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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Interpolation of Head-Related Transfer Functions Using Manifold Learning

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Author(s):
Grijalva, Felipe ; Martini, Luiz Cesar ; Florencio, Dinei ; Goldenstein, Siome
Total Authors: 4
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