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Real-time Sound Source Localization for a Mobile Robot Based on the Guided Spectral-Temporal Position Method

机译:基于引导频谱时间位置方法的移动机器人的实时声源定位

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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-partial derivative gamma 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.
机译:基于麦克风阵列的声源定位,用于移动机器人面临巨大的挑战,因为机器人运动,噪音和混响的不确定等因素,紧凑型麦克风阵列的要求等。本文研究了使用交叉麦克风阵列平面的动态环境中的声源定位方法,该方法由智能移动机器人平台上的四个麦克风组成。首先,引入了光谱减法和校准的频谱均衡方法以丧失噪声。然后,提出了基于广义互相关方法来抑制噪声和混响的GCC-PHAT部分导数伽马和引导谱 - 局部衍生物γ和引导谱 - 时间(ST)位置方法来估计延迟时间延迟。最后,通过采用几何位置方法定位声源。该系统经过2,016套实验测试。即使在一个强烈的嘈杂和混响的环境中,引导的ST位置方法也实现了超过95%的角度定位精度,定位误差小于15度。同时,所有实验数据都可以实时处理,0.4s。

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