An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating

Abstract

The detection and optimization of ocean environmental noise anomalies play a crucial role in enhancing the safety of marine engineering applications and ecological protection. Current anomaly detection methods for ocean environmental noise often suffer from issues of accuracy and robustness. To address these challenges, this paper first proposes an end-to-end framework that combines time–frequency information and expert gating, significantly improving the precision of noise sequence generation. Secondly, a Gamma distribution-based residual analysis method for anomaly detection is designed, enhancing the robustness of anomaly detection. Finally, an anomaly optimization module is developed to improve data quality, enabling efficient noise anomaly detection and optimization. Our experimental results demonstrate that the proposed model significantly outperforms traditional models in multi-frequency noise prediction, with strong robustness in anomaly detection and high generalization performance. The proposed framework offers a novel approach for analyzing the causes of noise anomalies and optimizing models. Additionally, the research outcomes provide efficient technical support for deep-sea exploration, equipment optimization, and environmental protection.

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