Marmoset Call Classification

Classification of marmoset calls using tweetynet. Check out more at poster.

Development of the MarmosetCallClassification work.

The pipeline converts audio to spectrograms, generates predictions with the trained neural network and displays spectrograms with the classifications (optional).

Performance

Performance of the TweetyNet model trained on marmoset vocalizations*:

Accuracy	Loss	Segment Error Rate
0.9584	0.1472	0.2821

• Recorded and labeled by Daniel Y. Takahashi and colleauges. See Takahashi, D. Y., Fenley, A. R., Teramoto, Y., Narayanan, D. Z., Borjon, J. I., Holmes, P., & Ghazanfar, A. A. (2015). The developmental dynamics of marmoset monkey vocal production. Science, 349(6249), 734-738. for details

Training metrics for validation data

Accuracy of the model as a function of the time of trained audios.

Average error of the model as a function of the time of trained audios.

Normalized confusion matrix of the model on validation data calls.

Installation

Install vak version == 0.8.2.

Use

python3 predict.py <data_dir> <out_dir> --plot_spec

• You don't need to modify predict.toml!

data_dir: Directory containing the data to predict on (wav audio files).

out_dir: Directory to save the outputs (preprocessed spectrograms and predictions). Two folders will be created: results to store the predictions (predictions.csv and spect_plots for the spectrograms with the classifications if --plot_spec) and data to store the preprocessed data that will serve as input for tweetynet.

--plot_spec: Add to plot the spectrograms.