Head-related transfer functions (HRTFs) are essential in binaural audio. Because HRTFs are highly individualized and difficult to acquire, much research has been devoted towards improving HRTF performance for the general population. Such research requires a valid and robust method for classifying and comparing HRTFs. This study used a k-nearest neighbor (KNN) classifier to evaluate the ability of several different frequency arrays to characterize HRTFs. The perceptual impact of these frequency arrays was evaluated through a subjective test. Mel-frequency arrays showed the best results in the KNN classification tests while the subjective test results were inconclusive.
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