On Optimal Smoothing in Minimum Statistics Based Noise Tracking

A. Chinaev, R. Haeb-Umbach, in: Interspeech 2015, 2015, pp. 1785–1789.

Conference Paper | English
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Abstract
Noise tracking is an important component of speech enhancement algorithms. Of the many noise trackers proposed, Minimum Statistics (MS) is a particularly popular one due to its simple parameterization and at the same time excellent performance. In this paper we propose to further reduce the number of MS parameters by giving an alternative derivation of an optimal smoothing constant. At the same time the noise tracking performance is improved as is demonstrated by experiments employing speech degraded by various noise types and at different SNR values.
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Interspeech 2015
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1785-1789
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Chinaev A, Haeb-Umbach R. On Optimal Smoothing in Minimum Statistics Based Noise Tracking. In: Interspeech 2015. ; 2015:1785-1789.
Chinaev, A., & Haeb-Umbach, R. (2015). On Optimal Smoothing in Minimum Statistics Based Noise Tracking. In Interspeech 2015 (pp. 1785–1789).
@inproceedings{Chinaev_Haeb-Umbach_2015, title={On Optimal Smoothing in Minimum Statistics Based Noise Tracking}, booktitle={Interspeech 2015}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2015}, pages={1785–1789} }
Chinaev, Aleksej, and Reinhold Haeb-Umbach. “On Optimal Smoothing in Minimum Statistics Based Noise Tracking.” In Interspeech 2015, 1785–89, 2015.
A. Chinaev and R. Haeb-Umbach, “On Optimal Smoothing in Minimum Statistics Based Noise Tracking,” in Interspeech 2015, 2015, pp. 1785–1789.
Chinaev, Aleksej, and Reinhold Haeb-Umbach. “On Optimal Smoothing in Minimum Statistics Based Noise Tracking.” Interspeech 2015, 2015, pp. 1785–89.

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