Noise Variance Estimation for Kalman Filtering of Noisy Speech

Wooil KIM; Hanseok KO

Noise Variance Estimation for Kalman Filtering of Noisy Speech

Wooil KIM, Hanseok KO

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This paper proposes an algorithm that adaptively estimates time-varying noise variance used in Kalman filtering for real-time speech signal enhancement. In the speech signal contaminated by white noise, the spectral components except dominant ones in high frequency band are expected to reflect the noise energy. Our approach is first to find the dominant energy bands over speech spectrum using LPC. We then calculate the average value of the actual spectral components over the high frequency region excluding the dominant energy bands and use it as the noise variance. The resulting noise variance estimate is then applied to Kalman filtering to suppress the background noise. Experimental results indicate that the proposed approach achieves a significant improvement in terms of speech enhancement over those of the conventional Kalman filtering that uses the average noise power over silence interval only. As a refinement of our results, we employ multiple-Kalman filtering with multiple noise models and improve the intelligibility.

Publication: IEICE TRANSACTIONS on Information Vol.E84-D No.1 pp.155-160

Publication Date: 2001/01/01

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Type of Manuscript: PAPER

Category: Speech and Hearing

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Wooil KIM, Hanseok KO, "Noise Variance Estimation for Kalman Filtering of Noisy Speech" in IEICE TRANSACTIONS on Information, vol. E84-D, no. 1, pp. 155-160, January 2001, doi: .
Abstract: This paper proposes an algorithm that adaptively estimates time-varying noise variance used in Kalman filtering for real-time speech signal enhancement. In the speech signal contaminated by white noise, the spectral components except dominant ones in high frequency band are expected to reflect the noise energy. Our approach is first to find the dominant energy bands over speech spectrum using LPC. We then calculate the average value of the actual spectral components over the high frequency region excluding the dominant energy bands and use it as the noise variance. The resulting noise variance estimate is then applied to Kalman filtering to suppress the background noise. Experimental results indicate that the proposed approach achieves a significant improvement in terms of speech enhancement over those of the conventional Kalman filtering that uses the average noise power over silence interval only. As a refinement of our results, we employ multiple-Kalman filtering with multiple noise models and improve the intelligibility.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_1_155/_p

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@ARTICLE{e84-d_1_155,
author={Wooil KIM, Hanseok KO, },
journal={IEICE TRANSACTIONS on Information},
title={Noise Variance Estimation for Kalman Filtering of Noisy Speech},
year={2001},
volume={E84-D},
number={1},
pages={155-160},
abstract={This paper proposes an algorithm that adaptively estimates time-varying noise variance used in Kalman filtering for real-time speech signal enhancement. In the speech signal contaminated by white noise, the spectral components except dominant ones in high frequency band are expected to reflect the noise energy. Our approach is first to find the dominant energy bands over speech spectrum using LPC. We then calculate the average value of the actual spectral components over the high frequency region excluding the dominant energy bands and use it as the noise variance. The resulting noise variance estimate is then applied to Kalman filtering to suppress the background noise. Experimental results indicate that the proposed approach achieves a significant improvement in terms of speech enhancement over those of the conventional Kalman filtering that uses the average noise power over silence interval only. As a refinement of our results, we employ multiple-Kalman filtering with multiple noise models and improve the intelligibility.},
keywords={},
doi={},
ISSN={},
month={January},}

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TY - JOUR
TI - Noise Variance Estimation for Kalman Filtering of Noisy Speech
T2 - IEICE TRANSACTIONS on Information
SP - 155
EP - 160
AU - Wooil KIM
AU - Hanseok KO
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2001
AB - This paper proposes an algorithm that adaptively estimates time-varying noise variance used in Kalman filtering for real-time speech signal enhancement. In the speech signal contaminated by white noise, the spectral components except dominant ones in high frequency band are expected to reflect the noise energy. Our approach is first to find the dominant energy bands over speech spectrum using LPC. We then calculate the average value of the actual spectral components over the high frequency region excluding the dominant energy bands and use it as the noise variance. The resulting noise variance estimate is then applied to Kalman filtering to suppress the background noise. Experimental results indicate that the proposed approach achieves a significant improvement in terms of speech enhancement over those of the conventional Kalman filtering that uses the average noise power over silence interval only. As a refinement of our results, we employ multiple-Kalman filtering with multiple noise models and improve the intelligibility.
ER -