Model-based Noise PSD Estimation from Speech in Non-stationary Noise

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Resumé

Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation. The proposed framework allows us to include prior spectral information about the speech and noise sources, can be configured to have zero tracking delay, and does not depend on estimated speech presence probabilities. This is in contrast to other noise PSD estimators which often have a too large tracking delay to give good results in non- stationary situations and offer no consistent way of including prior information about the speech or the noise type. The results show that the proposed method outperforms state-of-the-art noise PSD estima- tors in terms of tracking speed and estimation accuracy.
OriginalsprogEngelsk
TitelIEEE International Conference on Acoustics, Speech, and Signal Processing
Antal sider5
Udgivelses stedCalgary, Canada
ForlagIEEE
Publikationsdato10 sep. 2018
Sider5424-5428
Artikelnummer8461683
ISBN (Elektronisk)978-1-5386-4658-8
DOI
StatusUdgivet - 10 sep. 2018
Begivenhed2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Varighed: 15 apr. 201820 apr. 2018
https://2018.ieeeicassp.org/

Konference

Konference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
LandCanada
ByCalgary
Periode15/04/201820/04/2018
Internetadresse
NavnI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Fingerprint

Power spectral density
Speech enhancement

Citer dette

Nielsen, J. K., Kavalekalam, M. S., Christensen, M. G., & Boldt, J. B. (2018). Model-based Noise PSD Estimation from Speech in Non-stationary Noise. I IEEE International Conference on Acoustics, Speech, and Signal Processing (s. 5424-5428). [8461683] Calgary, Canada: IEEE. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings https://doi.org/10.1109/ICASSP.2018.8461683
Nielsen, Jesper Kjær ; Kavalekalam, Mathew Shaji ; Christensen, Mads Græsbøll ; Boldt, Jesper Bünsow. / Model-based Noise PSD Estimation from Speech in Non-stationary Noise. IEEE International Conference on Acoustics, Speech, and Signal Processing. Calgary, Canada : IEEE, 2018. s. 5424-5428 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).
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title = "Model-based Noise PSD Estimation from Speech in Non-stationary Noise",
abstract = "Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation. The proposed framework allows us to include prior spectral information about the speech and noise sources, can be configured to have zero tracking delay, and does not depend on estimated speech presence probabilities. This is in contrast to other noise PSD estimators which often have a too large tracking delay to give good results in non- stationary situations and offer no consistent way of including prior information about the speech or the noise type. The results show that the proposed method outperforms state-of-the-art noise PSD estima- tors in terms of tracking speed and estimation accuracy.",
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Nielsen, JK, Kavalekalam, MS, Christensen, MG & Boldt, JB 2018, Model-based Noise PSD Estimation from Speech in Non-stationary Noise. i IEEE International Conference on Acoustics, Speech, and Signal Processing., 8461683, IEEE, Calgary, Canada, I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, s. 5424-5428, Calgary, Canada, 15/04/2018. https://doi.org/10.1109/ICASSP.2018.8461683

Model-based Noise PSD Estimation from Speech in Non-stationary Noise. / Nielsen, Jesper Kjær; Kavalekalam, Mathew Shaji; Christensen, Mads Græsbøll; Boldt, Jesper Bünsow.

IEEE International Conference on Acoustics, Speech, and Signal Processing. Calgary, Canada : IEEE, 2018. s. 5424-5428 8461683 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

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

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AB - Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation. The proposed framework allows us to include prior spectral information about the speech and noise sources, can be configured to have zero tracking delay, and does not depend on estimated speech presence probabilities. This is in contrast to other noise PSD estimators which often have a too large tracking delay to give good results in non- stationary situations and offer no consistent way of including prior information about the speech or the noise type. The results show that the proposed method outperforms state-of-the-art noise PSD estima- tors in terms of tracking speed and estimation accuracy.

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Nielsen JK, Kavalekalam MS, Christensen MG, Boldt JB. Model-based Noise PSD Estimation from Speech in Non-stationary Noise. I IEEE International Conference on Acoustics, Speech, and Signal Processing. Calgary, Canada: IEEE. 2018. s. 5424-5428. 8461683. (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings). https://doi.org/10.1109/ICASSP.2018.8461683