Interpolation of Head-Related Transfer Functions Using Manifold Learning

Author(s)

Grijalva, Felipe ; Martini, Luiz Cesar ; Florencio, Dinei ; Goldenstein, Siome

• Document type: Journal article
• Source: IEEE SIGNAL PROCESSING LETTERS; v. 24, n. 2, p. 221-225, FEB 2017.
• Web of Science Citations: 7

Abstract

We propose a new head-related transfer function (HRTF) interpolation method using Isomap, a nonlinear dimensionality reduction technique. First, we construct a singlemanifold for all subjects across both azimuth and elevation angles through the construction of an intersubject graph (ISG) that includes important prior knowledge of the HRTFs such as correlations across individuals, directions, and ears. Then, for a new direction, we predict its corresponding low-dimensional HRTF by interpolating over same subject low-dimensional measured HRTFs. Finally, we use a local neighborhood mapping in the manifold to reconstruct the high-dimensional HRTF from measured HRTFs of all subjects. We show that a single manifold representation obtained through the ISG is a powerful way to allow measured HRTFs from different subjects to contribute for reconstructing the HRTFs for new directions. Moreover, our results suggest that a small number of spatial measurements capture most of acoustical properties of HRTFs. Finally, our approach outperforms other linear and nonlinear dimensionality reduction techniques such as principal component analysis, locally linear embedding, and Laplacian eigenmaps. (AU)

• FAPESP's process: 12/50468-6 - Vision for the blind: translating 3D visual concepts into 3D auditory clues
• Grantee: Siome Klein Goldenstein
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• FAPESP's process: 13/21349-1 - Vision for the blind: translating 3D visual concepts into 3D auditory clues
• Grantee: Felipe Leonel Grijalva Arévalo
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 14/14630-9 - Machine learning for signal processing applied to spatial audio
• Grantee: Felipe Leonel Grijalva Arévalo
• Support Opportunities: Scholarships in Brazil - Doctorate