2D sound-source localization on the binaural manifold

The problem of 2D sound-source localization based on a robotic binaural setup and audio-motor learning is addressed. We first introduce a methodology to experimentally verify the existence of a locally-linear bijective mapping between sound-source positions and high-dimensional interaural data, usin...

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Bibliographic Details
Published in2012 IEEE International Workshop on Machine Learning for Signal Processing pp. 1 - 6
Main Authors Deleforge, Antoine, Horaud, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
Data models
Manifolds
Probabilistic logic
Robots
Spectrogram
Training
Vectors
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ISBN1467310247
9781467310246
ISSN1551-2541
DOI10.1109/MLSP.2012.6349784

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Summary:The problem of 2D sound-source localization based on a robotic binaural setup and audio-motor learning is addressed. We first introduce a methodology to experimentally verify the existence of a locally-linear bijective mapping between sound-source positions and high-dimensional interaural data, using manifold learning. Based on this local linearity assumption, we propose an novel method, namely probabilistic piecewise affine regression, that learns the localization-to-interaural mapping and its inverse. We show that our method outperforms two state-of-the art mapping methods, and allows to achieve accurate 2D localization of natural sounds from real world binaural recordings.
ISBN:1467310247
9781467310246
ISSN:1551-2541
DOI:10.1109/MLSP.2012.6349784