Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering

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Resumé

Supervised non-negative matrix factorization (NMF) is effective in speech enhancement through training spectral models of speech and noise signals. However, the enhancement quality reduces when the models are trained on data that is not highly relevant to a speech signal and a noise signal in a noisy observation. In this paper, we propose to train a classifier in order to overcome such poor characterization of the signals through the trained models. The main idea is to decompose the noisy observation into parts and the enhanced signal is reconstructed by combining the less-corrupted ones which are identified in the cepstral domain using the trained classifier. We apply unsupervised NMF followed by Wiener filtering for the decomposition, and use a support vector machine trained on the mel-frequency cepstral coefficients of the parts of training speech and noise signals for the classification. The results show the effectiveness of the proposed method compared with the supervised NMF.
OriginalsprogEngelsk
Titel26th European Signal Processing Conference (EUSIPCO)
Antal sider5
ForlagIEEE
Publikationsdato2018
Artikelnummer8553123
ISBN (Elektronisk)978-9-0827-9701-5
DOI
StatusUdgivet - 2018
Begivenhed26th European Signal Processing Conference (EUSIPCO 2018) - Rome, Italien
Varighed: 3 sep. 20187 sep. 2018
Konferencens nummer: 26
http://www.eusipco2018.org

Konference

Konference26th European Signal Processing Conference (EUSIPCO 2018)
Nummer26
LandItalien
ByRome
Periode03/09/201807/09/2018
Internetadresse
NavnProceedings of the European Signal Processing Conference
ISSN2076-1465

Fingerprint

Speech enhancement
Factorization
Classifiers
Support vector machines
Decomposition

Citer dette

Fakhry, M., Poorjam, A. H., & Christensen, M. G. (2018). Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering. I 26th European Signal Processing Conference (EUSIPCO) [8553123] IEEE. Proceedings of the European Signal Processing Conference https://doi.org/10.23919/EUSIPCO.2018.8553123
Fakhry, Mahmoud ; Poorjam, Amir Hossein ; Christensen, Mads Græsbøll. / Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering. 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. (Proceedings of the European Signal Processing Conference).
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title = "Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering",
abstract = "Supervised non-negative matrix factorization (NMF) is effective in speech enhancement through training spectral models of speech and noise signals. However, the enhancement quality reduces when the models are trained on data that is not highly relevant to a speech signal and a noise signal in a noisy observation. In this paper, we propose to train a classifier in order to overcome such poor characterization of the signals through the trained models. The main idea is to decompose the noisy observation into parts and the enhanced signal is reconstructed by combining the less-corrupted ones which are identified in the cepstral domain using the trained classifier. We apply unsupervised NMF followed by Wiener filtering for the decomposition, and use a support vector machine trained on the mel-frequency cepstral coefficients of the parts of training speech and noise signals for the classification. The results show the effectiveness of the proposed method compared with the supervised NMF.",
author = "Mahmoud Fakhry and Poorjam, {Amir Hossein} and Christensen, {Mads Gr{\ae}sb{\o}ll}",
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Fakhry, M, Poorjam, AH & Christensen, MG 2018, Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering. i 26th European Signal Processing Conference (EUSIPCO)., 8553123, IEEE, Proceedings of the European Signal Processing Conference, Rome, Italien, 03/09/2018. https://doi.org/10.23919/EUSIPCO.2018.8553123

Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering. / Fakhry, Mahmoud; Poorjam, Amir Hossein; Christensen, Mads Græsbøll.

26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. 8553123 (Proceedings of the European Signal Processing Conference).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering

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AU - Christensen, Mads Græsbøll

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N2 - Supervised non-negative matrix factorization (NMF) is effective in speech enhancement through training spectral models of speech and noise signals. However, the enhancement quality reduces when the models are trained on data that is not highly relevant to a speech signal and a noise signal in a noisy observation. In this paper, we propose to train a classifier in order to overcome such poor characterization of the signals through the trained models. The main idea is to decompose the noisy observation into parts and the enhanced signal is reconstructed by combining the less-corrupted ones which are identified in the cepstral domain using the trained classifier. We apply unsupervised NMF followed by Wiener filtering for the decomposition, and use a support vector machine trained on the mel-frequency cepstral coefficients of the parts of training speech and noise signals for the classification. The results show the effectiveness of the proposed method compared with the supervised NMF.

AB - Supervised non-negative matrix factorization (NMF) is effective in speech enhancement through training spectral models of speech and noise signals. However, the enhancement quality reduces when the models are trained on data that is not highly relevant to a speech signal and a noise signal in a noisy observation. In this paper, we propose to train a classifier in order to overcome such poor characterization of the signals through the trained models. The main idea is to decompose the noisy observation into parts and the enhanced signal is reconstructed by combining the less-corrupted ones which are identified in the cepstral domain using the trained classifier. We apply unsupervised NMF followed by Wiener filtering for the decomposition, and use a support vector machine trained on the mel-frequency cepstral coefficients of the parts of training speech and noise signals for the classification. The results show the effectiveness of the proposed method compared with the supervised NMF.

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Fakhry M, Poorjam AH, Christensen MG. Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering. I 26th European Signal Processing Conference (EUSIPCO). IEEE. 2018. 8553123. (Proceedings of the European Signal Processing Conference). https://doi.org/10.23919/EUSIPCO.2018.8553123