# The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning

arXiv: https://arxiv.org/abs/2505.12904 · HTML: https://mlsanity.com/paper/2505.12904/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 15 of 15)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Large Batch Training of Convolutional Networks](https://mlsanity.com/paper/1708.03888/) (2017)
- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://mlsanity.com/paper/2106.00750/) (2021)
- [Contrastive Learning of General-Purpose Audio Representations](https://mlsanity.com/paper/2010.10915/) (2020)
- [BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations](https://mlsanity.com/paper/2204.07402/) (2022)
- [Unsupervised Contrastive Learning of Sound Event Representations](https://mlsanity.com/paper/2011.07616/) (2020)
- [Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks](https://mlsanity.com/paper/2110.07313/) (2021)
- [Guiding the underwater acoustic target recognition with interpretable contrastive learning](https://mlsanity.com/paper/2402.12658/) (2024)
- [Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation](https://mlsanity.com/paper/1907.13188/) (2019)
- [WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database](https://mlsanity.com/paper/2402.17775/) (2024)

## Built on by (most cited first, 2 of 2)

- [Linear probing enables Ship-Radiated Noise recognition with pretrained audio embeddings](https://mlsanity.com/paper/2601.08358/) (2026)
- [Automated data curation for self-supervised learning in underwater acoustic analysis](https://mlsanity.com/paper/2505.20066/) (2025)

## Abstract (arXiv)

The increasing level of sound pollution in marine environments poses an increased threat to ocean health, making it crucial to monitor underwater noise. By monitoring this noise, the sources responsible for this pollution can be mapped. Monitoring is performed by passively listening to these sounds. This generates a large amount of data records, capturing a mix of sound sources such as ship activities and marine mammal vocalizations. Although machine learning offers a promising solution for automatic sound classification, current state-of-the-art methods implement supervised learning. This requires a large amount of high-quality labeled data that is not publicly available. In contrast, a massive amount of lower-quality unlabeled data is publicly available, offering the opportunity to explore unsupervised learning techniques. This research explores this possibility by implementing an unsupervised Contrastive Learning approach. Here, a Conformer-based encoder is optimized by the so-called Variance-Invariance-Covariance Regularization loss function on these lower-quality unlabeled data and the translation to the labeled data is made. Through classification tasks involving recognizing ship types and marine mammal vocalizations, our method demonstrates to produce robust and generalized embeddings. This shows to potential of unsupervised methods for various automatic underwater acoustic analysis tasks.