The use of Head-Related Transfer Functions for binaural rendering of spatial audio is quickly emerging in today’s audio market. Benefits of individual HRTFs, or personalized HRTF selection, have been demonstrated in numerous previous studies. A number of recent works have examined assisted or automated selection of HRTFs for optimized personalization. Such techniques attempt to rank HRTFs according to expected spatial quality for a given user based on signal, morphological, and/or perceptual studies. In parallel, there exist several HRTF processing methods that are often used to compact and/or smooth HRTFs in order to facilitate real-time treatments. Nevertheless, the potential impact of such processes on HRTF spatial quality is not always considered. This study examines the effects of three commonly used HRTF processing techniques (spectral smoothing in constant absolute bandwidths, minimum-phase decomposition, and infinite impulse response modeling) on perceptual quality ratings of selected HRTFs. Results showed that the frequency and phase-spectra variations introduced in the data by the three processing methods can lead to significant changes in HRTF evaluations. In addition, they highlight the challenging nature of non-individualized HRTF rating tasks and establish the need for systematic participant screening and sufficient task repetitions in perceptual HRTF evaluation studies.
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