TemporalContext

Implementation of algorithm described in

Shyam Madhusudhana et al. (2021). "Improve automatic detection of animal call sequences with temporal context." Journal of the Royal Society Interface, 20210297. DOI: 10.1098/rsif.2021.0297

for improving a CNN's ability to detect animal call sequences by attaching a recurrent network to it.

The user interface is made available as a set of jupyter notebooks to accomplish the different aspects of the workflow. A subset of the data used in the research paper is available at . Download and extract the contents of the archive on your local storage.

Setting up

• After cloning the repository, install the required python packages listed in requirements.txt (pip3 install -r requirements.txt). Based on your preference for using tensorflow or tensorflow-gpu package, make sure you uncomment the appropriate line in requirements.txt before running 'pip install ...'.
• Update raw_data_root in temporalcontext/settings.py to point to the local directory where you extracted the dataset.
• Change project_root in temporalcontext/settings.py to point to a local directory where generated intermediate files, models and results will be placed.

Training and Testing

This stage requires you to run the jupyter notebooks in the following order.

• Run input_prep-cnn.ipynb to process the audio data and save spectrograms of fixed-length segments for training.
• Run train_eval-cnn.ipynb to train the base CNN models.
• Run input_prep-hybrid.ipynb. It uses the trained base CNN models to generate training and validation data for all three hybrid variants.
• The code in train_eval-hybrid.ipynb will train the hybrid models. You need to change the variable lstm_type in the notebook and run it once per hybrid model type.
• Run test.ipynb to make predictions on the test data (for each fold) with all of the trained models.

Generating results

The testing code in the above step only saves the scores output by the trained models. To produce the Precision-Recall curves and F1-score vs. Threshold curves (like the ones presented in the article) run:

$> python3 generate_results.py

All the result figures will be produced in the perf_results_dir subdirectory set under project_root.

PS: If you work with data from the aforementioned dataset, please know not to expect similar results as presented in the article since this dataset is only a subset of what was used in the study.