Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Papers -- Digital Signal Processing |
Robust Noise Suppression Algorithm with the Kalman Filter Theory for White and Colored Disturbance
1 The author is with the Department of Electronic Systems Engineering, Tokyo University of Science, Chino-shi, 391-0292 Japan. E-mail: nari{at}rs.suwa.tus.ac.jp, 2 The author is with the Department of Management Science, Tokyo University of Science, Tokyo, 162-0825 Japan. E-mail: furukawa{at}ms.kagu.tus.ac.jp, 3 The author is with the Graduate School of Information Security, Institute of Information Security, Yokohama-shi, 221-0835 Japan. E-mail: tsujii{at}iisec.ac.jp
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.
Key Words: robust noise suppression, Kalman filter, canonical state space models, white and colored noises, high performance, high quality, AR system, driving source
Manuscript received July 6, 2007. Manuscript revised November 13, 2007.
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