BatDetect2

Code for detecting and classifying bat echolocation calls in high frequency audio recordings.

Getting started

1. Install the Anaconda Python 3.9 distribution for your operating system from here.
2. Download this code from the repository (by clicking on the green button on top right) and unzip it.
3. Create a new environment and install the required packages:
   conda create -y --name batdetect python==3.9
conda activate batdetect
conda install --file requirements.txt

Try the model on Colab

TODO

Running the model on your own data

After following the above steps you can run the model on your own data by opening the command line where the code is located and typing:
python run_batdetect.py AUDIO_DIR ANN_DIR DETECTION_THRESHOLD

AUDIO_DIR is the path on your computer to the files of interest.
ANN_DIR is the path on your computer where the detailed predictions will be saved. The model will output both .csv and .json results for each audio file.
DETECTION_THRESHOLD is a number between 0 and 1 specifying the cut-off threshold applied to the calls. A smaller number will result in more calls detected, but with the chance of introducing more mistakes:
python run_batdetect.py example_data/audio/ example_data/anns/ 0.3

There are also optional arguments e.g. you can request that the model outputs features (i.e. call parameters) such as duration, max_frequency, etc. by setting the flag --spec_features. These will be saved as *_spec_features.csv files:
python run_batdetect.py example_data/audio/ example_data/anns/ 0.3 --spec_features

You can also specify which model to use by setting the --model_path argument. If not specified, it will default to using a model trained on UK data.

Requirements

The code has been tested using Python3.9 with the following package versions described in requirements.txt.

FAQ

For more information please consult our FAQ.

Reference

If you find our work useful in your research please consider citing our paper:

@article{batdetect2_2022,
    author    = {TODO},
    title     = {TODO},
    journal   = {TODOD},
    year      = {2022}
}

Acknowledgements

TODO