Estimation of individualized HRTF in unsupervized conditions

International audienceHead Related Transfer Functions (HRTF) are the key features of binaural sound spatialization. Those filters arespecific to each individual and generally measured in an anechoic room using a complex process. Although theuse of non-individual filters can cause perceptual artefacts, the generalization of such measurements is hardlyaccessible for large public. Thus, many authors have proposed alternative individualization methods to prevent frommeasuring HRTFs. Examples of such methods are based on numerical modelling, adaptation of non-individualHRTFs or selection of non-individual HRTFs from a database. In this article, we propose an individualizationmethod where the best matching set of HRTFs is selected from a database on the basis of an unsupervised binauralrecording of the listener in real-life environment