Use of k-means clustering analysis to select representative head related transfer functions for use in subjective studies

Abstract

A head related transfer function (HRTF) must be applied when creating auralizations; however, the HRTFs of individual subjects are not typically known in advance. Often, an overall ‘average’ HRTF is used instead. The purpose of this study was to develop a listening test to identify a ‘matched’ (best) and ‘unmatched’ (worst) HRTF for specific subjects, which could be applied to customize auralizations for individual participants. The method of k-means clustering was used to identify eight representative HRTFs from the CIPIC database. HRTFs from 45 subjects’ left and right ears in four directions were clustered, which resulted in 56 cluster centers (possible representative HRTFs). A comparative analysis was conducted to determine an appropriate set of HRTFs. These HRTFs were then convolved with pink noise bursts at 00 elevation and various azimuths to sound like the bursts were rotating around a subject’s head. A paired comparison test was used where listeners selected the ‘most natural’ sounding HRTF signal. ‘Most natural’ was described as coming from the correct directions and located outside the head. The results from the clustering analysis and listening test will be presented, along with a subjective study that incorporated the HRTF listening test. [Work was supported by NSF Grant 1302741.]