Gaussian process models for HRTF based 3D sound localization

The human ability to localize sound-source direction using just two receivers is a complex process of direction inference from spectral cues of sound arriving at the ears. While these cues can be described using the well-known head-related transfer function (HRTF) concept, it is unclear as to how de...

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Bibliographic Details
Main Authors: Yuancheng Luo, Zotkin, Dmitry N., Duraiswami, Ramani
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Covariance matrices
Ear
Gaussian process regression
Ground penetrating radar
head-related transfer function
source cancellation algorithm
subset selection
Three-dimensional displays
Training
Transfer functions
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Summary:The human ability to localize sound-source direction using just two receivers is a complex process of direction inference from spectral cues of sound arriving at the ears. While these cues can be described using the well-known head-related transfer function (HRTF) concept, it is unclear as to how densely HRTF must be sampled and whether a higher-order representation is employed in localization. We propose a class of binaural sound source localization models to answer these two questions. First, using the sound received by two ears, we derive several binaural features that are invariant to the sound source signal. Second, these are implicitly mapped to a high-dimensional reproducing kernel Hilbert space via a Gaussian process regression model for feature-direction tuples. Lastly, the features that are most relevant in the model are found via an efficient forward subset-selection method. Experimental results are shown for HRTFs belonging to the CIPIC database.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6854122