SPGM: Prioritizing Local Features for enhanced speech separation performance
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929269201960960 |
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| author | Yip, Jia Qi Zhao, Shengkui Ma, Yukun Ni, Chongjia Zhang, Chong Wang, Hao Nguyen, Trung Hieu Zhou, Kun Ng, Dianwen Chng, Eng Siong Ma, Bin |
| author_facet | Yip, Jia Qi Zhao, Shengkui Ma, Yukun Ni, Chongjia Zhang, Chong Wang, Hao Nguyen, Trung Hieu Zhou, Kun Ng, Dianwen Chng, Eng Siong Ma, Bin |
| contents | Dual-path is a popular architecture for speech separation models (e.g. Sepformer) which splits long sequences into overlapping chunks for its intra- and inter-blocks that separately model intra-chunk local features and inter-chunk global relationships. However, it has been found that inter-blocks, which comprise half a dual-path model's parameters, contribute minimally to performance. Thus, we propose the Single-Path Global Modulation (SPGM) block to replace inter-blocks. SPGM is named after its structure consisting of a parameter-free global pooling module followed by a modulation module comprising only 2% of the model's total parameters. The SPGM block allows all transformer layers in the model to be dedicated to local feature modelling, making the overall model single-path. SPGM achieves 22.1 dB SI-SDRi on WSJ0-2Mix and 20.4 dB SI-SDRi on Libri2Mix, exceeding the performance of Sepformer by 0.5 dB and 0.3 dB respectively and matches the performance of recent SOTA models with up to 8 times fewer parameters. Model and weights are available at huggingface.co/yipjiaqi/spgm |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_12608 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SPGM: Prioritizing Local Features for enhanced speech separation performance Yip, Jia Qi Zhao, Shengkui Ma, Yukun Ni, Chongjia Zhang, Chong Wang, Hao Nguyen, Trung Hieu Zhou, Kun Ng, Dianwen Chng, Eng Siong Ma, Bin Audio and Speech Processing Sound Dual-path is a popular architecture for speech separation models (e.g. Sepformer) which splits long sequences into overlapping chunks for its intra- and inter-blocks that separately model intra-chunk local features and inter-chunk global relationships. However, it has been found that inter-blocks, which comprise half a dual-path model's parameters, contribute minimally to performance. Thus, we propose the Single-Path Global Modulation (SPGM) block to replace inter-blocks. SPGM is named after its structure consisting of a parameter-free global pooling module followed by a modulation module comprising only 2% of the model's total parameters. The SPGM block allows all transformer layers in the model to be dedicated to local feature modelling, making the overall model single-path. SPGM achieves 22.1 dB SI-SDRi on WSJ0-2Mix and 20.4 dB SI-SDRi on Libri2Mix, exceeding the performance of Sepformer by 0.5 dB and 0.3 dB respectively and matches the performance of recent SOTA models with up to 8 times fewer parameters. Model and weights are available at huggingface.co/yipjiaqi/spgm |
| title | SPGM: Prioritizing Local Features for enhanced speech separation performance |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2309.12608 |