# WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database

arXiv: https://arxiv.org/abs/2402.17775 · HTML: https://mlsanity.com/paper/2402.17775/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Kymatio: Scattering Transforms in Python](https://mlsanity.com/paper/1812.11214/) (2018)
- [AVES: Animal Vocalization Encoder based on Self-Supervision](https://mlsanity.com/paper/2210.14493/) (2022)
- [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013)
- [Global birdsong embeddings enable superior transfer learning for bioacoustic classification](https://mlsanity.com/paper/2307.06292/) (2023)
- [BEANS: The Benchmark of Animal Sounds](https://mlsanity.com/paper/2210.12300/) (2022)
- [Multiscale Sparse Microcanonical Models](https://mlsanity.com/paper/1801.02013/) (2018)
- [Wavelet Conditional Renormalization Group](https://mlsanity.com/paper/2207.04941/) (2022)
- [Quantification of MagLIF morphology using the Mallat Scattering Transformation](https://mlsanity.com/paper/2005.01600/) (2020)

## Built on by (most cited first, 3 of 3)

- [WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection](https://mlsanity.com/paper/2602.02980/) (2026)
- [The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning](https://mlsanity.com/paper/2505.12904/) (2025)
- [Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning](https://mlsanity.com/paper/2506.09674/) (2025)

## Abstract (arXiv)

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 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%.