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Model-based STFT phase recovery for audio source separation

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Details

Original languageEnglish
Pages (from-to)1095-1105
Number of pages11
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume26
Issue number6
DOIs
Publication statusPublished - 1 Jun 2018
Publication typeA1 Journal article-refereed

Abstract

For audio source separation applications, it is common to estimate the magnitude of the short-time Fourier transform (STFT) of each source. In order to further synthesize time-domain signals, it is necessary to recover the phase of the corresponding complex-valued STFT. Most authors in this field choose a Wiener-like filtering approach, which boils down to use the phase of the original mixture. In this paper, a different standpoint is adopted. Many music events are partially composed of slowly varying sinusoids and the STFT phase increment over time of those frequency components takes a specific form. This allows phase recovery by an unwrapping technique once a short-term frequency estimate has been obtained. Herein, a novel iterative source separation procedure is proposed that builds upon these results. It consists in minimizing the mixing error by means of the auxiliary function method. This procedure is initialized by exploiting the unwrapping technique in order to generate estimates that benefit from a temporal continuity property. Experiments conducted on realistic music pieces show that, given accurate magnitude estimates, this procedure outperforms the state-of-the-art consistent Wiener filter.

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Field of science, Statistics Finland