            "(128, 173)\n",
            "(12, 173)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7HSXHQSBUkJ",
        "colab_type": "code",
        "outputId": "ac4a1fed-d056-4fef-d23d-aacab5c3afe2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "finaltesting = np.concatenate((testing1,testing2,testing3),axis=0)\n",
        "print(finaltesting.shape)\n",
        "finaltesting = finaltesting.reshape(1,finaltesting.shape[0],finaltesting.shape[1])\n",
        "\n",
        "finaltesting.shape\n",
        "finaltesting = np.reshape(finaltesting,(1,-1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(152, 173)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aP4dpBfdVumw",
        "colab_type": "code",
        "outputId": "fd749b93-1016-47a6-c2da-f5198194adee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Rd.predict(finaltesting)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WS-L8bxs72_i",
        "colab_type": "text"
      },
      "source": [
        "#Experiments On Real Data\n",
        "\n",
        "> working on 32 samples \n",
        "\n",
        "> working on 64 samples\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eOSvYxAek4py",
        "colab_type": "text"
      },
      "source": [
        "#First Trial on Real Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwTucywM0vI0",
        "colab_type": "text"
      },
      "source": [
        "> This is first trial of training on real data in this \n",
        "\n",
        "> I have 32 samples 16 positive and 16 negative samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6F27q3G1KLM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddoJ-1iI1KqB",
        "colab_type": "text"
      },
      "source": [
        "**Data Extracting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oy_eVbPm8K-c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "df = []\n",
        "for folder in ['/content/drive/My Drive/Colab Notebooks/Audio Data/COVID-19 Converted/COVID-19 Converted/Postive-con/**',\n",
        "             \n",
        "               ]:\n",
        "  for fname in glob.iglob(folder):\n",
        "    if os.path.exists(fname):\n",
        "      label = os.path.basename(fname).split(\".\")[1]\n",
        "      if label  in [\"wav\"]:\n",
        "      \n",
        "        df.append(\n",
        "              {\n",
        "                  \"filename \" : fname,\n",
        "               \"label\" : \"positve\" ,\n",
        "               \n",
        "              })\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xA508rLy8Wtl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "for folder in [\"/content/drive/My Drive/Colab Notebooks/Audio Data/Real/coughs/Neg/**\",\n",
        "             \n",
        "               ]:\n",
        "  for fname in glob.iglob(folder):\n",
        "    if os.path.exists(fname):\n",
        "      label = os.path.basename(fname).split(\".\")[1]\n",
        "      \n",
        "      if label  in [\"wav\"]:\n",
        "      \n",
        "        df.append(\n",
        "              {\n",
        "                  \"filename \" : fname,\n",
        "               \"label\" : \"negative\" ,\n",
        "               \n",
        "              })\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCoOkeae87Oa",
        "colab_type": "code",
        "outputId": "61ddef68-2ee1-48d8-8bd2-4a280b856f81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import pandas as pd\n",
        "data = pd.DataFrame(df)\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           filename     label\n",
              "0  /content/drive/My Drive/Colab Notebooks/Audio ...  positve\n",
              "1  /content/drive/My Drive/Colab Notebooks/Audio ...  positve\n",
              "2  /content/drive/My Drive/Colab Notebooks/Audio ...  positve\n",
              "3  /content/drive/My Drive/Colab Notebooks/Audio ...  positve\n",
              "4  /content/drive/My Drive/Colab Notebooks/Audio ...  positve"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_pHOq999NfT",
        "colab_type": "code",
        "outputId": "c5eb71f9-a285-40b6-b054-aa1c63bf2676",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize = (12,6))\n",
        "data['label'].value_counts().plot(kind = 'bar' , title = ' Distribution' )\n",
        "plt.show()\n",
        "print(data['label'].value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "negative    16\n",
            "positve     16\n",
            "Name: label, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CqF-diB9gCA",
        "colab_type": "code",
        "outputId": "3e45e292-3351-4ab7-eeb4-1f4d5591e7f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "from sklearn.utils import shuffle\n",
        "data =shuffle(data, random_state =48)\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>/content/drive/My Drive/Colab Notebooks/Audio ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            filename      label\n",
              "29  /content/drive/My Drive/Colab Notebooks/Audio ...  negative\n",
              "25  /content/drive/My Drive/Colab Notebooks/Audio ...  negative\n",
              "9   /content/drive/My Drive/Colab Notebooks/Audio ...   positve\n",
              "1   /content/drive/My Drive/Colab Notebooks/Audio ...   positve\n",
              "18  /content/drive/My Drive/Colab Notebooks/Audio ...  negative"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OOInmJGm1R2r",
