Active speaker localization with circular likelihoods and bootstrap filtering

Marković, Ivan; Portello, Alban; Danès, Patrick; Argentieri, Sylvain

doi:10.1109/iros.2013.6696769

Cited by 12 publications

(11 citation statements)

References 18 publications

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“…Generally, this is only possible if the sound source is static in the environment. Some recent works [43,44] proposed a filtering strategy to cope with a possibly moving source. The algorithm mainly relies on ITD to provide an estimation of the source position (r, θ) during the movement of a binaural sensor.…”

Section: Distance Localizationmentioning

confidence: 99%

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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: Distance Localizationmentioning

confidence: 99%

“…Recent contributions have proven the effectiveness of the approach in an active binaural experimental context, with the ability to cope with intermittent moving sources in the presence of false measurements [44,43].…”

mentioning

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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“…It is therefore likely that approaches that use MCT will still suffer from front-back confusions when tested under more demanding conditions. Also, previous work on binaural localization using mobile robots has typically fused information from various positions, but has not used human-like head movements to resolve confusions (e.g., [10]). …”

Section: Introductionmentioning

confidence: 99%

Robust localisation of multiple speakers exploiting head movements and multi-conditional training of binaural cues

May

Brown

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper addresses the problem of localizing multiple competing speakers in the presence of room reverberation, where sound sources can be positioned at any azimuth on the horizontal plane. To reduce the amount of front-back confusions which can occur due to the similarity of interaural time differences (ITDs) and interaural level differences (ILDs) in the front and rear hemifield, a machine hearing system is presented which combines supervised learning of binaural cues using multi-conditional training (MCT) with a head movement strategy. A systematic evaluation showed that this approach substantially reduced the amount of front-back confusions in challenging acoustic scenarios. Moreover, the system was able to generalize to a variety of different acoustic conditions not seen during training.

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“…One of the benefits that robots provide to source localization is active localization which fuses robot motion and the measurements taken at different poses of the robot [4][5][6][7][8][9][10][11]. As can be seen, the movements of the robot improve source localization [12][13][14].…”

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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Active speaker localization with circular likelihoods and bootstrap filtering

Cited by 12 publications

References 18 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

Robust localisation of multiple speakers exploiting head movements and multi-conditional training of binaural cues

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

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