Audio servo for robotic systems with pinnae

Kumon, Makoto; Shimoda, Tomoko; Kohzawa, Ryuichi; Mizumoto, Ikuro; Iwai, Zenta

doi:10.1109/iros.2005.1545092

Cited by 20 publications

(23 citation statements)

References 10 publications

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“…Peaks in the resulting histogram are then regarded as potential sound azimuths. This allows to cope with the multisource case, where [37], [35], [39], [40]. They all share the fundamental asymmetry property.…”

Section: Horizontal 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

133

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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: Horizontal Localizationmentioning

confidence: 99%

“…Reproducing such capabilities in Robotics is a difficult problem due to the lack of a model of the pinnae shapes which lead to elevation dependent notches. Yet, as a rule of thumb, these shapes must be irregular or asymmetric, and artificial pinnae were proposed in [37], [38], [31], [35], or [39]. Fig.…”

Section: Vertical Localization: Spectral Cuesmentioning

confidence: 99%

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

Argentieri

Danès

Souères

2015

Computer Speech & Language

133

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show abstract

“…Harris et al designed analog circuitry to acquire the time difference between the direct and indirect propagation induced from the pinna structure [13]. The localization dimension of a binaural system was extended to a vertical axis by adapting a pinna-like reflector and cue-detection algorithm [14–18]. Several structures around the microphone were studied for understanding the head-related transfer function in order to enhance a directivity pattern [19].…”

Section: Introductionmentioning

confidence: 99%

Monaural Sound Localization Based on Structure-Induced Acoustic Resonance

Kim

2015

Sensors

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A physical structure such as a cylindrical pipe controls the propagated sound spectrum in a predictable way that can be used to localize the sound source. This paper designs a monaural sound localization system based on multiple pyramidal horns around a single microphone. The acoustic resonance within the horn provides a periodicity in the spectral domain known as the fundamental frequency which is inversely proportional to the radial horn length. Once the system accurately estimates the fundamental frequency, the horn length and corresponding angle can be derived by the relationship. The modified Cepstrum algorithm is employed to evaluate the fundamental frequency. In an anechoic chamber, localization experiments over azimuthal configuration show that up to 61% of the proper signal is recognized correctly with 30% misfire. With a speculated detection threshold, the system estimates direction 52% in positive-to-positive and 34% in negative-to-positive decision rate, on average.

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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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Audio servo for robotic systems with pinnae

Cited by 20 publications

References 10 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

Monaural Sound Localization Based on Structure-Induced Acoustic Resonance

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

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