Millions of people use headphones everyday for listening to music, for watching movies, or when communicating with others. Nevertheless, the sounds presented via headphones are usually perceived inside the head and not at their actual natural spatial position. This limited perception is inherent and results in unrealistic listening situations.
When listening to a sound without headphones, the acoustic information of the sound source is modified by our head and our torso, an effect described by the head-related transfer functions (HRTFs). The shape of our ears contributes to that modification by filtering the sound depending on the source direction. But the ear is very listener-specific – its individuality is similar to that of a finger print, and thus HRTFs are very listener-specific. When listening to sounds via headphones, the listener-specific filtering is usually not available. One of the main reasons is the difficulty in the process of acquisition of the ear shape of a person, and thus in calculation of listener-specific HRTFs.
Thus, in softpinna, we will work on the development of new methods for a better acquisition of listener-specific ear shapes of a person. Specifically, we will investigate and improve the so-called "non-rigid registration" (NRR) algorithms, applied on 3-D ear geometries calculated from 2-D photos of a person’s ears. The improvement in the quality of the 3-D ear geometries acquisition will allow computer programs to accurately calculate the listener-specific HRTFs, thus enabling the incorporation of listener-specific HRTFs in future headphone systems providing realistic presentation of spatial sounds. The new ear-shape acquisition method will vastly reduce the technical requirements for accurate calculation of listener-specific HRTFs.