標題: Subband Kalman filtering for speech enhancement
作者: Wu, WR
Chen, PC
電信工程研究所
Institute of Communications Engineering
關鍵字: AR modeling;Kalman filtering;LMS algorithm;speech enhancement;subband filtering
公開日期: 1-八月-1998
摘要: Kalman filtering is an effective speech-enhancement technique, in which speech signals are usually modeled as autoregressive (AR) processes and represented in the state-space domain. Since AR coefficients identification and Kalman filtering require extensive computations, real-time implementation of this approach is difficult. This paper proposes a simple and practical scheme that overcomes these obstacles. Speech signals are first decomposed into subbands. Subband speech signals are then modeled as low-order AR processes, such that low-order Kalman filters can he applied. Enhanced fullband speech signals are finally obtained by combining the enhanced subband speech signals. To identify AR coefficients, prediction-error filters adapted by the LMS algorithm are applied. Due to noisy inputs, the LMS algorithm converges to biased solutions. The performance of the Kalman filter with biased parameters is analyzed, It is shown that accurate estimates of AR coefficients are not required when the driving-noise variance is properly estimated. New methods for making such estimates are proposed. Thus, we can tolerate biased AR coefficients and take advantage of the LMS algorithm's simple structure. Simulation results show that speech enhancement in the subband domain not only greatly reduces the computational complexity, but also achieves better performance compared to that in the fullband domain.
URI: http://dx.doi.org/10.1109/82.718814
http://hdl.handle.net/11536/32461
ISSN: 1057-7130
DOI: 10.1109/82.718814
期刊: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING
Volume: 45
Issue: 8
起始頁: 1072
結束頁: 1083
顯示於類別:期刊論文


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  1. 000075817000012.pdf