Estimating the azimuth of a sound source from the binaural spectral amplitude

Abstract

A computational model of auditory localization based on processing spectral amplitude cues for localizing broadband high frequency sound sources is presented. The cues extracted are binaural spectral level di erence patterns of the Head Related Transfer Functions (HRTF) corresponding to the direction of the sound source. Four di erent pattern classi ers are used to evaluate the feasibility of localization using such spectral cues. The four classi ers investigated are normalized correlation, maximum likelihood estimation, a backpropagation based fuzzy neural network model and a novel linear ltering method which optimizes a new Discriminative Matching Measure (DMM). The DMM is de ned to quantify the relative ability of each of these classi ers to identify such patterns in the presence of noise or with missing data. An independent estimate of the localization acuity using the RMS error is also made. The results indicate that both optimal DMM ltering and the fuzzy neural network model are successful in extracting azimuthal location of broadband high frequency sound sources. The proposed model attempts to complement current auditory localization theory by exploring the role of HRTF based binaural spectral level di erence cues in localizing high frequency broadband auditory stimuli.