Multiple Sound Source Mapping for a Mobile Robot by Self-motion Triangulation

Sasaki, Yoko; Kagami, Satoshi; Mizoguchi, Hiroshi

doi:10.1109/iros.2006.281797

Cited by 68 publications

(39 citation statements)

References 4 publications

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“…뿐만 아니라, 사람과 로봇의 원활한 상호작용을 위한 로봇의 청각 시스템에 활용되기도 한다 [4]. 로봇의 청각 시스템에 적용된 음원 위치 추정 방법 에는 로봇의 머리전달함수를 이용한 방법, 다수의 마이크로 폰 어레이를 이용하여 빔포밍을 적용한 방법, 인공귀를 이용 한 방법 등이 있다 [5][6][7][8][9]. 또한 두 마이크로폰 사이의 도달시 간지연을 이용한 음원 위치 추정 방법도 널리 연구되고 있으 며, 이는 적은 계산량과 넓고 정확한 방향 검지 특성을 갖고 있다 [10,11].…”

Section: 서론 마이크로폰 어레이에서 측정된 음향 신호를 이용하여 음 원의 방향 및 위치를 알아내는 방법을 음원 unclassified

Spatially Mapped GCC Function Analysis for Multiple Source and Source Localization Method

Kwon¹,

Park²,

Park³

2010

Journal of Institute of Control, Robotics and Systems

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A variety of methods for sound source localization have been developed and applied to several applications such as noise detection system, surveillance system, teleconference system, robot auditory system and so on. In the previous work, we proposed the sound source localization using the spatially mapped GCC functions based on TDOA for robot auditory system. Performance of the proposed one for the noise effect and estimation resolution was verified with the real environmental experiment under the single source assumption. However, since multi-talker case is general in human-robot interaction, multiple source localization approaches are necessary. In this paper, the proposed localization method under the single source assumption is modified to be suitable for multiple source localization. When there are two sources which are correlated, the spatially mapped GCC function for localization has three peaks at the real source locations and imaginary source location. However if two sources are uncorrelated, that has only two peaks at the real source positions. Using these characteristics, we modify the proposed localization method for the multiple source cases. Experiments with human speeches in the real environment are carried out to evaluate the performance of the proposed method for multiple source localization. In the experiments, mean value of estimation error is about 1.4° and percentage of multiple source localization is about 62% on average.

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Section: 서론 마이크로폰 어레이에서 측정된 음향 신호를 이용하여 음 원의 방향 및 위치를 알아내는 방법을 음원 unclassified

Spatially Mapped GCC Function Analysis for Multiple Source and Source Localization Method

Kwon¹,

Park²,

Park³

2010

Journal of Institute of Control, Robotics and Systems

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show abstract

“…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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“…Such techniques can also help a robot to self-localize in its working area. Therefore, the sound source localization (one or more sources) has been studied by many researchers (Ying & Runze, 2007); (Sasaki et al, 2006); . Sound localization can be defined as the process of determining the spatial location of a sound source based on multiple observations of the received sound signals.…”

Section: Introductionmentioning

confidence: 99%