        "colab_type": "text"
      },
      "source": [
        "**Features Extraction**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "E7ZMFlqjNcZS"
      },
      "source": [
        "I  have Extract different Features down below\n",
        "* rms\n",
        "* zcr \n",
        "* spectral_energy \n",
        "* centroid\n",
        "* mfcc\n",
        "* rolloff\n",
        "*pitch\n",
        "*melspectrogram\n",
        "*chroma\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhvZwfCHFhC5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "rms_energy = []\n",
        "zcr = []\n",
        "spectral_energy = []\n",
        "centroid=[]\n",
        "mfcc=[]\n",
        "rolloff=[]\n",
        "pitch=[]\n",
        "mel=[]\n",
        "chroma1 = []\n",
        "\n",
        "dr =10 \n",
        "\n",
        "def features1(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.spectral_centroid(x,sr)\n",
        "  return values\n",
        "\n",
        "def features2(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values = librosa.core.zero_crossings(x)\n",
        "  return values\n",
        "\n",
        "def features3(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.spectral_rolloff(y=x,sr=sr)\n",
        "  return values\n",
        "\n",
        "def features4(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values, magnitudes = librosa.piptrack(y=x, sr=sr)\n",
        "  return values\n",
        "\n",
        "def features5(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.mfcc(x,sr,n_mfcc=25)\n",
        "  return values\n",
        "\n",
        "\n",
        "def features6(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.rms(y=x)\n",
        "\n",
        "def features7(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.melspectrogram(y=x,sr=sr)\n",
        "  return values\n",
        "\n",
        "\n",
        "def features8(path):\n",
        "  x,sr = librosa.load(path)\n",
        "  values =librosa.get_duration(y=x,sr=sr)\n",
        "  return values\n",
        "def melfeatures(path):\n",
        "  f,samp_rate = librosa.load(path=path,duration = dr)\n",
        "  mel = librosa.feature.melspectrogram(y=f ,sr = samp_rate)\n",
        "  return mel\n",
        "def chroma(path):\n",
        "  f,samp_rate = librosa.load(path=path,duration = 10)\n",
        "  mfccs = librosa.feature.chroma_cqt(y=f,sr=samp_rate)\n",
        "  return mfccs \n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JR22eyPMXn-B",
        "colab_type": "code",
        "outputId": "f18c5758-42ab-49a4-9382-5313e5461139",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "import librosa\n",
        "train = data.rename(columns={'filename ': 'filename'})\n",
        "for i in tqdm(range(len(train))):\n",
        "  rms_energy.append(features6(train.filename.iloc[i]))\n",
        "  zcr.append(features2(train.filename.iloc[i])) \n",
        "  mfcc.append(features5(train.filename.iloc[i])) \n",
        "  centroid.append(features1(train.filename.iloc[i])) \n",
        "  spectral_energy.append(features5(train.filename.iloc[i]))\n",
        "  rolloff.append(features3(train.filename.iloc[i])) \n",
        "  pitch.append(features4(train.filename.iloc[i]))\n",
        "  mel.append(melfeatures(train.filename.iloc[i]))\n",
        "  chroma1.append(chroma(train.filename.iloc[i]))\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgHMDLZURzgs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "rms_energy = np.array(rms_energy)\n",
        "zcr = np.array(zcr)\n",
        "spectral_energy= np.array(spectral_energy)\n",
        "centroid = np.array(centroid)\n",
        "mfcc= np.array(mfcc)\n",
        "rolloff=np.array(rolloff)\n",
        "pitch= np.array(pitch)\n",
        "mel=np.array(mel)\n",
        "chroma1 = np.array(chroma1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gU1T9gLJPM-i",
        "colab_type": "code",
        "outputId": "0f82c389-5a0f-4375-a29d-8f4464962841",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "print(rms_energy.shape)\n",
        "print(zcr.shape)\n",
        "print(spectral_energy.shape)\n",
        "print(centroid.shape)\n",
        "print(mfcc.shape)\n",
        "print(rolloff.shape)\n",
        "print(pitch.shape)\n",
        "print(mel.shape)\n",
        "print(chroma1.shape)\n",
        "'''\n",
        "mfcc=np.save('/content/drive/My Drive/Extra/mfccc.npy',mfcc)\n",
        "labels=np.save('/content/drive/My Drive/Extra/labels.npy',labels)\n",
        "mel=np.save('/content/drive/My Drive/Extra/mel.npy',mel)\n",
        "chroma=np.save('/content/drive/My Drive/Extra/chroma.npy',chroma1)\n",
        "pitch=np.save('/content/drive/My Drive/Extra/pitch.npy',pitch)\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32,)\n",
            "(32, 220500)\n",
            "(32, 25, 431)\n",
            "(32, 1, 431)\n",
            "(32, 25, 431)\n",
            "(32, 1, 431)\n",
            "(32, 1025, 431)\n",
            "(32, 128, 431)\n",
            "(32, 12, 431)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nmfcc=np.save('/content/drive/My Drive/Extra/mfccc.npy',mfcc)\\nlabels=np.save('/content/drive/My Drive/Extra/labels.npy',labels)\\nmel=np.save('/content/drive/My Drive/Extra/mel.npy',mel)\\nchroma=np.save('/content/drive/My Drive/Extra/chroma.npy',chroma1)\\npitch=np.save('/content/drive/My Drive/Extra/pitch.npy',pitch)\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OidrNalUzWIu",
