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Improving Audio Spatialization Using Customizable Pinna Based Anthropometric Model of Head-Related Transfer Functions
Abstract
The role of binaural and immersive sound is becoming crucial in virtual reality and HCI related systems. This chapter proposes a structural model for the pinna, to be used as a block within structural models for the synthesis of Head-Related Transfer Functions, needed for digital audio spatialization. An anthropometrically plausible pinna model is presented, justified and verified by comparison with measured Head-Related Impulse Responses (HRIRs). Similarity levels better than 90% are found in this comparison. Further, the relationships between key anthropometric features of the listener and the parameters of the model are established, as sets of predictive equations. Modeled HRIRs are obtained substituting anthropometric features measured from 10 volunteers into the predictive equations to find the model parameters. These modeled HRIRs are used in listening tests by the subjects to assess the elevation of spatialized sound sources. The modeled HRIRs yielded a smaller average elevation error (29.9o) than “generic” HRIRs (31.4o), but higher than the individually measured HRIRs for the subjects (23.7o).
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