Individualized head-related transfer functions (HRTFs) are closely related to anthropometry (measurements of torso, head, and pinna) of listeners. This relation not only derives the individualized HRTFs from anthropometric measurements, but can also be viewed as a means to derive the anthropometry of the listener from his/her measured HRTFs (bypass direct anthropometric measurements). In this study, we propose to estimate a person’s anthropometry information using the linear representation obtained from the individualized HRTF features of the person and a HRTF feature database with a number of subjects. Five different HRTF features as well as their best combination are considered in the training stage. Although our experiments showed that the performance of these methods varies in general, the best combination method yields considerable accuracy for the estimation of most anthropometric features. The proposed idea also provides further insights on the complex relation between anthropometry and HRTFs. Our experiment revealed that the anthropometric features that are not well estimated could be removed from HRTF individualization process without causing significant performance degradation.
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