        "colab_type": "code",
        "outputId": "44b0f12c-7a3f-45b6-8b73-e72808dbe73f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "feature1 = np.concatenate((mel,pitch,mfcc,centroid,chroma1,rolloff),axis=1)\n",
        "#feature1 = np.concatenate((mel,pitch,mfcc,chroma1),axis=1)\n",
        "feature2= feature1.reshape(feature1.shape[0],feature1.shape[1]*feature1.shape[2])\n",
        "feature = np.concatenate((feature2,zcr),axis =1 )\n",
        "feature.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 734252)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0QifVdCGZvH",
        "colab_type": "text"
      },
      "source": [
        "#Feature Importance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jk32WQ2DTEJ0",
        "colab_type": "text"
      },
      "source": [
        "Correlation between MFFC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDRZnquFHCay",
        "colab_type": "code",
        "outputId": "42311403-3ccd-44ba-ac5b-17436a0acbde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "import librosa\n",
        "path1='/content/drive/My Drive/COVID-19 Converted-333/COVID-19 Converted/Postive-con/pos-7.wav'\n",
        "path2 ='/content/drive/My Drive/COVID-19 Converted-333/COVID-19 Converted/Negative-con/neg-1.wav'\n",
        "dr =10\n",
        "x,sr = librosa.load(path1,duration=dr)\n",
        "values1 =librosa.feature.mfcc(x,sr,n_mfcc=25)\n",
        "x1,sr1 = librosa.load(path1,duration=dr)\n",
        "values2 =librosa.feature.mfcc(x1,sr1,n_mfcc=25)\n",
        "mat = [values1,values2]\n",
        "mat=np.array(mat)\n",
        "mat = np.reshape(mat,(2,-1))\n",
        "mat= mat.reshape(mat.shape[1],2)\n",
        "#print(mat)\n",
        "mat1 = pd.DataFrame(data=mat,columns=['x','y'])\n",
        "\n",
        "\n",
        "print(sc.score(mat1,\"x\",\"y\"))\n",
        "\n",
        "import seaborn as sns\n",
        "df_matrix = sc.matrix(mat1)\n",
        "sns.heatmap(df_matrix, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'x': 'x', 'y': 'y', 'task': 'regression', 'ppscore': 0.7283461638058915, 'metric': 'mean absolute error', 'baseline_score': 26.91166976043008, 'model_score': 7.310658328809819, 'model': DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
            "                      max_features=None, max_leaf_nodes=None,\n",
            "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                      min_samples_leaf=1, min_samples_split=2,\n",
            "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                      random_state=None, splitter='best')}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f3c0be518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKexBzLwTIJp",
        "colab_type": "text"
      },
      "source": [
        "Correlation between Constant Q Chromogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJYYbqj-K7Cm",
        "colab_type": "code",
        "outputId": "e8be5182-c841-42a2-d989-c41fdc7e1b54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "x,sr = librosa.load(path1,duration=dr)\n",
        "values1 =librosa.feature.chroma_cqt(x,sr)\n",
        "x1,sr1 = librosa.load(path1,duration=dr)\n",
        "values2 =librosa.feature.chroma_cqt(x1,sr1)\n",
        "mat = [values1[1],values2[1]]\n",
        "mat=np.array(mat)\n",
        "mat = np.reshape(mat,(2,-1))\n",
        "mat= mat.reshape(mat.shape[1],2)\n",
        "#print(mat)\n",
        "mat1 = pd.DataFrame(data=mat,columns=['x','y'])\n",
        "'''\n",
        "corr = mat1.corr()\n",
        "\n",
        "# plot the heatmap\n",
        "sns.heatmap(corr, \n",
        "        xticklabels=corr.columns,\n",
        "        yticklabels=corr.columns)\n",
        "'''\n",
        "\n",
        "print(pps.score(mat1,\"x\",\"y\"))\n",
        "\n",
        "import seaborn as sns\n",
        "df_matrix = pps.matrix(mat1)\n",
        "sns.heatmap(df_matrix, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'x': 'x', 'y': 'y', 'task': 'regression', 'ppscore': 0, 'metric': 'mean absolute error', 'baseline_score': 0.21722013359057776, 'model_score': 0.22190913712459542, 'model': DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
            "                      max_features=None, max_leaf_nodes=None,\n",
            "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                      min_samples_leaf=1, min_samples_split=2,\n",
            "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                      random_state=None, splitter='best')}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f34c475f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmKRAkYAS4VN",
        "colab_type": "text"
      },
      "source": [
        "Correlation between Melspectrograms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8aiYh6svK8CR",
        "colab_type": "code",
        "outputId": "746a0000-dbec-46d4-c609-3432db2616d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "x,sr = librosa.load(path1,duration=dr)\n",
        "values1 =librosa.feature.melspectrogram(x,sr)\n",
        "x1,sr1 = librosa.load(path1,duration=dr)\n",
        "values2 =librosa.feature.melspectrogram(x1,sr1)\n",
        "mat = [values1[1],values2[1]]\n",
        "mat=np.array(mat)\n",
        "mat = np.reshape(mat,(2,-1))\n",
        "mat= mat.reshape(mat.shape[1],2)\n",
        "mat1 = pd.DataFrame(data=mat,columns=['x','y'])\n",
        "\n",
        "\n",
