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A Head Related Transfer Function (HRTF) characterizes how a human earreceives sounds from a point in space, and depends on the shapes of one's head,pinna, and torso. Accurate estimations of HRTFs for human subjects are crucialin enabling binaural acoustic applications such as sound localization and 3Dsound spatialization. Unfortunately, conventional approaches for HRTFestimation rely on specialized devices or lengthy measurement processes. Thiswork proposes a novel lightweight method for HRTF individualization that can beimplemented using commercial-off-the-shelf components and performed by averageusers in home settings. The proposed method has two key components: agenerative neural network model that can be individualized to predict HRTFs ofnew subjects from sparse measurements, and a lightweight measurement procedurethat collects HRTF data from spatial locations. Extensive experiments using apublic dataset and in house measurement data from 10 subjects of different agesand genders, show that the individualized models significantly outperform abaseline model in the accuracy of predicted HRTFs. To further demonstrate theadvantages of individualized HRTFs, we implement two prototype applications forbinaural localization and acoustic spatialization. We find that the performanceof a localization model is improved by 15 degree after trained withindividualized HRTFs. Furthermore, in hearing tests, the success rate ofcorrectly identifying the azimuth direction of incoming sounds increases by183% after individualization.

Individualizing Head-Related Transfer Functions for Binaural Acoustic Applications