@InProceedings{GrijalvaGoldFlorMart:2015:MaLeUs,
author = "Grijalva, Felipe and Goldenstein, Siome and Florencio, Dinei and
Martini, Luiz",
affiliation = "School of Electrical and Computer Engineering, University of
Campinas, Campinas, Brazil. and Institute of Computing, University
of Campinas, Campinas, Brazil. and Multimedia, Interaction and
Communication Group, Microsoft Research, Redmond, WA, USA. and
School of Electrical and Computer Engineering, University of
Campinas, Campinas, Brazil.",
title = "Manifold learning using Isomap applied to spatial audio
personalization",
booktitle = "Proceedings...",
year = "2015",
editor = "Segundo, Maur{\'{\i}}cio Pamplona and Faria, Fabio Augusto",
organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Isomap, Manifold Learning, Spatial Audio.",
abstract = "As augmented reality applications become more important, there is
increasing effort in spatial audio research. The term spatial
audio or 3D sound refers to techniques where a person's anatomy is
modeled as digital filters. By filtering a sound source with these
filters, a listener is capable of perceiving a sound as though it
were reproduced at a specific spatial location. In the frequency
domain, these filters are known as Head-Related Transfer
Functions(HRTFs). A significant problem for the implementation of
3D sound systems is the fact that spectral features of HRTFs
differ widely among individuals due to their anatomical
differences. Thus, it is necessary to personalize them to
guarantee high quality sound perception. With this aim, we
introduce a new anthropometric-based method for customizing of
HRTFs in the horizontal plane using manifold learning. The method
uses Isomap, artificial neural networks (ANN), and a
neighborhood-based reconstruction procedure. We first modify
Isomap's graph construction step to emphasize the individuality of
HRTFs and perform a customized nonlinear dimensionality reduction
of the HTRFs. We then use an ANN to model the nonlinear
relationship between anthropometric features and our
low-dimensional HRTFs. Finally, we use a neighborhood-based
reconstruction approach to reconstruct the HRTF from the estimated
low-dimensional version. Simulations show that our approach
performs better than PCA (Principal Component Analysis) and
confirm that Isomap is capable of discovering the underlying
nonlinear relationships of sound perception.",
conference-location = "Salvador, BA, Brazil",
conference-year = "26-29 Aug. 2015",
language = "en",
ibi = "8JMKD3MGPBW34M/3JUMPP2",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JUMPP2",
targetfile = "WTD sibgrapi 2015 Camera Ready.pdf",
urlaccessdate = "2024, May 03"
}