# Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation

arXiv: https://arxiv.org/abs/1907.13188 · HTML: https://mlsanity.com/paper/1907.13188/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

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

- [The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning](https://mlsanity.com/paper/2505.12904/) (2025)
- [Cross-domain Sound Recognition for Efficient Underwater Data Analysis](https://mlsanity.com/paper/2309.03451/) (2023)
- [Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained Models](https://mlsanity.com/paper/2409.13878/) (2024)

## Abstract (arXiv)

Research into automated systems for detecting and classifying marine mammals in acoustic recordings is expanding internationally due to the necessity to analyze large collections of data for conservation purposes. In this work, we present a Convolutional Neural Network that is capable of classifying the vocalizations of three species of whales, non-biological sources of noise, and a fifth class pertaining to ambient noise. In this way, the classifier is capable of detecting the presence and absence of whale vocalizations in an acoustic recording. Through transfer learning, we show that the classifier is capable of learning high-level representations and can generalize to additional species. We also propose a novel representation of acoustic signals that builds upon the commonly used spectrogram representation by way of interpolating and stacking multiple spectrograms produced using different Short-time Fourier Transform (STFT) parameters. The proposed representation is particularly effective for the task of marine mammal species classification where the acoustic events we are attempting to classify are sensitive to the parameters of the STFT.