基于单声矢量传感器的语音信号时频掩蔽盲分离改进算法

An Improved Blind Speech Separation Algorithm via Time-frequency Masking Based on a Single Acoustic Vector Sensor

利用单声矢量传感器精确的测向能力,提出了一种基于波达方位估计(DOA)的语音信号盲分离改进算法。该算法在时频域进行,采用基于混合冯·米塞斯分布的期望最大化算法对混合信号中各个源信号在每个时频点的概率进行估计。基于此,针对高混响及信号方位较近时很难估计到均值的问题,提出了一种简单、有效的改进算法,并在不同混响强度、不同方位差及不同混合信号数目情况下对其进行了仿真验证。仿真结果表明,相较于二值时频掩蔽和泛值时频掩蔽,文中提出的改进算法在信号-失真率(SDR)和客观感知质量(PESQ)两方面均有较大提高。

An improved blind speech separation algorithm is presented based on the direction of arrival（DOA） estimation, which is obtained by the precise direction finding ability of a single acoustic vector sensor（AVS）. The proposed algorithm works in time-frequency domain, in which the probability at each time-frequency unit of a specific source is estimated via an expectation-maximization（EM） algorithm based on the von Mises distribution mixture model. Because the mean value is difficult to estimate when the reverberation level is high or the sources are placed closely, a simple but effective improved algorithm is proposed, and is verified via simulation under different reverberation level, direction difference and source number. Simulation results show that the improved algorithm is superior to the binary time-frequency masking algorithm and the soft time-frequency masking algorithm in terms of signal-to-distortion ratio（SDR） and perceptual evaluation of speech quality（PESQ）.