Robustness Analysis of Binaural Hearing Aid Beamformer Algorithms by Means of Objective Perceptual Quality Measures

In this contribution different microphone array-based noise reduction schemes for hearing aids are suggested and compared in terms of their performance, signal quality and robustness against model errors. The algorithms all have binaural output and are evaluated using objective perceptual quality measures [1, 2, 3]. It has been shown earlier that these measures are able to predict subjective data that is relevant for the assessment of noise reduction algorithms. The quality measures showed clearly that fixed beamformers designed with head models were relatively robust against steering errors whereas for the adaptive beamformers tested in this study the robustness was limited and the benefit due to higher noise reduction depended on the noise scenario and the reliability of a direction of arrival estimation. Furthermore, binaural cue distortions introduced by the different binaural output strategies could be identified by the binaural speech intelligibility measure [3] even in case monaural quality values were similar. Thus, this perceptual quality measure seems to be suitable to discover the benefit that the listener might have from the effect of spatial unmasking.