Mixture to Mixture: Leveraging Close-talk Mixtures as Weak-supervision for Speech Separation
IEEE Signal Processing Letters(2024)
摘要
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.
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关键词
Weakly-supervised neural speech separation
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