Multichannel audio declipping
| Autor: | Alexey Ozerov, Cagdas Bilen, Patrick Pérez |
|---|---|
| Přispěvatelé: | Technicolor R & I [Cesson Sévigné], Technicolor, ANR-14-CE27-0002,MAD,Inpainting de données audio manquantes(2014), Ozerov, Alexey, Appel à projets générique - Inpainting de données audio manquantes - - MAD2014 - ANR-14-CE27-0002 - Appel à projets générique - VALID |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Channel (digital image)
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing Speech recognition audio declipping Model parameters generalized expectation-maximization 02 engineering and technology Data_CODINGANDINFORMATIONTHEORY Set (abstract data type) 030507 speech-language pathology & audiology 03 medical and health sciences [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing multichannel audio Computer Science::Multimedia 0202 electrical engineering electronic engineering information engineering Nonnegative tensor factorization Mathematics full-rank spatial model business.industry nonnegative tensor factorization 020206 networking & telecommunications Pattern recognition Covariance Power (physics) Sound recording and reproduction Computer Science::Sound Spectrogram Artificial intelligence 0305 other medical science business |
| Zdroj: | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'16) IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'16), Mar 2016, Shanghai, China ICASSP |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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