Learning the Direction of a Sound Source Using Head Motions and Spectral Features

Antoine Deleforge 1 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In this paper we address the problem of localizing a sound-source by combining binaural or monaural spectral features with head movements. Based on a number of psychophysical and behavioral studies suggesting that the problem of spatial hearing is both listener-dependent and dynamic, we propose to address the problem at hand within the framework of unsupervised learning. More precisely, our method is able to retrieve an intrinsic low-dimensional parameterization from the high-dimensional spectral representation of the acoustic input. We address both binaural and monaural spatial localization with both static and dynamic cues. We show that the recovered low-dimensional representations are homeomorphic to the two-dimensional manifold associated with the motor states of a robotic head with two rotational degrees of freedom. We describe the experimental setup and protocols allowing us to gather acoustic data sets with ground truth for both the emitter-to-listener directions and precise head motions. We validate our method using extensive experiments that consist in classifying acoustic vectors from a test set, based on manifold learning with a different training set. Our method strongly contrasts with current approaches in sound localization because it puts forward the role of learning.
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Antoine Deleforge, Radu Horaud. Learning the Direction of a Sound Source Using Head Motions and Spectral Features. [Research Report] RR-7529, INRIA. 2011, pp.29. ⟨inria-00564708⟩

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