Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing

Hanfang Cui; Longfei Song; Li Li; Dongxing Xu; Yanhua Long

doi:10.1007/s44443-025-00181-5

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arXiv · 2505.11889

Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing

Hanfang Cui·Longfei Song·Li Li·Dongxing Xu·Yanhua Long

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Abstract

Self-supervised learning (SSL) models offer powerful representations for sound event detection (SED), yet their synergistic potential remains underexplored. This study systematically evaluates state-of-the-art SSL models to guide optimal model selection and integration for SED. We propose a framework that combines heterogeneous SSL representations (e.g., BEATs, HuBERT, WavLM) through three fusion strategies: individual SSL embedding integration, dual-modal fusion, and full aggregation. Experiments on the DCASE 2023 Task 4 Challenge reveal that dual-modal fusion (e.g., CRNN+BEATs+WavLM) achieves complementary performance gains, while CRNN+BEATs alone delivers the best results among individual SSL models. We further introduce normalized sound event bounding boxes (nSEBBs), an adaptive post-processing method that dynamically adjusts event boundary predictions, improving PSDS1 by up to 4% for standalone SSL models. These findings highlight the compatibility and complementarity of SSL architectures, providing guidance for task-specific fusion and robust SED system design.

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BibTeX RIS

Hanfang Cui, Longfei Song, Li Li, Dongxing Xu, Yanhua Long. 2025-05-17. Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing. https://doi.org/10.1007/s44443-025-00181-5

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