Stochastic gradient implementation of spatially preprocessed multi-channel Wiener filtering for noise reduction in hearing aids

Recently, a generalized noise reduction scheme was proposed, called the spatially preprocessed speech distortion weighted multi-channel Wiener filter (SP-SDW-MWF). Compared to GSC with quadratic inequality constraint (QIC-GSC), the SP-SDW-MWF reduces more noise, for a given maximum speech distortion level. We develop time-domain and frequency-domain stochastic gradient implementations of the SP-SDW-MWF. Experimental results with a hearing aid show that the proposed stochastic gradient algorithm preserves the benefit of the SP-SDW-MWF over the QIC-GSC, while its computational cost is comparable to the NLMS based scaled projection algorithm (SPA) for QIC-GSC.