2012 IEEE International Conference on Robotics and Automation 2012
DOI: 10.1109/icra.2012.6224972
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Sensorimotor learning of sound localization from an auditory evoked behavior

Abstract: Abstract-A new method for self-supervised sensorimotor learning of sound source localization is presented, that allows a simulated listener to learn an auditorimotor map from the sensorimotor experience provided by an auditory evoked behavior. The map represents the auditory space and is used to estimate the azimuthal direction of sound sources. The learning mainly consists in non-linear dimensionality reduction of sensorimotor data. Our results show that an auditorimotor map can be learned, both from real and… Show more

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Cited by 13 publications
(14 citation statements)
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“…One can also mention [109] -who proposed a motion planning system whose objective is to maximize the effectiveness of a speech recognition module-, or [110,111] -where the sound localization problem is rewritten in terms of a sensorimotor approach, with experiments made on the famous Psikharpax rat robot from the European FP7-ICT-IP ICEA (Integrating Cognition Emotion and Autonomy) project-. Stochastic filtering has also emerged as an ideal tool for sound localization and tracking during robot movement [112].…”
Section: Resultsmentioning
confidence: 99%
“…One can also mention [109] -who proposed a motion planning system whose objective is to maximize the effectiveness of a speech recognition module-, or [110,111] -where the sound localization problem is rewritten in terms of a sensorimotor approach, with experiments made on the famous Psikharpax rat robot from the European FP7-ICT-IP ICEA (Integrating Cognition Emotion and Autonomy) project-. Stochastic filtering has also emerged as an ideal tool for sound localization and tracking during robot movement [112].…”
Section: Resultsmentioning
confidence: 99%
“…Recent computational modeling of the development of auditory spatial representations (Aytekin et al 2008;Bernard et al, 2012) has shown that an ordered representation of space can be established through unsupervised sensory-motor learning. Put simply, a representation can be learnt by analyzing the auditory consequences of moving the system's directional sensors by a known amount.…”
Section: Discussionmentioning
confidence: 99%
“…6). From a computational perspective, some recent models have been developed whereby auditory spatial representations can be created by unsupervised sensorimotor learning where receiver motion is integrated with acoustic information (Aytekin et al 2008;Bernard et al 2012). Whether the motion signal is of motor (efference copy) or sensory origin (proprioception) is not known.…”
Section: Accommodation To the New Spectral Cuesmentioning
confidence: 99%