Head-related transfer functions (HRTFs) describe the spectral filtering that occurs between a source sound and the listener's eardrum. Since HRTFs vary as a function of relative source location and subject, practical implementation of 3-D audio must take into account a large set of HRTFs for different azimuths and elevations. Previous work has proposed several HRTF models for data reduction. This report describes work in applying genetic algorithms to find a set of HRTF basis spectra, and least squares to compute the optimal combination of linear weights to represent the individual HRTFs at different azimuths and elevations. The genetic algorithm selects the basis spectra from the set of original HRTF amplitude responses, using an average relative spectral error as the fitness function. Encouraging results from the experiments suggest that genetic algorithms provide an effective approach to this data reduction problem.
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