Wavelet Scattering Transform for Bioacustics: Application to Watkins
  Marine Mammal Sound Database
Davide Carbone Alessandro Licciardi
Marine mammal communication is a complex field, hindered by the diversity of
vocalizations and environmental factors. The Watkins Marine Mammal Sound
Database (WMMD) is an extensive labeled dataset used in machine learning
applications. However, the methods for data preparation, preprocessing, and
classification found in the literature are quite disparate. This study first
focuses on a brief review of the state-of-the-art benchmarks on the dataset,
with an emphasis on clarifying data preparation and preprocessing methods.
Subsequently, we propose the application of the Wavelet Scattering Transform
(WST) in place of standard methods based on the Short-Time Fourier Transform
(STFT). The study also tackles a classification task using an ad-hoc deep
architecture with residual layers. We outperform the existing classification
architecture by $6\%$ in accuracy using WST and $8\%$ using Mel spectrogram
preprocessing, effectively reducing by half the number of misclassified
samples, and reaching a top accuracy of $96\%$.