A comparison of auditory localization models

A novel approach for auditory localization of broadband high frequency acoustic stimuli is presented. Computational models for extracting localization cues from estimations of the incoming sound spectra at the two ears are developed. These models can be structured as feedforward networks to classify binaural ratio patterns of head related transfer functions (HRTF). The computational models studied are pattern classifiers based on normalized correlation, fuzzy neural networks and a novel linear filtering method which optimizes a new discriminative matching measure (DMM). The DMM is defined to quantify the relative ability of each of these classifiers to identify such patterns in the presence of noise. Both optimal DMM matching and fuzzy neural networks prove successful in localization with good DMM and acceptably small RMS errors