Modified LMS-Based Feedback-Reduction Subsystems in Digital Hearing Aids Based on WOLA Filter Bank

Digital hearing aids usually suffer from acoustic feedback. This feedback corrupts the speech signal, causes instability, and damages the speech intelligibility. To solve these problems, an acoustic feedback reduction (AFR) subsystem using adaptive algorithms such as the least mean square (LMS) algorithm is needed. Although this algorithm has a reduced computational cost, it is very unstable. To avoid this situation, other AFR subsystems based on modifications of the LMS algorithm are used. Such algorithms are given as follows: 1) normalized LMS (NLMS); 2) filtered-X LMS (FXLMS); and 3) normalized FXLMS (NFXLMS). These algorithms are tested in three digital hearing aid categories: 1) in the ear (ITE); 2) in the canal (ITC); and behind the ear (BTE). The first and second categories under study suffer from great feedback effects due to the short distance between the loudspeaker and the microphone, whereas the third category suffers from these effects due to the high signal level at the hearing aid output; thus, robust AFR subsystems are needed. The added stable gains (ASGs) over the limit gain when AFR subsystems are working in the digital hearing aids are studied for all the categories. The ASG is determined as a tradeoff between two measurements: 1) segmented signal-to-noise ratio (objective measurement) and 2) speech quality (subjective measurement). The results show how the digital hearing aids that work with AFR subsystems adapted with the NLMS or the NFXLMS algorithms can achieve up to 18 dB of increase over the limit gain. After analyzing the results, it is observed that the subjective measurement always limits the achieved ASG, but when the NLMS algorithm is used, it is appreciated that the objective measurement is a good approximation for estimating the maximum achieved ASG. Finally, taking into consideration the hearing aid performances and the computational cost of each AFR subsystem implementation, an AFR subsystem based on the NLMS algorithm to adapt feedback-reduction filters that are 128 coefficients long is proposed.