Abstract:

Delay and Sum Beamforming（DSB） is widely used in angle of arrival estimation of microphone array signals. However, this method is not ideal for azimuth estimation of multiple speech signal sources due to the problem of grid lobe in speech signal sources. In addition, in the actual complex environment, it is affected by noise and reverberation, which makes azimuth recognition more difficult. In order to solve these problems, an improved DSB method is proposed, which combines signal frequency and microphone array spacing to select the frequency points in the sub-segment, and then weighs the data covariance matrix. At the same time, experiments are carried out in the simulation and actual environment, and the results show that, compared with the unimproved DSB method, the computational complexity of this method is reduced to 18.37%, and the amount of computation is effectively reduced. In the simulation experiment, the average angle positioning deviation is reduced by 27.3%, 21.4% and 36%, respectively, under different reflection coefficients of 0.2, 0.4 and 0.6, respectively. In the actual environmental experiment, the maximum azimuth angle estimation deviation is 9° and the minimum azimuth angle estimation deviation is 1.35°, which is lower than 12.1° and 3° of the unimproved algorithm.

Key words: delay and sum, beamforming, microphone array, covariance matrix, multi-source localization

摘要：

延迟求和波束形成（DSB）在麦克风阵列信号到达角估计上有着广泛应用，然而在语音信号源下由于栅瓣等问题使得该方法对多个语音信号源方位估计不理想，此外，在实际复杂环境下，该方法受噪声混响影响，方位识别更加困难。针对这些问题，提出一种改进的DSB方法，联合信号频率及麦克风阵列间距对子段内的频点进行选择，之后对数据协方差矩阵加权处理。同时在仿真及实际环境下进行实验，结果表明，与未改进DSB方法相比，该方法计算量降低为原来的18.37%，有效地降低了运算量；仿真实验中在不同反射系数0.2、0.4、0.6下，平均角度定位偏差分别降低了27.3%、21.4%、36%；实际环境实验方位角度估计偏差最大值为9°、最低为1.35°，要低于未改进算法的12.1°和3°。

关键词: 延迟求和, 波束形成, 麦克风阵列, 协方差矩阵, 多声源定位