Microphone array for 2D sound localization and capture

Kagami, Satoshi; Mizoguchi, Hiroshi; Tamai, Y.; Kanade, Takeo

doi:10.1109/robot.2004.1307231

Cited by 10 publications

(7 citation statements)

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“…For example, reverberation is a significant problem in TDOA analysis, as TDOA methods assume that the sound signal's route between the sound source and the recording sensor is straight, as Figure 8 demonstrates. With respect to practical applications, academic studies have used sound source localization to locate speakers in a meeting room [46][47][48], to detect face-to-face conversations in a home environment [43], to conduct real-time sound source localization in a hall [39], and to detect the user's position in robotic applications [49]. Another popular and well known application of sound source localization using a microphone array is Microsoft's Kinect-a motion sensing input device (http://www.microsoft.com/en-us/kinectforwindows/).…”

Section: Sound Source Localizationmentioning

confidence: 99%

A Review on Device-Free Passive Indoor Positioning Methods

Kivimäki¹,

Vuorela²,

Peltola³

et al. 2014

IJSH

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There is an increasing need for unobtrusive positioning systems in smart homes and assisted living environments. This review presents the main technologies used for device-free passive indoor positioning, including pressure sensors, thermal infrared sensors, sound source localization, ultrasound, radio frequency, infrastructure-mediated sensing and electric-field-based methods. In addition, it compares the advantages and disadvantages of a range of positioning systems to help identify the most suitable positioning technology for specific cases.

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Section: Sound Source Localizationmentioning

confidence: 99%

A Review on Device-Free Passive Indoor Positioning Methods

Kivimäki¹,

Vuorela²,

Peltola³

et al. 2014

IJSH

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“…It was found that a system combining TDOA and DOA estimation in a single step improves the system's robustness, while allowing localization (but not tracking) of simultaneous sources [3]. Kagami et al [9] reports a system using 128 microphones for 2D sound localization of sound sources: obviously, it would not be practical to include such a large number of microphones on a mobile robot.…”

Section: Related Workmentioning

confidence: 99%

Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering

Valin¹,

Michaud²,

Rouat³

2007

Robotics and Autonomous Systems

242

184

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Mobile robots in real-life settings would benefit from being able to localize and track sound sources. Such a capability can help localizing a person or an interesting event in the environment, and also provides enhanced processing for other capabilities such as speech recognition. To give this capability to a robot, the challenge is not only to localize simultaneous sound sources, but to track them over time. In this paper we propose a robust sound source localization and tracking method using an array of eight microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm. Results show that a mobile robot can localize and track in real-time multiple moving sources of different types over a range of 7 meters. These new capabilities allow a mobile robot to interact using more natural means with people in real life settings.

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“…Kagami et al present in [12] an array consisting of 128 microphones capable of localising sound sources. A large number of microphones increases the computational complexity and also the accuracy might not increase significantly.…”

Section: Sense Of Soundmentioning

confidence: 99%

Mitigating the effect of background noise in sound based helicopter control

Passow

Coupland

Góngora

2010

International Conference on Fuzzy Systems

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Abstract-Our research focuses on the use of sound to enhance the control of an autonomous indoor helicopterFlyper. One of the many challenging problems in this project is managing the uncertainty which is present in input data and the control actions; this paper focuses on managing the uncertainty in the input data. We present a fuzzy logic system which infers how much confidence should be placed on a control decision based on the data which was used to make that decision. The input data is a supervised and sound based position estimate of the flying robot. The output of the fuzzy inference system provides us with a confidence parameter used to attenuate the position control of the autonomous helicopter. We performed test flights with and without the fuzzy confidence parameter and with and without artificial disturbance in form of concurrent speech. We employed a motion tracker to capture the helicopter's movement during all test flights. The analysis of the data collected shows encouraging results.

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Microphone array for 2D sound localization and capture

Cited by 10 publications

References 0 publications

A Review on Device-Free Passive Indoor Positioning Methods

A Review on Device-Free Passive Indoor Positioning Methods

Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering

Mitigating the effect of background noise in sound based helicopter control

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