Toward Universal Speech Enhancement for Diverse Input Conditions
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913235491356672 |
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| author | Zhang, Wangyou Saijo, Kohei Wang, Zhong-Qiu Watanabe, Shinji Qian, Yanmin |
| author_facet | Zhang, Wangyou Saijo, Kohei Wang, Zhong-Qiu Watanabe, Shinji Qian, Yanmin |
| contents | The past decade has witnessed substantial growth of data-driven speech enhancement (SE) techniques thanks to deep learning. While existing approaches have shown impressive performance in some common datasets, most of them are designed only for a single condition (e.g., single-channel, multi-channel, or a fixed sampling frequency) or only consider a single task (e.g., denoising or dereverberation). Currently, there is no universal SE approach that can effectively handle diverse input conditions with a single model. In this paper, we make the first attempt to investigate this line of research. First, we devise a single SE model that is independent of microphone channels, signal lengths, and sampling frequencies. Second, we design a universal SE benchmark by combining existing public corpora with multiple conditions. Our experiments on a wide range of datasets show that the proposed single model can successfully handle diverse conditions with strong performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_17384 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Toward Universal Speech Enhancement for Diverse Input Conditions Zhang, Wangyou Saijo, Kohei Wang, Zhong-Qiu Watanabe, Shinji Qian, Yanmin Audio and Speech Processing Sound Signal Processing The past decade has witnessed substantial growth of data-driven speech enhancement (SE) techniques thanks to deep learning. While existing approaches have shown impressive performance in some common datasets, most of them are designed only for a single condition (e.g., single-channel, multi-channel, or a fixed sampling frequency) or only consider a single task (e.g., denoising or dereverberation). Currently, there is no universal SE approach that can effectively handle diverse input conditions with a single model. In this paper, we make the first attempt to investigate this line of research. First, we devise a single SE model that is independent of microphone channels, signal lengths, and sampling frequencies. Second, we design a universal SE benchmark by combining existing public corpora with multiple conditions. Our experiments on a wide range of datasets show that the proposed single model can successfully handle diverse conditions with strong performance. |
| title | Toward Universal Speech Enhancement for Diverse Input Conditions |
| topic | Audio and Speech Processing Sound Signal Processing |
| url | https://arxiv.org/abs/2309.17384 |