Separating time-frequency sources from time-domain convolutive mixtures using non-negative matrix factorization
This paper addresses the problem of under-determined audio source separation in multichannel reverberant mixtures. We target a semiblind scenario assuming that the mixing filters are known. Source separation is performed from the time-domain mixture signals in order to accurately model the convoluti...
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| Published in: | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics : proceedings pp. 264 - 268 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
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01.10.2017
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| ISSN: | 1947-1629 |
| Online Access: | Get full text |
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| Abstract | This paper addresses the problem of under-determined audio source separation in multichannel reverberant mixtures. We target a semiblind scenario assuming that the mixing filters are known. Source separation is performed from the time-domain mixture signals in order to accurately model the convolutive mixing process. The source signals are however modeled as latent variables in a time-frequency domain. In a previous paper we proposed to use the modified discrete cosine transform. The present paper generalizes the method to the use of the odd-frequency short-time Fourier transform. In this domain, the source coefficients are modeled as centered complex Gaussian random variables whose variances are structured by means of a non-negative matrix factorization model. The inference procedure relies on a variational expectation-maximization algorithm. In the experiments we discuss the choice of the source representation and we show that the proposed approach outperforms two methods from the literature. |
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| AbstractList | This paper addresses the problem of under-determined audio source separation in multichannel reverberant mixtures. We target a semiblind scenario assuming that the mixing filters are known. Source separation is performed from the time-domain mixture signals in order to accurately model the convolutive mixing process. The source signals are however modeled as latent variables in a time-frequency domain. In a previous paper we proposed to use the modified discrete cosine transform. The present paper generalizes the method to the use of the odd-frequency short-time Fourier transform. In this domain, the source coefficients are modeled as centered complex Gaussian random variables whose variances are structured by means of a non-negative matrix factorization model. The inference procedure relies on a variational expectation-maximization algorithm. In the experiments we discuss the choice of the source representation and we show that the proposed approach outperforms two methods from the literature. |
| Author | Badeau, Roland Richard, Gael Leglaive, Simon |
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| Snippet | This paper addresses the problem of under-determined audio source separation in multichannel reverberant mixtures. We target a semiblind scenario assuming that... |
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| StartPage | 264 |
| SubjectTerms | Audio source separation Conferences Convolution Discrete Fourier transforms non-negative matrix factorization Random variables reverberant mixtures Source separation Time-domain analysis variational inference |
| Title | Separating time-frequency sources from time-domain convolutive mixtures using non-negative matrix factorization |
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