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2D Sound-Source Localization on the Binaural Manifold

Antoine Deleforge 1 Radu Horaud 1, *
* Corresponding author
1 PERCEPTION [2007-2015] - Interpretation and Modelling of Images and Videos [2007-2015]
Inria Grenoble - Rhône-Alpes, LJK [2007-2015] - Laboratoire Jean Kuntzmann [2007-2015], Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019]
Abstract : 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.
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Submitted on : Saturday, December 22, 2012 - 5:54:06 PM
Last modification on : Thursday, July 23, 2020 - 10:02:02 AM
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Antoine Deleforge, Radu Horaud. 2D Sound-Source Localization on the Binaural Manifold. MLSP 2012 - IEEE Workshop on Machine Learning for Signal Processing, Sep 2012, Santander, Spain. pp.1-6, ⟨10.1109/MLSP.2012.6349784⟩. ⟨hal-00768657⟩

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