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Sound source localization based on discrimination of cross-correlation functions

机译：基于互相关函数判别的声源定位

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摘要

Sound source localization plays a crucial role in many microphone arrays application, ranging from speech enhancement to human-computer interface in a reverberant noisy environment. The steered response power (SRP) using the phase transform (SRP-PHAT) method is one of the most popular modern localization algorithms. The SRP-based source localizers have been proved robust, however, the methods may fail to locate the sound source in adverse noise and reverberation conditions, especially when the direct paths to the microphones are unavailable. This paper proposes a localization algorithm based on discrimination of cross-correlation functions. The cross-correlation functions are calculated by the generalized cross-correlation phase transform (CCC-PHAT) method. Using cross-correlation functions, sound source location is estimated by one of the two classifiers: Naive-Bayes classifier and Euclidean distance classifier. Simulation results have demonstrated that the proposed algorithms provide higher localization accuracy than the SRP-PHAT algorithm in reverberant noisy environment.
机译：声源定位在许多麦克风阵列应用中起着至关重要的作用，范围从语音增强到混响嘈杂环境中的人机界面。使用相位变换（SRP-PHAT）方法的转向响应功率（SRP）是最流行的现代定位算法之一。事实证明，基于SRP的源定位器是可靠的，但是，这些方法可能无法在不利的噪声和混响条件下定位声源，尤其是在没有指向麦克风的直接路径时。提出了一种基于互相关函数判别的定位算法。通过广义互相关相位变换（CCC-PHAT）方法计算互相关函数。使用互相关函数，可以通过两个分类器之一来估计声源位置：朴素贝叶斯分类器和欧几里德距离分类器。仿真结果表明，所提出的算法在混响噪声环境中比SRP-PHAT算法具有更高的定位精度。

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