        "print(pps.score(mat1,\"x\",\"y\"))\n",
        "\n",
        "import seaborn as sns\n",
        "df_matrix = pps.matrix(mat1)\n",
        "sns.heatmap(df_matrix, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'x': 'x', 'y': 'y', 'task': 'regression', 'ppscore': 0, 'metric': 'mean absolute error', 'baseline_score': 0.005875087965101716, 'model_score': 0.008317048916141044, 'model': DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
            "                      max_features=None, max_leaf_nodes=None,\n",
            "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                      min_samples_leaf=1, min_samples_split=2,\n",
            "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                      random_state=None, splitter='best')}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f3bef4550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdmmEM6HS0WT",
        "colab_type": "text"
      },
      "source": [
        "Correlation between Pitches"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMN7yN74MV0w",
        "colab_type": "code",
        "outputId": "5cd2126c-126e-45d4-dec0-2d9b862cdd8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "x,sr = librosa.load(path1,duration=dr)\n",
        "values1 =librosa.piptrack(x,sr)\n",
        "x1,sr1 = librosa.load(path1,duration=dr)\n",
        "values2 =librosa.piptrack(x1,sr1)\n",
        "mat = [values1[1],values2[1]]\n",
        "mat=np.array(mat)\n",
        "mat = np.reshape(mat,(2,-1))\n",
        "mat= mat.reshape(mat.shape[1],2)\n",
        "mat1 = pd.DataFrame(data=mat,columns=['x','y'])\n",
        "\n",
        "\n",
        "print(sc.score(mat1,\"x\",\"y\"))\n",
        "\n",
        "import seaborn as sns\n",
        "df_matrix = sc.matrix(mat1)\n",
        "sns.heatmap(df_matrix, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'x': 'x', 'y': 'y', 'task': 'regression', 'ppscore': 0, 'metric': 'mean absolute error', 'baseline_score': 0.12622401196788996, 'model_score': 0.205598646294191, 'model': DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
            "                      max_features=None, max_leaf_nodes=None,\n",
            "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                      min_samples_leaf=1, min_samples_split=2,\n",
            "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                      random_state=None, splitter='best')}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f34f42a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SWhAUlbOSGBg",
        "colab_type": "text"
      },
      "source": [
        "Correlation between ZCR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rxg02zm2OVzr",
        "colab_type": "code",
        "outputId": "8870c942-6e31-4652-a02a-47b13a6d9514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "x,sr = librosa.load(path1,duration=dr)\n",
        "values1 =librosa.core.zero_crossings(x)\n",
        "x1,sr1 = librosa.load(path1,duration=dr)\n",
        "values2 =librosa.core.zero_crossings(x1)\n",
        "mat = [values1[1],values2[1]]\n",
        "mat=np.array(mat)\n",
        "mat = np.reshape(mat,(2,-1))\n",
        "mat= mat.reshape(mat.shape[1],2)\n",
        "mat1 = pd.DataFrame(data=mat,columns=['x','y'])\n",
        "\n",
        "\n",
        "print(sc.score(mat1,\"x\",\"y\"))\n",
        "\n",
        "import seaborn as sns\n",
        "df_matrix = sc.matrix(mat1)\n",
        "sns.heatmap(df_matrix, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'x': 'x', 'y': 'y', 'task': 'predict_constant', 'ppscore': 1, 'metric': None, 'baseline_score': 1, 'model_score': 1, 'model': None}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f3c042828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XVdWmC+v1zOc63Xb2s7bY+jHgN+Y8F81ZVf1nkq8luRA4G7jLwD01GLrtvAB4DvAs1ire/5nvdDQA7wOuAl4IHJzvVNSK7YVGkqwAtwLnAi+qqn1znpLmbLS19F9Z+xfxrqr61pynpAasdBtI8gbgiar6wOjByJ9K8lNV9bF5z03zU1WPJ/k48A0D99RhpSvNyegLtM8Cr6uq++Y9H7XhkjFpDkZ/9mUV+KiBe2qx0pWkhqx0JakhQ1eSGjJ0JakhQ1eSGjJ0Jamh/wPdJFRXKbYZtwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxW0o9nBbCBK",
        "colab_type": "text"
      },
      "source": [
        "Correlations between Spectral Roll_off"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psmfL6cBSOEj",
        "colab_type": "code",
        "outputId": "a23f42a4-b8f2-47fe-a342-1aef63ec9991",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "x,sr = librosa.load(path1,duration=dr)\n",
        "values1 =librosa.feature.spectral_rolloff(y=x,sr=sr)\n",
        "x1,sr1 = librosa.load(path1,duration=dr)\n",
        "values2 =librosa.feature.spectral_rolloff(y=x1,sr=sr)\n",
        "mat = [values1,values2]\n",
        "mat=np.array(mat)\n",
        "mat = np.reshape(mat,(2,-1))\n",
        "mat= mat.reshape(mat.shape[1],2)\n",
        "mat1 = pd.DataFrame(data=mat,columns=['x','y'])\n",
        "\n",
        "\n",
        "print(sc.score(mat1,\"x\",\"y\"))\n",
        "\n",
        "import seaborn as sns\n",
        "df_matrix = sc.matrix(mat1)\n",
        "sns.heatmap(df_matrix, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'x': 'x', 'y': 'y', 'task': 'regression', 'ppscore': 0.4540056713689332, 'metric': 'mean absolute error', 'baseline_score': 1051.4048565291473, 'model_score': 574.0610887600749, 'model': DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
            "                      max_features=None, max_leaf_nodes=None,\n",
