Skip to content

Latest commit

 

History

History
193 lines (145 loc) · 6.93 KB

README.md

File metadata and controls

193 lines (145 loc) · 6.93 KB
Raw

Flippy

Score-follower.

Requirements

  • Cloned repository with all submodules
git clone <REPO_URL> --recurse-submodules
  • Python 3 (Tested on Python 3.8, Ubuntu 20.04)

Setup

(Run scripts/install.sh to get these automatically for Ubuntu 20.04)

  • FluidSynth
  • Requirements: pip install -r requirements.txt
  • Install nsgt separately: pip install nsgt
  • Initialise pre-commit: pre-commit install
  • [Optional] Install fftw
  • [Optional] Audio playback: ffmpeg

WSL2 Note

  • For audio playback, PulseAudio is required. See here for a guide.

Usage

python flippy.py --help

Using the Quantitative Testbench

  • The Quantitative Testbench is already included as a submodule in this repository in flippy_quantitative_testbench
  • The Results Reproduction section below use the testbench, see repro.py for references to the testbench reprository.
  • To output compatible score-follower output (also compatible with the MIREX format), set the backend type to alignment (default), an example is:
python flippy.py \
    --perf_wave_path <PERFORMANCE_WAVE_PATH> \ # path to the wave file of the performance
    --score_midi_path <SCORE_MIDI_PATH> \      # path to the midi score file
    --mode offline \                           # offline (alignment mode)
    --backend alignment                        # output alignment in the backend

Using the Qualitative Testbench

Running your own pieces

  • The Qualitative Testbench can be found here
  • You need to set up a UDP Port number in the testbench--see instructions in that repository
  • With the host name and UDP Port number of the testbench machine, run flippy on online mode and timestamp backend, an example that also plays the performance audio on the score-follower machine is:
python flippy.py \
    --perf_wave_path <PERFORMANCE_WAVE_PATH> \ # path to the wave file of the performance
    --score_midi_path <SCORE_MIDI_PATH> \      # path to the midi score file
    --mode online \                            # online (following mode)
    --backend timestamp \                      # output timestamps in the backend
    --backend_output udp:<HOSTNAME>:<PORT> \   # output to stderr and the UDP server at <HOSTNAME>:<PORT>
    --play_performance_audio \                 # play the performance audio on the machine where this command is run
    --simulate_performance                     # stream the performance wave audio slices "live" into the system

Demos

See Demos subsection below.

Demos

Demo videos are provided in the demos directory. To understand the structure and reproduce these, see the Demos Reproduction subsection below.

Results Reproduction

Report Results

These scripts reproduce results shown in the project report.

To run everything:

python repro.py

cqt_time

python repro.py cqt_time

Plots the time taken to extract CQT featuers on different lengths of audio using the librosa, nsgt and librosa_pseudo and librosa_hybrid techniques.

dtw_time

python repro.py dtw_time

Plots the time taken to align sequences of different lengths using the oltw and classical DTW methods.

bwv846_feature

python repro.py bwv846_feature

Plots the extracted features from the first 15 seconds of the Prelude and Fugue of Bach's BWV846 to repro_results/bwv846_feature.

bach10_feature

python repro.py bach10_feature

Plots the extracted features from the first 15 seconds of all Bach10 pieces to repro_results/bach10_feature.

bwv846_align

python repro.py bwv846_align

Aligns (offline) BWV846 and then runs the testbench to output results in repro_results/bwv846_align.

bach10_align

python repro.py bach10_align

Aligns (offline) Bach10 and then runs the testbench to output results in repro_results/bwv846_align.

bach10_follow

python repro.py bach10_follow

Follows (online) Bach10 and then runs the testbench to output results in repro_results/bach10_follow.

bwv846_follow

python repro.py bwv846_follow

Follows (online) BWV846 and then runs the testbench to output results in repro_results/bwv846_follow.

bach10_plot_precision

python repro.py bach10_plot_precision

Plots total precision results for Bach10--requires bach10_align and bach10_follow repro steps to be run a priori.

bwv846_plot_precision

python repro.py bwv846_plot_precision

Plots total precision results for Bach10--requires bwv846_align and bwv846_follow repro steps to be run a priori.

Demos Reproduction

Possible combinations of <GROUP_ID> and <PIECE_ID> are defined in the QualScofo dataset.

demos structure

This directory contains videos of the following in action (using the qualitative testbench to visualise the following).

demos
|---videos
    |---<GROUP_ID>
        |---<PIECE_ID>.mkv

Reproduction scripts

./scripts/qual/qual.sh <GROUP_ID> <PIECE_ID> <QUALITATIVE_TESTBENCH_IP> <QUALITATIVE_TESTBENCH_PORT>

You may try to use the preprocessed pickle files, which should work on Python 3.8.x systems:

./scripts/qual/qual_pickle.sh <GROUP_ID> <PIECE_ID> <QUALITATIVE_TESTBENCH_IP> <QUALITATIVE_TESTBENCH_PORT>

Contributing

Citing

BibTeX

@misc{https://doi.org/10.48550/arxiv.2205.03247,
  doi = {10.48550/ARXIV.2205.03247},
  url = {https://arxiv.org/abs/2205.03247},
  author = {Lee, Lin Hao},
  keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
  title = {Musical Score Following and Audio Alignment},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}