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