            "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                      min_samples_leaf=1, min_samples_split=2,\n",
            "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                      random_state=None, splitter='best')}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f34bda550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XAcnOTvZcDrj"
      },
      "source": [
        "Correlations between Spectral Centroid"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbR-fbOta_oM",
        "colab_type": "code",
        "outputId": "243337bd-7400-4dc5-a5b4-714517e7ec80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "x,sr = librosa.load(path1,duration=dr)\n",
        "values1 =librosa.feature.spectral_centroid(x,sr)\n",
        "x1,sr1 = librosa.load(path1,duration=dr)\n",
        "values2 =librosa.feature.spectral_centroid(x1,sr)\n",
        "mat = [values1,values2]\n",
        "mat=np.array(mat)\n",
        "mat = np.reshape(mat,(2,-1))\n",
        "mat= mat.reshape(mat.shape[1],2)\n",
        "mat1 = pd.DataFrame(data=mat,columns=['x','y'])\n",
        "\n",
        "\n",
        "print(sc.score(mat1,\"x\",\"y\"))\n",
        "\n",
        "import seaborn as sns\n",
        "df_matrix = sc.matrix(mat1)\n",
        "sns.heatmap(df_matrix, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'x': 'x', 'y': 'y', 'task': 'regression', 'ppscore': 0.4379946629550828, 'metric': 'mean absolute error', 'baseline_score': 441.02828408269283, 'model_score': 247.86024944223524, 'model': DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
            "                      max_features=None, max_leaf_nodes=None,\n",
            "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                      min_samples_leaf=1, min_samples_split=2,\n",
            "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                      random_state=None, splitter='best')}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f34d20b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-j2Rqzv1Yny",
        "colab_type": "text"
      },
      "source": [
        "**Data Splitting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5F2hNFzyYwt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "(x_train, xtest, ytrain, ytest) = train_test_split(feature, labels,\n",
        "\ttest_size=0.20, stratify=labels, random_state=40)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWLLdAjLRuWk",
        "colab_type": "code",
        "outputId": "c4df2048-7c93-49bd-d0b2-03d7015fb6af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from keras.utils import to_categorical\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "lb = LabelBinarizer()\n",
        "labels = lb.fit_transform(data.label)\n",
        "lb.get_params(deep=True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'neg_label': 0, 'pos_label': 1, 'sparse_output': False}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iUAUvyiv1jhB",
        "colab_type": "text"
      },
      "source": [
        "**Model checking**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCN9wNklGy1I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "(x_train, xtest, ytrain, ytest) = train_test_split(feature, labels,\n",
        "\ttest_size=0.20, stratify=labels, random_state=40)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Js3hmabcKjfj",
        "colab_type": "code",
        "outputId": "52958b14-dea2-451a-bb7a-f32ff5e165b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rc = RandomForestClassifier(n_estimators = 300,max_depth=50,random_state=50)\n",
        "rc .fit(x_train,ytrain)\n",
        "accuracy = rc.score(xtest,ytest)\n",
        "print(accuracy*100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "85.71428571428571\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-0LnCC1SARm",
        "colab_type": "code",
        "outputId": "8fe9b25e-e9f3-48dd-adf6-0a69204d3b13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "pred = rc.predict(xtest)\n",
        "print(sklearn.metrics.confusion_matrix(ytest, pred))\n",
        "print(classification_report(ytest, pred)) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3 1]\n",
            " [0 3]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.75      0.86         4\n",
            "           1       0.75      1.00      0.86         3\n",
            "\n",
            "    accuracy                           0.86         7\n",
            "   macro avg       0.88      0.88      0.86         7\n",
            "weighted avg       0.89      0.86      0.86         7\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WBWaZAM9pDa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_estimators = [100, 300, 500, 800, 1200]\n",
        "max_depth = [5, 8, 15, 25, 30]\n",
        "min_samples_split = [2, 5, 10, 15, 100]\n",
        "min_samples_leaf = [1, 2, 5, 10] \n",
        "\n",
        "hyperF = dict(n_estimators = n_estimators, max_depth = max_depth,  \n",
        "              min_samples_split = min_samples_split, \n",
        "             min_samples_leaf = min_samples_leaf)\n",
        "\n",
        "gridF = GridSearchCV(forest, hyperF,  verbose = 3, \n",
        "                      )\n",
        "bestF = gridF.fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbWF9JEJlU_X",
        "colab_type": "text"
      },
      "source": [
        "#Second Trial On Real Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uc2QWbrHVIrO"
