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Estimation of Individualized HRTF in Unsupervised Conditions

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 artifacts, 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 modeling, 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 unsupervised binaural recordings of the listener in a real-life environment.


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