Mixture to Mixture: Leveraging Close-talk Mixtures as Weak-supervision for Speech Separation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916289375633408 |
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| author | Wang, Zhong-Qiu |
| author_facet | Wang, Zhong-Qiu |
| contents | We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discriminative models to separate far-field mixtures. Our idea is that, for a target speaker, its close-talk mixture has a much higher signal-to-noise ratio (SNR) of the target speaker than any far-field mixtures, and hence could be utilized to design a weak supervision for separation. To realize this, at each training step we feed a far-field mixture to a deep neural network (DNN) to produce an intermediate estimate for each speaker, and, for each of considered close-talk and far-field microphones, we linearly filter the DNN estimates and optimize a loss so that the filtered estimates of all the speakers can sum up to the mixture captured by each of the considered microphones. Evaluation results on a 2-speaker separation task in simulated reverberant conditions show that M2M can effectively leverage close-talk mixtures as a weak supervision for separating far-field mixtures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_09313 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mixture to Mixture: Leveraging Close-talk Mixtures as Weak-supervision for Speech Separation Wang, Zhong-Qiu Audio and Speech Processing Signal Processing We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discriminative models to separate far-field mixtures. Our idea is that, for a target speaker, its close-talk mixture has a much higher signal-to-noise ratio (SNR) of the target speaker than any far-field mixtures, and hence could be utilized to design a weak supervision for separation. To realize this, at each training step we feed a far-field mixture to a deep neural network (DNN) to produce an intermediate estimate for each speaker, and, for each of considered close-talk and far-field microphones, we linearly filter the DNN estimates and optimize a loss so that the filtered estimates of all the speakers can sum up to the mixture captured by each of the considered microphones. Evaluation results on a 2-speaker separation task in simulated reverberant conditions show that M2M can effectively leverage close-talk mixtures as a weak supervision for separating far-field mixtures. |
| title | Mixture to Mixture: Leveraging Close-talk Mixtures as Weak-supervision for Speech Separation |
| topic | Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2402.09313 |