      },
      "source": [
        "> This is Second trial of training on real data  \n",
        "\n",
        "> I have 63 samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3R4zwVVQ1z10",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ff7VhYof10fC",
        "colab_type": "text"
      },
      "source": [
        "**Data Extracting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Etz0gMiNlXPU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "df_new = []\n",
        "for folder in ['/content/drive/My Drive/Clean/Postive-con/**',\n",
        "             \n",
        "               ]:\n",
        "  for fname in glob.iglob(folder):\n",
        "    if os.path.exists(fname):\n",
        "      label = os.path.basename(fname).split(\".\")[1]\n",
        "      if label  in [\"wav\"]:\n",
        "      \n",
        "        df_new.append(\n",
        "              {\n",
        "                  \"filename \" : fname,\n",
        "               \"result\" : \"positve\" ,\n",
        "               \n",
        "              })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8sPPm3Vleur",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "for folder in [\"/content/drive/My Drive/Colab Notebooks/Audio Data/COVID-19 Converted/COVID-19 Converted/Negative-con/**\",\n",
        "             \n",
        "               ]:\n",
        "  for fname in glob.iglob(folder):\n",
        "    if os.path.exists(fname):\n",
        "      label = os.path.basename(fname).split(\".\")[1]\n",
        "      \n",
        "      if label  in [\"wav\"]:\n",
        "      \n",
        "        df_new.append(\n",
        "              {\n",
        "                  \"filename \" : fname,\n",
        "               \"result\" : \"negative\" ,\n",
        "               \n",
        "              })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjORdsx5liC0",
        "colab_type": "code",
        "outputId": "496cdc3e-a581-45fb-d6e9-43afb2dfdcf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import pandas as pd\n",
        "data_new = pd.DataFrame(df_new)\n",
        "data_new.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/drive/My Drive/Clean/Postive-con/pos-...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/drive/My Drive/Clean/Postive-con/pos-...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/drive/My Drive/Clean/Postive-con/pos-...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/drive/My Drive/Clean/Postive-con/pos-...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/drive/My Drive/Clean/Postive-con/pos-...</td>\n",
              "      <td>positve</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           filename    result\n",
              "0  /content/drive/My Drive/Clean/Postive-con/pos-...  positve\n",
              "1  /content/drive/My Drive/Clean/Postive-con/pos-...  positve\n",
              "2  /content/drive/My Drive/Clean/Postive-con/pos-...  positve\n",
              "3  /content/drive/My Drive/Clean/Postive-con/pos-...  positve\n",
              "4  /content/drive/My Drive/Clean/Postive-con/pos-...  positve"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmBbiveBllTs",
        "colab_type": "code",
        "outputId": "ef5d7f3f-86a1-48c4-8261-1735fb3e59a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize = (12,6))\n",
        "data_new['result'].value_counts().plot(kind = 'bar' , title = ' Distribution' )\n",
        "plt.show()\n",
        "print(data_new['result'].value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "positve     32\n",
            "negative    32\n",
            "Name: result, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0Kuedlf183T",
        "colab_type": "text"
      },
      "source": [
        "**Features Extraction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "t9fC_prYTl3r"
      },
      "source": [
        "I  have Extract different Features down below\n",
        "* rms\n",
        "* zcr \n",
        "* spectral_energy \n",
        "* centroid\n",
        "* mfcc\n",
        "* rolloff\n",
        "*pitch\n",
        "*melspectrogram\n",
        "*Constant-Q chromagram\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7phQBueRlqkk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "rms_energy = []\n",
        "zcr = []\n",
        "spectral_energy = []\n",
        "centroid=[]\n",
        "mfcc=[]\n",
        "rolloff=[]\n",
        "pitch=[]\n",
        "mel=[]\n",
        "chroma1 = []\n",
        "\n",
        "dr =10 \n",
        "\n",
        "def features1(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.spectral_centroid(x,sr)\n",
        "  return values\n",
        "\n",
        "def features2(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values = librosa.core.zero_crossings(x)\n",
        "  return values\n",
        "\n",
        "def features3(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.spectral_rolloff(y=x,sr=sr)\n",
        "  return values\n",
        "\n",
        "def features4(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values, magnitudes = librosa.piptrack(y=x, sr=sr)\n",
        "  return values\n",
        "\n",
        "def features5(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.mfcc(x,sr,n_mfcc=25)\n",
        "  return values\n",
        "\n",
        "\n",
        "def features6(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.rms(y=x)\n",
