New efficient two channel forward set-membership partial-update NLMS algorithms for blind speech enhancement and acoustic noise reduction

Abstract

This paper addresses the problem of speech enhancement by partial adaptive algorithms combined with the forward blind source separation (FBSS) structure. We propose three new two-channel forward partial update algorithms: (i) The first algorithm is based on the use of the correlation properties to adapt and control the variable step-size parameter of adaptive cross-filters of the FBSS structure, this algorithm is called the two channel correlated set membership partial update NLMS (TC-SM-PU-NLMScor). (ii) The second algorithm is based on the use of a new coefficients update relations to control the normalization energy of the adaptive NLMS cross filters of the FBSS structure. This algorithm is called the two channel set member ship partial update regularized NLMS (TC-SM-PU-RNLMS). (iii) The third algorithm is a combination of proposed 1 (TC-SM-PU-NLMScor) and proposed 2 (TC-SM-PU-RNLMS), this algorithm is called the two channel correlated set membership partial update regularized NLMS (TC-SM-PU-RNLMScor). In this paper, we show the obtained results of the comparison between the three proposed algorithms and the original two channel correlated set membership partial NLMS (TC-SM-PU-NLMS) algorithm. This comparison is expressed in terms of objective criteria as the System Mismatch (SM), the Segmental Mean Square Error (SegMSE) and the Segmental SNR (SegSNR).