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Conference papers

Autonomous Sensorimotor Learning for Sound Source Localization by a Humanoid Robot

Quan Nguyen 1 Laurent Girin 1 Gérard Bailly 1 Frédéric Elisei 1, 2 Duc-Canh Nguyen 1
2 GIPSA-Services - GIPSA-Services
GIPSA-lab - Grenoble Images Parole Signal Automatique
Abstract : We consider the problem of learning to localize a speech source using a humanoid robot equipped with a binaural hearing system. We aim to map binaural audio features into the relative angle between the robot's head direction and the target source direction based on a sensorimotor training framework. To this end, we make the following contributions: (i) a procedure to automatically collect and label audio and motor data for sensorimotor training; (ii) the use of a convolutional neural network (CNN) trained with white noise signal and ground truth relative source direction. Experimental evaluation with speech signals shows that the CNN can localize the speech source even without an explicit algorithm for dealing with missing spectral features.
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Submitted on : Wednesday, November 14, 2018 - 10:41:24 AM
Last modification on : Monday, November 29, 2021 - 4:27:27 PM
Long-term archiving on: : Friday, February 15, 2019 - 1:03:43 PM


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  • HAL Id : hal-01921882, version 1



Quan Nguyen, Laurent Girin, Gérard Bailly, Frédéric Elisei, Duc-Canh Nguyen. Autonomous Sensorimotor Learning for Sound Source Localization by a Humanoid Robot. IROS 2018 - Workshop on Crossmodal Learning for Intelligent Robotics in conjunction with IEEE/RSJ IROS, Oct 2018, Madrid, Spain. ⟨hal-01921882⟩



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