A Kalman filter based on wavelet filter-bank and psychoacoustic modeling for speech enhancement

This paper presents a subband adaptive filter based on wavelet filter-bank for speech enhancement. The adaptation of Kalman filter in wavelet domain has effectively reduced the non-stationary noise. A perceptual weighting filter exploiting the masking properties of psychoacoustic model is concatenated with the Kalman filter to further improve the intelligibility of speech. The proposed method owns its merits from the successful porting of Kalman filter into the wavelet domain so that speech analysis and enhancement can be carried out in time-frequency spectrum based on the human auditory model. Experimental results show that the speech enhancement system is capable of reducing noise with little speech degradation in adverse noise environments and the overall performance is superior to several competitive methods