Multichannel audio declipping

Abstract : Audio declipping consists in recovering so-called clipped audio samples that are set to a maximum / minimum threshold. Many different approaches were proposed to solve this problem in case of single-channel (mono) recordings. However, while most of audio recordings are multichannel nowadays, there is no method designed specifically for multichannel audio declipping, where the inter-channel correlations may be efficiently exploited for a better declipping result. In this work we propose for the first time such a multichannel audio declipping method. Our method is based on representing a multichannel audio recording as a convolutive mixture of several audio sources, and on modeling the source power spectrograms and mixing filters by nonnegative tensor factorization model and full-rank covariance matrices, respectively. A generalized expectation-maximization algorithm is proposed to estimate model parameters. It is shown experimentally that the proposed multichannel audio de-clipping algorithm outperforms in average and in most cases a state-of-the-art single-channel declipping algorithm applied to each channel independently.
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Conference papers
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https://hal.inria.fr/hal-01254950
Contributor : Alexey Ozerov <>
Submitted on : Tuesday, January 12, 2016 - 10:43:16 PM
Last modification on : Wednesday, January 31, 2018 - 3:08:01 PM
Long-term archiving on : Monday, April 18, 2016 - 10:00:11 PM

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  • HAL Id : hal-01254950, version 1

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Alexey Ozerov, Cagdas Bilen, Patrick Pérez. Multichannel audio declipping. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'16), Mar 2016, Shanghai, China. ⟨hal-01254950v1⟩

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