Localization of sound sources in robotics: A review

https://hal.archives-ouvertes.fr/hal-01058575/document

A Survey on Sound Source Localization in Robotics: from Binaural to Array Processing Methods

In search and rescue activities, unmanned aerial vehicles (UAV) should exploit sound information to compensate for poor visual information. This paper describes the design and implementation of a UAV-embedded microphone array system for sound source localization in outdoor environments. Four critical development problems included water-resistance of the microphone array, efficiency in assembling, reliability of wireless communication, and sufficiency of visualization tools for operators. To solve these problems, we developed a spherical microphone array system (SMAS) consisting of a microphone array, a stable wireless network communication system, and intuitive visualization tools. The performance of SMAS was evaluated with simulated data and a demonstration in the field. Results confirmed that the SMAS provides highly accurate localization, water resistance, prompt assembly, stable wireless communication, and intuitive information for observers and operators.

/pdf/design-of-uav-embedded-microphone-array-system-for-sound-2hdcn4uld6.pdf

Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments

A UAV is provided to cancel background noise from audio data collected by the UAV. The UAV is provided with one or more background microphones in a proximity of one or more background noise-producing components. The UAV is also provided with one or more audio source collecting microphones. The audio data collected by the background microphones may be used to reduce or cancel interfering background noise from the audio signal detected by the audio source collecting microphone. The target audio may be captured or recorded with little or no background noise.

An unmanned aerial vehicle (uav) for collecting audio data

A method has been developed for improving sound source localization (SSL) using a microphone array from an unmanned aerial vehicle with multiple rotors, a “multirotor UAV”. One of the main problems in SSL from a multirotor UAV is that the ego noise of the rotors on the UAV interferes with the audio observation and degrades the SSL performance. We employ a generalized eigenvalue decomposition-based multiple signal classification (GEVD-MUSIC) algorithm to reduce the effect of ego noise. While GEVD-MUSIC algorithm requires a noise correlation matrix corresponding to the auto-correlation of the multichannel observation of the rotor noise, the noise correlation is nonstationary due to the aerodynamic control of the UAV. Therefore, we need an adaptive estimation method of the noise correlation matrix for a robust SSL using GEVD-MUSIC algorithm. Our method uses a Gaussian process regression to estimate the noise correlation matrix in each time period from the measurements of self-monitoring sensors attached to the UAV such as the pitch-roll-yaw tilt angles, xyz speeds, and motor control values. Experiments compare our method with existing SSL methods in terms of precision and recall rates of SSL. The results demonstrate that our method outperforms existing methods, especially under high signal-to-noise-ratio conditions.

/pdf/noise-correlation-matrix-estimation-for-improving-sound-4tb0imrzkj.pdf

Noise correlation matrix estimation for improving sound source localization by multirotor UAV

This paper addresses auditory scene analysis, especially, sound source localization using an aerial vehicle with a microphone array in an outdoor environment. Since such a vehicle is able to search sound sources quickly and widely, it is useful to detect outdoor sound sources, for instance, to find distressed people in a disaster situation. In such an environment, noise is quite loud and dynamically-changing, and conventional microphone array techniques studied in the field of indoor robot audition are of less use. We, thus, proposed MUltiple SIgnal Classification based on incremental Generalized EigenValue Decomposition (iGEVD-MUSIC). It can deal with high power noise by introducing a noise correlation matrix and GEVD even when the signal-to-noise ratio is less than 0 dB. In addition, the noise correlation matrix is incrementally estimated to adapt to dynamic changes in noise. We developed a prototype system for auditory scene analysis based on the proposed method using the Parrot AR.Drone with a microphone array and a Kinect device. Experimental results using the prototype system showed that dynamically-changing noise is properly suppressed with the proposed method and multiple human voice sources are able to be localized even when the AR.Drone is moving in an outdoor environment.

http://www.jp.honda-ri.com/upload/document/entry/20121016/Okutani_2012_939_6210.pdf

Outdoor auditory scene analysis using a moving microphone array embedded in a quadrocopter

https://www.jstage.jst.go.jp/article/jrsj/31/7/31_31_676/_pdf

Keita Okutani

Papers

Outdoor auditory scene analysis using a moving microphone array embedded in a quadrocopter

Noise correlation matrix estimation for improving sound source localization by multirotor UAV

Incremental Noise Estimation in Outdoor Auditory Scene Analysis using a Quadrocopter with a Microphone Array