Motion strategies for binaural localisation of speech sources in azimuth and distance by artificial listeners

Lu, Yan‐Chen; Cooke, Martin

doi:10.1016/j.specom.2010.06.001

Cited by 25 publications

(15 citation statements)

References 31 publications

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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%

A survey on sound source localization in robotics: From binaural to array processing methods

Argentieri

Danès

Souères

2015

Computer Speech & Language

136

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This paper attempts to provide a state-of-the-art of sound source localization in Robotics. Noticeably, this context raises original constraints-e.g. embeddability, real time, broadband environments, noise and reverberation-which are seldom simultaneously taken into account in Acoustics or Signal Processing. A comprehensive review is proposed of recent robotics achievements, be they binaural or rooted in Array Processing techniques. The connections are highlighted with the underlying theory as well as with elements of physiology and neurology of human hearing.

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Section: Resultsmentioning

confidence: 99%

A survey on sound source localization in robotics: From binaural to array processing methods

Argentieri

Danès

Souères

2015

Computer Speech & Language

136

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“…The manifold is learned from the initial states through Laplacian eigenmaps. The final states, projected on the manifold with the out-of-sample extension, are all projected on a region indicating a centered sound source, thus providing an experimental evidence of the existence of a reference state as introduced in (2).…”

Section: B Orienting Behavior and Auditorimotor Map Learningmentioning

confidence: 98%

“…where e 0 and m 0 as in (2). In this simulation the motor state error δm is due to the sampling error of a constant rotation controlled by the sign of s ILD sum (t).…”

Section: Auditory Evoked Orientation Behavior (Ob)mentioning

confidence: 99%

“…Most studies in spatial hearing have considered a static environment, where both the listener and the source are immobile, but active skills involving the listener's own movements are known to contribute reliable cues for sound source localization, for example in distance perception or front-back disambiguation [1], [2]. Among the active processes involved in hearing, the auditory evoked orienting behavior (OB) is a reflex present in human newborns that consists of head and eyes movement toward a sound source [3].…”

Section: Introductionmentioning

confidence: 99%

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Sensorimotor learning of sound localization from an auditory evoked behavior

Bernard

Pirim

Cheveigné

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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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 simulated data, and that the online learning leads to accurate estimations of azimuthal sources direction.

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“…As can be seen, the movements of the robot improve source localization [12][13][14]. For example, head rotation can resolve the ambiguity on the angle of arrival [15,16].…”

Section: Introductionmentioning

confidence: 99%

Long-term robot motion planning for active sound source localization with Monte Carlo tree search

Nguyen

Colas

Charpillet

2017

2017 Hands-Free Speech Communications and Microphone Arrays (HSCMA)

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We consider the problem of controlling a mobile robot in order to localize a sound source. A microphone array can provide the robot with information on source localization. By combining this information with the movements of the robot, the localization accuracy can be improved. However, random robot motion or short-term planning may not result in optimal localization. In this paper, we propose an optimal longterm robot motion planning algorithm for active source localization. We introduce a Monte Carlo tree search (MCTS) method to find a sequence of robot actions that minimize the entropy of the belief on the source location. A tree of possible robot movements which balances between exploration and exploitation is first constructed. Then, the movement that leads to minimum uncertainty is selected and executed. Experiments and statistical results show the effectiveness of our proposed method on improving sound source localization in the long term compared to other motion planning methods.

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Motion strategies for binaural localisation of speech sources in azimuth and distance by artificial listeners

Cited by 25 publications

References 31 publications

A survey on sound source localization in robotics: From binaural to array processing methods

A survey on sound source localization in robotics: From binaural to array processing methods

Sensorimotor learning of sound localization from an auditory evoked behavior

Long-term robot motion planning for active sound source localization with Monte Carlo tree search

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