  Marine mammal communication is a complex field, hindered by the diversity of
vocalizations and environmental factors. The Watkins Marine Mammal Sound
Database (WMMD) constitutes a comprehensive labeled dataset employed in machine
learning applications. Nevertheless, the methodologies for data preparation,
preprocessing, and classification documented in the literature exhibit
considerable variability and are typically not applied to the dataset in its
entirety. This study initially undertakes a concise review of the
state-of-the-art benchmarks pertaining to the dataset, with a particular focus
on clarifying data preparation and preprocessing techniques. Subsequently, we
explore the utilization of the Wavelet Scattering Transform (WST) and Mel
spectrogram as preprocessing mechanisms for feature extraction. In this paper,
we introduce \textbf{WhaleNet} (Wavelet Highly Adaptive Learning Ensemble
Network), a sophisticated deep ensemble architecture for the classification of
marine mammal vocalizations, leveraging both WST and Mel spectrogram for
enhanced feature discrimination. By integrating the insights derived from WST
and Mel representations, we achieved an improvement in classification accuracy
by $8-10\%$ over existing architectures, corresponding to a classification
accuracy of $97.61\%$.
