IEICE TRANSACTIONS on Fundamentals
Robust Noise Suppression Algorithm with the Kalman Filter Theory for White and Colored Disturbance
Summary :
We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.3 pp.818-829
- Publication Date
- 2008/03/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e91-a.3.818
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Keyword
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Nari TANABE, Toshihiro FURUKAWA, Shigeo TSUJII, "Robust Noise Suppression Algorithm with the Kalman Filter Theory for White and Colored Disturbance" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 3, pp. 818-829, March 2008, doi: 10.1093/ietfec/e91-a.3.818.
Abstract: We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.3.818/_p
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@ARTICLE{e91-a_3_818,
author={Nari TANABE, Toshihiro FURUKAWA, Shigeo TSUJII, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Robust Noise Suppression Algorithm with the Kalman Filter Theory for White and Colored Disturbance},
year={2008},
volume={E91-A},
number={3},
pages={818-829},
abstract={We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.},
keywords={},
doi={10.1093/ietfec/e91-a.3.818},
ISSN={1745-1337},
month={March},}
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TY - JOUR
TI - Robust Noise Suppression Algorithm with the Kalman Filter Theory for White and Colored Disturbance
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 818
EP - 829
AU - Nari TANABE
AU - Toshihiro FURUKAWA
AU - Shigeo TSUJII
PY - 2008
DO - 10.1093/ietfec/e91-a.3.818
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2008
AB - We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.
ER -
English
