Single-channel speech enhancement using learnable loss mixup
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909056745078784 |
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| author | Chang, Oscar Tran, Dung N. Koishida, Kazuhito |
| author_facet | Chang, Oscar Tran, Dung N. Koishida, Kazuhito |
| contents | Generalization remains a major problem in supervised learning of single-channel speech enhancement. In this work, we propose learnable loss mixup (LLM), a simple and effortless training diagram, to improve the generalization of deep learning-based speech enhancement models. Loss mixup, of which learnable loss mixup is a special variant, optimizes a mixture of the loss functions of random sample pairs to train a model on virtual training data constructed from these pairs of samples. In learnable loss mixup, by conditioning on the mixed data, the loss functions are mixed using a non-linear mixing function automatically learned via neural parameterization. Our experimental results on the VCTK benchmark show that learnable loss mixup achieves 3.26 PESQ, outperforming the state-of-the-art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17255 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Single-channel speech enhancement using learnable loss mixup Chang, Oscar Tran, Dung N. Koishida, Kazuhito Audio and Speech Processing Machine Learning Sound Generalization remains a major problem in supervised learning of single-channel speech enhancement. In this work, we propose learnable loss mixup (LLM), a simple and effortless training diagram, to improve the generalization of deep learning-based speech enhancement models. Loss mixup, of which learnable loss mixup is a special variant, optimizes a mixture of the loss functions of random sample pairs to train a model on virtual training data constructed from these pairs of samples. In learnable loss mixup, by conditioning on the mixed data, the loss functions are mixed using a non-linear mixing function automatically learned via neural parameterization. Our experimental results on the VCTK benchmark show that learnable loss mixup achieves 3.26 PESQ, outperforming the state-of-the-art. |
| title | Single-channel speech enhancement using learnable loss mixup |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2312.17255 |