Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915464005812224 |
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| author | Cui, Hanfang Song, Longfei Li, Li Xu, Dongxing Long, Yanhua |
| author_facet | Cui, Hanfang Song, Longfei Li, Li Xu, Dongxing Long, Yanhua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11889 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing Cui, Hanfang Song, Longfei Li, Li Xu, Dongxing Long, Yanhua Audio and Speech Processing Artificial Intelligence Sound I.5.4; I.2.10; H.5.5 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. |
| title | Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing |
| topic | Audio and Speech Processing Artificial Intelligence Sound I.5.4; I.2.10; H.5.5 |
| url | https://arxiv.org/abs/2505.11889 |