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arXiv · 2010.10255

Phase recovery with Bregman divergences for audio source separation

Paul Magron·Pierre-Hugo Vial·Thomas Oberlin·Cédric Févotte
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Abstract

Time-frequency audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a phase recovery algorithm to retrieve time-domain signals. In particular, the multiple input spectrogram inversion (MISI) algorithm has shown good performance in several recent works. This algorithm minimizes a quadratic reconstruction error between magnitude spectrograms. However, this loss does not properly account for some perceptual properties of audio, and alternative discrepancy measures such as beta-divergences have been preferred in many settings. In this paper, we propose to reformulate phase recovery in audio source separation as a minimization problem involving Bregman divergences. To optimize the resulting objective, we derive a projected gradient descent algorithm. Experiments conducted on a speech enhancement task show that this approach outperforms MISI for several alternative losses, which highlights their relevance for audio source separation applications.

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Paul Magron, Pierre-Hugo Vial, Thomas Oberlin, Cédric Févotte. 2020-10-20. Phase recovery with Bregman divergences for audio source separation. https://arxiv.org/abs/2010.10255

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