Individualized HRTFs From Few Measurements: a Statistical Learning Approach

Abstract

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEEInternational audienceVirtual Auditory Space (VAS) refers to the synthesis and simulation of spatial hearing using earphones and/or a speaker system. High-fidelity VAS requires the use of individualized head-related transfer functions (HRTFs) which describe the acoustic filtering properties of the listener's external auditory periphery. HRTFs serve the increasingly dominant role of implementation 3-D audio systems, which have been realized in some commercial applications. However, the cost of a 3-D audio system cannot be brought down because the efficiency of computation, the size of memory, and the synthesis of unmeasured HRTFs remain to be made better. Because HRTFs are unique for each user depending on his morphology, the economically realist synthesis of individualized HRTFs has to rely on some measurements. This paper presents a way to reduce the cost of a 3-D audio system using a statistical modeling which allows to use only few measurements for each user