Joint Time–frequency Scattering-Enhanced Representation for Bird Vocalization Classification

Abstract

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.