一种改进的AEDA声源定位及跟踪算法

开展了基于麦克风阵列的真实声场环境声源定位的工作。针对传统的自适应特征值分解时延估计算法收敛时间慢、对初值敏感以及不能有效跟踪时延变化等问题，提出了一种改进的自适应特征值分解时延估计算法，该方法通过改进初值设定方法，有效改善了对时延变化的估计。另外，通过引入一个基于相关运算的语音检测算法，提高了定位系统的抗噪声能力。实验表明在真实的声场环境下该算法能够对单个声源的三维空间位置进行实时的定位和跟踪，系统在 1.5m 范围内对声源的定位误差小于 8cm ，声源位置变化时，系统也能准确跟踪声源的位置。

A Modified AEDA Algorithm for Sound Source Localization and Tracking

Sound source localization and tracking has turned to be one of hotspots in acoustic signal processing area in recent years. It is widely adopted in a lot of applications, such as multimedia conference, intelligent robot, speech enhancement, etc. Adaptive Eigenvalue Deposition Algorithm (AEDA) is one of the effective methods for its robustness performance of noise and reverberation. However, AEDA is suffered from its slowness in tracking variation of time delay of arrival (TDOA) as well as its sensitivity to initial value. Faced with such problems, a Modified Adaptive Eigenvalue Decomposition Algorithm (MAEDA) for time delay estimation is proposed, based on which an emulation system is developed. Experimental results show that the proposed new algorithm works well in sound source location and moving sound source tracking, meanwhile, it overcomes the drawbacks of the traditional AEDA algorithm.