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Workshop: Computational Sustainability: Promises and Pitfalls from Theory to Deployment

Joint time–frequency scattering-enhanced representation for bird vocalization classification

Yimeng Min · Carla Gomes


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.

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