New two-microphone simplified sub-band forward algorithm based on separated variable step-sizes for acoustic noise reduction

Abstract

This paper presents an extended two-microphone adaptive filtering algorithm implemented on the sub-band simplified forward technique using new separated variable step-sizes parameters (denoted SSF-SVS: Simplified Sub-band Forward Separated Variable Step-sizes). This algorithm is proposed to reduce the acoustic noise and enhance the quality of estimated speech signal. Firstly, a sub-signal decomposition processing strategy is used to facilitating effective noise reduction with very fast convergence rate using two analysis filter banks. Secondly, a two-microphone simplified forward blind source separation (BSS) structure updated by the normalized least-mean-square algorithm (NLMS) for extracting the estimated speech signal. To guarantee a very fast convergence rate with high level of enhanced speech quality, thirdly we proposed new separated variable step-sizes parameters for controlling the adaptation of sub-band filters. The concept involves suggesting individual variable step-size parameter for each adaptive sub-filter, which are updated independently using the input sub-signal and the estimated output sub-signal. Experimental results confirm significant improvements in terms of convergence rate based on the mean square error criterion (MSE) in diverse acoustical environments. In other hand, the good performance of the proposed SSF-SVS algorithm in terms of speech quality is verified by on output signal-to-noise ratio (Output-SNR). The proposed algorithm compared with six two-microphone adaptive filtering algorithms in term of convergence rate, speech quality and computational complexity.