Improvements in intelligibility of noisy reverberant speech using a binaural subband adaptive noise-cancellation processing scheme.

Abstract

This article reports on the performance of an adaptive subband noise cancellation scheme, which performs binaural preprocessing of speech signals for a hearing-aid application. The multi-microphone subband adaptive (MMSBA) signal processing scheme uses the least mean squares (LMS) algorithm in frequency-limited subbands. The use of subbands enables a diverse processing mechanism to be employed, splitting the two-channel wide-band signal into smaller frequency-limited subbands, which can be processed according to their individual signal characteristics. The frequency delimiting used a linear- or cochlear-spaced subband distribution. The effect of the processing scheme on speech intelligibility was assessed in a trial involving 15 hearing-impaired volunteers with moderate sensorineural hearing loss. The acoustic material consisted of speech and speech-shaped noise signals, generated using simulated and real-room acoustic environments, at signal-to-noise ratios (SNRs) in the range -6 to +3 dB. The results show that the MMSBA scheme delivered average speech intelligibility improvements of 11.5%, with a maximum of 37.25%, in noisy reverberant conditions. There was no significant reduction in mean speech intelligibility due to processing, in any of the test conditions.