Blind 2-Microphone Acoustic Noise Reduction Algorithms Using Efficient Variable Step-Size Adapted by Minimizing the Intercorrelation Function

Abstract Recently, there have been advancements in adaptive reduction of noise signal using 2-microphones adaptive algorithms. Specifically, the normalized form of least-mean-square algorithm (NLMS) with fixed-step-size parameters (FS) has been combined with direct-and-recursive structures of source separation. Compared to conventional one-microphone methods, these combinations provide speech quality superiority. However, the main limitation of these 2-microphones adapting algorithms (Direct combination: Forward NLMS and Recursive combination: Backward NLMS) is their poor steady state regime with large FS value, while small step-sizes values result a slow speed of convergence. To address these issues, we presented in this study a new step-size approach (VS) based the intercorrelation function minimization in the time domain for the basic FNLMS and BNLMS algorithms. Our approach is proposed exactly to obtain an optimal value of VS parameters by intercorrelation minimization among the enhanced signal and noisy microphone ones. The proposition optimization improves both the steady state values and convergence speed simultaneously. The proposed 2-microphones adapting algorithms were evaluated through simulations conducted in highly noisy environments, using the system of mismatch criterion and output estimation of signal-to-noise ratio ones. The comparative simulations results confirmed that our approach outperforms existing methods in terms of effectiveness and efficiency.