Application of localization models to noise suppression in hearing aids

One major problem of hearing-impaired people is the cocktail-party effect, i.e., the reduced ability to understand speech in noisy environments. Normal-hearing listeners use binaural information, i.e., interaural level and phase/time differences, in addition to monaural cues to localize sound sources in the environment. Based on the localization result and on the monaural cues, the listener performs a significant noise reduction by concentrating on one of the sources. Noise reduction algorithms have been proposed that try to imitate the binaural information processing in the hearing system by using two microphones at both ears and a central processor. This setup allows for the use of interaural parameters to estimate the direction of sound sources and to attenuate unwanted sound source directions. However, in noisy environments the binaural parameters become noisy as well, reducing the accuracy of the sound source estimation and therefore reducing the maximum performance of the noise reduction algorithm. In this presentation, a localization algorithm is introduced, which uses a statistical description of the binaural parameters in order to perform a robust estimation of sound source directions in noisy environments. Noise reduction schemes based on this localization algorithm are discussed.