差分传声器阵列期望最大化多声源方位估计方法

针对小尺寸传声器阵列多声源方位估计易受混响噪声影响的问题，提出了一种适用于差分传声器阵列的期望最大化多声源方位估计方法。首先，该方法利用期望最大化算法求解出各个时频点瞬时方位估计所应满足的高斯混合模型参数;然后，通过时频点分离技术估计出各声源的方位值。针对现有的硬、软时频点分离技术应用于差分传声器阵列所存在的缺陷，还提出了一种改进的时频点分离方法，该方法融合了软、硬分离方法所具有的优点，有效降低了时频点分离结果对混响噪声的敏感性。仿真和实测实验结果表明:相较于现有的差分传声器阵列多声源估计方法，所提方法在混响噪声环境下具有更高的估计精度和稳健性能。

A localization approach for multiple sound sources via an expectation maximization algorithm using differential microphone arrays

It is known that conventional sound localization approaches with small-sized microphone arrays are usually sensitive to noise and reverberation. To deal with the problem, an approach based on expectation maximization (EM) algorithm with differential microphone arrays (DMAs) is proposed. Firstly, the approach is to estimate the parameters of Gaussian mixture model for time-frequency instantaneous direction estimation through the EM algorithm, and secondly, to find the direction estimation of each sound source via time-frequency separation. In order to overcome the weakness of existing time-frequency separation techniques, i.e., the hard and soft separation methods, an improved time-frequency separation method, which combines the advantages of both the hard and soft separation methods, is also proposed. The improved time-frequency separation method is shown to be less sensitive to noise and reverberation. Simulation and experimental results demonstrate that the proposed localization approach is superior to its existing counterparts in terms of localization accuracy and robustness characteristics.