# Guiding the underwater acoustic target recognition with interpretable contrastive learning

arXiv: https://arxiv.org/abs/2402.12658 · HTML: https://mlsanity.com/paper/2402.12658/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2024.

## Built on by (most cited first, 4 of 4)

- [The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning](https://mlsanity.com/paper/2505.12904/) (2025)
- [Advancing Robust Underwater Acoustic Target Recognition through Multi-task Learning and Multi-Gate Mixture-of-Experts](https://mlsanity.com/paper/2411.02787/) (2024)
- [Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors](https://mlsanity.com/paper/2411.02848/) (2024)
- [DEMONet: Underwater Acoustic Target Recognition based on Multi-Expert Network and Cross-Temporal Variational Autoencoder](https://mlsanity.com/paper/2411.02758/) (2024)

## Abstract (arXiv)

Recognizing underwater targets from acoustic signals is a challenging task owing to the intricate ocean environments and variable underwater channels. While deep learning-based systems have become the mainstream approach for underwater acoustic target recognition, they have faced criticism for their lack of interpretability and weak generalization performance in practical applications. In this work, we apply the class activation mapping (CAM) to generate visual explanations for the predictions of a spectrogram-based recognition system. CAM can help to understand the behavior of recognition models by highlighting the regions of the input features that contribute the most to the prediction. Our explorations reveal that recognition models tend to focus on the low-frequency line spectrum and high-frequency periodic modulation information of underwater signals. Based on the observation, we propose an interpretable contrastive learning (ICL) strategy that employs two encoders to learn from acoustic features with different emphases (line spectrum and modulation information). By imposing constraints between encoders, the proposed strategy can enhance the generalization performance of the recognition system. Our experiments demonstrate that the proposed contrastive learning approach can improve the recognition accuracy and bring significant improvements across various underwater databases.