Evaluation of the Penalized Inequality Constrained Minimum Variance Beamformer for Hearing Aids
Jin‐Jun Xiao, Wenqiang Pu, Zhi-Quan Tom Luo, Tao Zhang · 2018
Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional beamforming algorithms such as linearly constrained minimum variance (LCMV) beamformer cannot effectively suppress multiple interferences when the degree of freedom (DoF) of the array is less than the number of sources in the environment. In [1], a penalized inequality-constrained minimum variance (P-ICMV) beamformer was proposed to address this challenge. In this study, we evaluate the P-ICMV beamformer and compare its performance with other beamformers including the LCMV in a multiple-interference environment. In an objective evaluation, objective metrics related to speech intelligibility and sound quality are used to compare the algorithm performance. In a subjective evaluation, the speech intelligibility of the beamformer processed stimuli are evaluated using normal-hearing listeners. Both the objective and subjective evaluation results show that the P-ICMV beamformer can suppress the interferences more effectively than the existing beamformers when the array DoF is limited.