Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition

TL;DR

This paper presents a multi-representation attention framework for underwater bioacoustic denoising and recognition, improving marine mammal call detection accuracy and robustness across diverse environmental conditions and signal-to-noise ratios.

Contribution

The study introduces a novel multi-step, attention-guided framework with segmentation-driven attention and mid-level fusion, enhancing signal discrimination and generalization in marine mammal monitoring.

Findings

Segmentation-driven attention improves detection accuracy.

Mid-level fusion enhances robustness to environmental noise.

The framework maintains performance under distributional shifts.

Abstract

Automated monitoring of marine mammals in the St. Lawrence Estuary faces extreme challenges: calls span low-frequency moans to ultrasonic clicks, often overlap, and are embedded in variable anthropogenic and environmental noise. We introduce a multi-step, attention-guided framework that first segments spectrograms to generate soft masks of biologically relevant energy and then fuses these masks with the raw inputs for multi-band, denoised classification. Image and mask embeddings are integrated via mid-level fusion, enabling the model to focus on salient spectrogram regions while preserving global context. Using real-world recordings from the Saguenay St. Lawrence Marine Park Research Station in Canada, we demonstrate that segmentation-driven attention and mid-level fusion improve signal discrimination, reduce false positive detections, and produce reliable representations for operational marine mammal monitoring across diverse environmental conditions and signal-to-noise ratios. Beyond in-distribution evaluation, we further assess the generalization of Mask-Guided Classification (MGC) under distributional shifts by testing on spectrograms generated with alternative acoustic transformations. While high-capacity baseline models lose accuracy in this Out-of-distribution (OOD) setting, MGC maintains stable performance, with even simple fusion mechanisms (gated, concat) achieving comparable results across distributions. This robustness highlights the capacity of MGC to learn transferable representations rather than overfitting to a specific transformation, thereby reinforcing its suitability for large-scale, real-world biodiversity monitoring. We show that in all experimental settings, the MGC framework consistently outperforms baseline architectures, yielding substantial gains in accuracy on both in-distribution and OOD data.