Parameter-efficient Dual-encoder Architecture with Differentiable Choquet Integral Fusion for Underwater Acoustic Classification
arXiv:2606.02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment. Waveform and spectrogram representations have been primarily used as acoustic data features for classification tasks in this domain. Spectrograms model harmonic dependencies, but these reduced representations can filter out acoustic features relevant for discrimination. While phase information from the waveform allows full characterization of the signal, the original waveform can be noisy and com
The increasing complexity of underwater acoustic environments and the limitations of traditional spectrogram features necessitate advanced AI techniques for robust classification.
Improved underwater acoustic classification can significantly enhance capabilities in maritime defence, resource exploration, and environmental monitoring, areas of growing strategic importance.
This research introduces a novel AI architecture that promises more accurate and efficient processing of complex underwater acoustic data, potentially enabling new applications and operational insights.
- · Naval defence organizations
- · Marine research institutions
- · Underwater resource exploration companies
- · AI/ML model developers
- · Developers of legacy acoustic classification systems
- · Actors reliant on obfuscated underwater environments
More reliable detection and identification of underwater objects and phenomena.
Enhanced submarine warfare capabilities and improved maritime domain awareness.
Potential for autonomous underwater vehicles to operate with greater sophistication and independence in complex environments.
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Read at arXiv cs.LG