        "\n",
        "def features7(path):\n",
        "  x,sr = librosa.load(path,duration=dr)\n",
        "  values =librosa.feature.melspectrogram(y=x,sr=sr)\n",
        "  return values\n",
        "\n",
        "\n",
        "def features8(path):\n",
        "  x,sr = librosa.load(path)\n",
        "  values =librosa.get_duration(y=x,sr=sr)\n",
        "  return values\n",
        "def melfeatures(path):\n",
        "  f,samp_rate = librosa.load(path=path,duration = dr)\n",
        "  mel = librosa.feature.melspectrogram(y=f ,sr = samp_rate)\n",
        "  return mel\n",
        "def chroma(path):\n",
        "  f,samp_rate = librosa.load(path=path,duration = 10)\n",
        "  mfccs = librosa.feature.chroma_cqt(y=f,sr=samp_rate)\n",
        "  return mfccs \n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLc1yEdplx8R",
        "colab_type": "code",
        "outputId": "b6f4c0a2-f2fa-4a6f-fd6d-ec645d95a3be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import librosa\n",
        "trai = data_new.rename(columns={'filename ': 'filename'})\n",
        "for i in tqdm(range(len(trai))):\n",
        "  rms_energy.append(features6(trai.filename.iloc[i]))\n",
        "  zcr.append(features2(trai.filename.iloc[i])) \n",
        "  mfcc.append(features5(trai.filename.iloc[i])) \n",
        "  centroid.append(features1(trai.filename.iloc[i])) \n",
        "  spectral_energy.append(features5(trai.filename.iloc[i]))\n",
        "  rolloff.append(features3(trai.filename.iloc[i])) \n",
        "  pitch.append(features4(trai.filename.iloc[i]))\n",
        "  mel.append(melfeatures(trai.filename.iloc[i]))\n",
        "  chroma1.append(chroma(trai.filename.iloc[i]))\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 64/64 [06:30<00:00,  6.10s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2rR5HwWl3GK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "rms_energy = np.array(rms_energy)\n",
        "zcr = np.array(zcr)\n",
        "spectral_energy= np.array(spectral_energy)\n",
        "centroid = np.array(centroid)\n",
        "mfcc= np.array(mfcc)\n",
        "rolloff=np.array(rolloff)\n",
        "pitch= np.array(pitch)\n",
        "mel=np.array(mel)\n",
        "chroma1 = np.array(chroma1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "460Da2cnl8m6",
        "colab_type": "code",
        "outputId": "1398b18e-4ae1-4f6e-cc8d-363e8211636b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(rms_energy.shape)\n",
        "print(zcr.shape)\n",
        "print(spectral_energy.shape)\n",
        "print(centroid.shape)\n",
        "print(mfcc.shape)\n",
        "print(rolloff.shape)\n",
        "print(pitch.shape)\n",
        "print(mel.shape)\n",
        "print(chroma1.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64,)\n",
            "(64, 220500)\n",
            "(64, 25, 431)\n",
            "(64, 1, 431)\n",
            "(64, 25, 431)\n",
            "(64, 1, 431)\n",
            "(64, 1025, 431)\n",
            "(64, 128, 431)\n",
            "(64, 12, 431)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dA4SdbWgmCNW",
        "colab_type": "code",
        "outputId": "0112975e-f8cc-420a-ef2b-dcae12d708fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "feature1_new = np.concatenate((mel,pitch,mfcc,centroid,chroma1,rolloff),axis=1)\n",
        "#feature1 = np.concatenate((mel,pitch,mfcc,chroma1),axis=1)\n",
        "feature2_new= feature1_new.reshape(feature1_new.shape[0],feature1_new.shape[1]*feature1_new.shape[2])\n",
        "feature_new = np.concatenate((feature2_new,zcr),axis =1 )\n",
        "feature_new.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 734252)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1jEO7Gj2EBI",
        "colab_type": "text"
      },
      "source": [
        "**Data Splitting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Np7pM8mooR5Q",
        "colab_type": "code",
        "outputId": "05a9e093-a1b3-419d-973f-8152c15f1e79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from keras.utils import to_categorical\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "lb = LabelBinarizer()\n",
        "results = lb.fit_transform(data_new.result)\n",
        "lb.get_params(deep=True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'neg_label': 0, 'pos_label': 1, 'sparse_output': False}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5c9gomcXoXNf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "(x_train_new, xtest_new, ytrain_new, ytest_new) = train_test_split(feature_new,results,\n",
        "\ttest_size=0.30, stratify=results, random_state=40)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K3w4Ugx52Jef",
        "colab_type": "text"
      },
      "source": [
        "**First Model Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CsN5QD-3o0lJ",
        "colab_type": "code",
        "outputId": "fbc5657b-d66f-478b-e348-0e095e39e25d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "sc_new= SVC(kernel='sigmoid')\n",
        "sc_new.fit(x_train_new,ytrain_new)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='sigmoid',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8mnlvdB6piD_",
        "colab_type": "code",
        "outputId": "7c92cd69-4a8a-4315-e8dd-ce62f338d36f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sc_new.score(xtest_new, ytest_new)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7894736842105263"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 370
