Abstract
Sound source localization based on a microphone array for mobile robots faces great challenges because of factors such as uncertainty of the robot's movement, noise and reverberation, the requirements of a compact microphone array and so on. This paper studies a method for sound source localization in a dynamic environment using a cross-microphone array plane, which consists of four microphones on an intelligent mobile robot platform. Firstly, the method of spectral subtraction and campestral mean normalization is introduced to de-noise. Then GCC-PHAT-ργ and the guided spectral-temporal (ST) position method are proposed to suppress noise and reverberation based on the generalized cross-correlation method to estimate time delay. Finally, the sound source is positioned by adopting the geometric location method. This system is tested by a total of 2,016 sets of experiments. Even in an intensely noisy and reverberating environment, the guided ST position method achieves angle positioning accuracy of more than 95% with a less than 15 degrees localization error. Meanwhile, all the experimental data can be processed in real-time, within 0.4s.
Highlights
The technology of sound source localization for robots is a simulation of the human auditory system
Phase transition (PHAT) weighting function sharpens the peak of time domain cross‐ correlation function by pre‐whitening the cross‐spectrum of signals, which improves the performance of time delay estimation (TDE) to a certain extent when environmental noise and reverberation exist
In this paper a speech source localization method is proposed for a mobile robot, which uses single voice or multiple voices to locate the angle and distance of a speaker
Summary
The technology of sound source localization for robots is a simulation of the human auditory system. Phase transition (PHAT) weighting function sharpens the peak of time domain cross‐ correlation function by pre‐whitening the cross‐spectrum of signals, which improves the performance of TDE to a certain extent when environmental noise and reverberation exist. This algorithm has low computational complexity and is easy to implement. If environmental noise and reverberation is too large, the accuracy of TDE will decline significantly It estimates single time delay, which indicates that it cannot be applied to multiple sound sources.
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