Measuring Anthropometric Data for HRTF Personalization

Nowadays, multimodal human-like sensing, e.g. vision, hap tics and audition seeks to improve interaction between an operator (human) and a teleoperator (robot) in human centered robotic systems. Head Related Transfer Function (HRTF) based sound rendering techniques, which seek to create a realistic virtual auditory space for listeners, have become a prominent concept in human robot interaction. Applications that demand high quality 3D sound synthesis are usually based on measured HRTFs of listeners. Recently, researchers propose to construct a set of personalized HRTFs using multiple linear regression models between anthropometric data and measured HRTFs, which implies the existence of a training HRTF dataset together with the corresponding anthropometric data. This paper focuses on the measurement of Head-Related transfer Functions (HRTFs) and the corresponding anthropometric data of a listener. Several state-of-the-art techniques of measuring the HRTFs are described. For measuring the anthropometric data, we develop a low budget approach, which enables us to measure the anthropometry of a person within short time at a high accuracy, whereas the hardware costs for the scanning system are significantly reduced.