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Global HRTF Personalization Using Anthropometric Measures

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In this paper, we propose an approach for global HRTF personalization employing subjects’ anthropometric features using spherical harmonics transform (SHT) and convolutional neural network (CNN). Existing methods employ different models for each elevation, which fails to take advantage of the underlying common features of the full set of HRTF’s. Using the HUTUBS HRTF database as our training set, a SHT was used to produce subjects’ personalized HRTF’s for all spatial directions using a single model. The resulting predicted HRTFs have a log-spectral distortion (LSD) level of 3.81 dB in comparison to the SHT reconstructed HRTFs, and 4.74 dB in comparison to the measured HRTFs. The personalized HRTFs show significant improvement upon the finite element acoustic computations of HRTFs provided in the HUTUBS database.

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