Multiple auditory template matching using expansion

In this paper we consider multiple template matching techniques for auditory localization. In our approach, auditory localization is based on extracting localization cues from the ratios of the incoming sound spectra at the two ears. Localization cues can be extracted from such ratios by matching them with stored templates of ratios of head related transfer functions. Here we compare the performance of several matching techniques in their ability to accurately extract localization cues from such ratios. We introduce a new Discriminative Matching Measure (DMM), a similarity measure to be optimized, and formulate a novel linear matching scheme which optimizes this measure. The DMM is similar to our Discriminative Signal-to-Noise Ratio measure. We compare the performance of several linear techniques, namely correlation and normalized correlation and our novel optimal matching method and also a non-linear method based on the backpropagation algorithm