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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Communication dans un congrès
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'16), Mar 2016, Shanghai, China. 2016
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  • HAL Id : hal-01254950, version 2

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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. 2016. 〈hal-01254950v2〉

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