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6ETVnESpqTv",
        "colab_type": "code",
        "outputId": "013176f0-362a-464f-d5df-fe1ff7352c27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "pred_new = sc_new.predict(xtest_new)\n",
        "print(sklearn.metrics.confusion_matrix(ytest_new, pred_new))\n",
        "print(classification_report(ytest_new, pred_new))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[7 3]\n",
            " [1 8]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.70      0.78        10\n",
            "           1       0.73      0.89      0.80         9\n",
            "\n",
            "    accuracy                           0.79        19\n",
            "   macro avg       0.80      0.79      0.79        19\n",
            "weighted avg       0.81      0.79      0.79        19\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuZJzyQK2a6P",
        "colab_type": "text"
      },
      "source": [
        "**Second Model Testing(Coding phase)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCidhKtQDJaS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "(x_train_new, xtest_new, ytrain_new, ytest_new) = train_test_split(feature_new,results,\n",
        "\ttest_size=0.30, stratify=results, random_state=20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzdhPXQb2YCL",
        "colab_type": "code",
        "outputId": "a276ff72-2185-4ae0-ce85-1c61c5c03a40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sc_new.score(xtest_new, ytest_new)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.85"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pdBuu1Lq3Ee",
        "colab_type": "code",
        "outputId": "c3174478-2f75-4d27-849b-becab01d9bdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import sklearn\n",
        "pred_new = sc_new.predict(xtest_new)\n",
        "print(sklearn.metrics.confusion_matrix(ytest_new, pred_new))\n",
        "print(classification_report(ytest_new, pred_new))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[10  0]\n",
            " [ 3  7]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      1.00      0.87        10\n",
            "           1       1.00      0.70      0.82        10\n",
            "\n",
            "    accuracy                           0.85        20\n",
            "   macro avg       0.88      0.85      0.85        20\n",
            "weighted avg       0.88      0.85      0.85        20\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7R2hUEK2uSA",
        "colab_type": "text"
      },
      "source": [
        "**Parameters Searching**\n",
        "> Still Doing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x40hjk6ft8lC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV \n",
        "\n",
        "param_grid = {'C': [0.1, 1, 10, 100, 1000],  \n",
        "               \n",
        "              'kernel':  ['sigmoid']}  \n",
        " \n",
        "grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3,cv = 3) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cqY3YBgusVi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid.fit(x_train_new, ytrain_new)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThnlnpecuyBE",
        "colab_type": "code",
        "outputId": "f66be579-1192-47b4-bca0-dcfbadde835b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 0.1, 'gamma': 1, 'kernel': 'sigmoid'}\n",
            "SVC(C=0.1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma=1, kernel='sigmoid',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWkcvPPTv4cQ",
        "colab_type": "code",
        "outputId": "6ee1ffef-33cd-459c-cb06-5b8739ffd24e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import sklearn\n",
        "pred_new = sc_new.predict(xtest_new)\n",
        "print(sklearn.metrics.confusion_matrix(ytest_new, pred_new))\n",
        "print(classification_report(ytest_new, pred_new))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[10  0]\n",
            " [ 3  7]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      1.00      0.87        10\n",
            "           1       1.00      0.70      0.82        10\n",
            "\n",
            "    accuracy                           0.85        20\n",
            "   macro avg       0.88      0.85      0.85        20\n",
            "weighted avg       0.88      0.85      0.85        20\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXaIuaziHF0E",
        "colab_type": "code",
        "outputId": "2817ccfe-196e-4aaf-e516-beb0d8dc20ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sklearn.metrics.matthews_corrcoef(ytest_new, pred_new)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7337993857053428"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebrGF8UmH0W0",
        "colab_type": "code",
        "outputId": "b6229ccb-50b1-4347-c125-b376fcbe5f4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sklearn.metrics.roc_auc_score(ytest_new, pred_new)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.85"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8Zp-SHBzqyf",
        "colab_type": "text"
      },
      "source": [
        "> TO DO:\n",
        "\n",
        "* Apply Cross Validation like Kfold\n",
        "\n",
        "* Working on more features and data\n",
        "\n",
        "* Filters part"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HK5vVp7WxtAG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}