Contrastive Loss Based Frame-wise Feature disentanglement for Polyphonic Sound Event Detection
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909069592231936 |
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| author | Guan, Yadong Han, Jiqing Song, Hongwei Song, Wenjie Zheng, Guibin Zheng, Tieran He, Yongjun |
| author_facet | Guan, Yadong Han, Jiqing Song, Hongwei Song, Wenjie Zheng, Guibin Zheng, Tieran He, Yongjun |
| contents | Overlapping sound events are ubiquitous in real-world environments, but existing end-to-end sound event detection (SED) methods still struggle to detect them effectively. A critical reason is that these methods represent overlapping events using shared and entangled frame-wise features, which degrades the feature discrimination. To solve the problem, we propose a disentangled feature learning framework to learn a category-specific representation. Specifically, we employ different projectors to learn the frame-wise features for each category. To ensure that these feature does not contain information of other categories, we maximize the common information between frame-wise features within the same category and propose a frame-wise contrastive loss. In addition, considering that the labeled data used by the proposed method is limited, we propose a semi-supervised frame-wise contrastive loss that can leverage large amounts of unlabeled data to achieve feature disentanglement. The experimental results demonstrate the effectiveness of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05850 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contrastive Loss Based Frame-wise Feature disentanglement for Polyphonic Sound Event Detection Guan, Yadong Han, Jiqing Song, Hongwei Song, Wenjie Zheng, Guibin Zheng, Tieran He, Yongjun Sound Audio and Speech Processing Overlapping sound events are ubiquitous in real-world environments, but existing end-to-end sound event detection (SED) methods still struggle to detect them effectively. A critical reason is that these methods represent overlapping events using shared and entangled frame-wise features, which degrades the feature discrimination. To solve the problem, we propose a disentangled feature learning framework to learn a category-specific representation. Specifically, we employ different projectors to learn the frame-wise features for each category. To ensure that these feature does not contain information of other categories, we maximize the common information between frame-wise features within the same category and propose a frame-wise contrastive loss. In addition, considering that the labeled data used by the proposed method is limited, we propose a semi-supervised frame-wise contrastive loss that can leverage large amounts of unlabeled data to achieve feature disentanglement. The experimental results demonstrate the effectiveness of our method. |
| title | Contrastive Loss Based Frame-wise Feature disentanglement for Polyphonic Sound Event Detection |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2401.05850 |