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neural network that segments and labels birdsong

Project description

DOI PyPI version

TweetyNet

tweetynet image

repository for the paper:
"TweetyNet: A neural network that enables high-throughput, automated annotation of birdsong"
https://www.biorxiv.org/content/10.1101/2020.08.28.272088v2

What is tweetynet?

A neural network architecture, shown below:

neural network architecture

tweetynet automates annotation of birdsong and other vocalizations.
An example of annotated song is shown below:

schematic of annotation

How is it used?

Installation

To install, run the following command at the command line:
pip install tweetynet

To facilitate training tweetynet models and using trained models to predict annotation on new datasets, we developed the vak library, that is installed automatically with tweetynet.

Please see the vak documentation for detailed installation instructions:
https://vak.readthedocs.io/en/latest/get_started/installation.html

Usage

For a tutorial on using tweetynet with vak, please see the vak documentation:
https://vak.readthedocs.io/en/latest/tutorial/autoannotate.html

FAQs

Training data

To train models, you must supply training data in the form of audio files or spectrogram files, and annotations. The package can generate spectrograms from .wav or .cbin audio files. It can also accept spectrograms in the form of Matlab .mat files or .npz files created by numpy. vak uses a separate library to parse annotations, crowsetta, which handles some common formats and can also be used to write custom parsers for other formats. Please see the crowsetta documentation for more detail:
https://crowsetta.readthedocs.io/en/latest/#

Preparing training files

It is possible to train on any manually annotated data but there are some useful guidelines:

  • Use as many examples as possible - The results will just be better. Specifically, this code will not label correctly syllables it did not encounter while training and will most probably generalize to the nearest sample or ignore the syllable.
  • Use noise examples - This will make the code very good in ignoring noise.
  • Examples of syllables on noise are important - It is a good practice to start with clean recordings. The code will not perform miracles and is most likely to fail if the audio is too corrupt or masked by noise. Still, training with examples of syllables on the background of cage noises will be beneficial.

For more details, please see the vak documentation.

Issues

If you run into problems, please use the issue tracker or contact the authors via email in the paper above.

Citation

If you use or adapt this code, please cite its DOI:
DOI

License

Released under BSD license.

Project details


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