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Introducing whoBIRD, the ultimate birding companion that can recognize birds by their sounds, anywhere in the world! Powered by the cutting-edge BirdNET project, whoBIRD boasts an extensive database of over 6,000 bird species worldwide. Using advanced machine learning algorithms, this Android app can accurately identify birds based on their unique vocalizations.
What's more, whoBIRD performs its magic in real time entirely on your device, without requiring an internet connection. This means you can use it anytime, anywhere – whether you're deep in the forest or at the edge of a remote lake.
At first start the app will download the required BirdNET model files. Once the app is installed simply open it and it will begin listening and analyzing.
If a bird is detected, its name will be briefly displayed. For a detailed list of all detections, navigate to the View tab. There you can also backup, share, or delete the database containing your observations.
Ignore Date and Place: Disable the meta model that checks if a bird can be present at your location at the current time. Useful when analyzing recordings from other locations.
Show Images: When enabled, an image of the detected bird will be downloaded if the detection probability is high.
Audio Source: Select the audio input that works best for your device. Typically, "Unprocessed" is the recommended choice. If using a USB microphone, select "Microphone".
High Pass Filter: Filter out low frequencies to reduce background noise, such as traffic sounds. For example, a 200Hz filter can help minimize low-frequency noise.
Threshold: Set the minimum probability required for a detection to be displayed. Be cautious when lowering the threshold, as it may lead to an increase in false detections.
This work is licensed under GPLv3, © woheller69
- This app is built on the BirdNET framework by @kahst, published under CC BY NC SA 4.0 license
- At first start it downloads the BirdNet TFLite library from whoBird-TFlite, which is published under CC BY NC SA 4.0 license
- Label files from BirdNET are used under GPL 3.0 with permission from the author
- It uses code from Tensorflow examples, published under Apache 2.0 license
- It uses Zip4j (https://github.com/srikanth-lingala/zip4j) which is licensed under Apache License Version 2.0
- It uses iirj (https://github.com/berndporr/iirj) which is licensed under Apache License Version 2.0
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