SPEECH PROCESSING FOR HEARING-AIDS - NOISE-REDUCTION MOTIVATED BY MODELS OF BINAURAL INTERACTION

Several signal processing techniques are reviewed that aim at reducing ambient noise and enhancing the ''desired'' speech signal in complex acoustical environments (''cocktail party processing''). These algorit hms are motivated by models of binaural interaction in the normal huma n auditory system arid try to simulate several different aspects of no rmal auditory function that are typically impaired in hearing-impaired listeners. All algorithms assume input signals from microphones locat ed near the ears of a subject and one or two output signals to be pres ented. The first class of algorithms performs a directional filtering with respect to the forward direction and a reduction of the perceived reverberation. The second class of algorithms performs an analysis in the modulation frequency domain and combines binaural cues with cues from modulation frequency analysis to perform a noise-robust direction al filtering. The third class of algorithms simulates a localization p rocess in a way comparable to neurophysiological findings in the barn owl, while the fourth class of algorithms combines cues from binaural interaction and fundamental frequency analysis. The respective psychoa coustical and physiological motivation of these algorithms as well as their advantages and shortcomings are outlined. In addition, the hardw are and software required for implementing and testing these algorithm s in real-time are introduced and discussed. Since most of these algor ithms are shown to provide significant benefit by increasing the ''eff ective'' signal-to-noise ratio in different acoustical situations, a c ombination of these algorithms appears promising for future ''intellig ent'' digital hearing aids.