@inproceedings{
min2023joint,
title={Joint time{\textendash}frequency scattering-enhanced representation for bird vocalization classification},
author={Yimeng Min and Eliot T Miller and Daniel Fink and Carla P Gomes},
booktitle={NeurIPS 2023 Computational Sustainability: Promises and Pitfalls from Theory to Deployment},
year={2023},
url={https://openreview.net/forum?id=3mlfh6c3dp}
}
Neural Networks (NNs) have been widely used in passive acoustic monitoring. Typically, audio is converted into a Mel Spectrogram as a preprocessing step before being fed into NNs. In this study, we investigate the Joint Time-Frequency Scattering transform as an alternative preprocessing technique for analyzing bird vocalizations.
We highlight its superiority over the Mel Spectrogram because it captures intricate time-frequency patterns and emphasizes rapid signal transitions. While the Mel Spectrogram often gives similar importance to all sounds, the scattering transform differentiates between rapid and slow variations better. 
We use a Convolution Neural Network architecture and an attention-based transformer. Our results demonstrate that both the NN architectures can benefit from this enhanced preprocessing, where scattering transform can provide a more discriminative representation of bird vocalizations than the traditional Mel Spectrogram.
Joint time–frequency scattering-enhanced representation for bird vocalization classification
Yimeng Min Eliot T Miller Daniel Fink Carla P Gomes