neural network that segments and labels birdsong
repository for the paper:
"TweetyNet: A neural network that enables high-throughput, automated annotation of birdsong"
A neural network architecture, shown below:
tweetynet automates annotation of birdsong and other vocalizations.
An example of annotated song is shown below:
How is it used?
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
that is installed automatically with
Please see the
vak documentation for detailed installation instructions:
For a tutorial on using
vak, please see the
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
.cbin audio files.
It can also accept spectrograms in the form of Matlab
.mat files or
.npz files created by
vak uses a separate library to parse annotations,
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:
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.
If you run into problems, please use the issue tracker or contact the authors via email in the paper above.
Released under BSD license.
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