Real-time Acoustic Source Separation Based on Kalman Filter

Deterministic techniques are based on the source-directions and multipath characteristics of the reverberant environment for different source signals. However, searching for the desired directions of the time-block sequence of an acoustic signal is time consuming, and existing deterministic methods rarely consider the motion properties of the acoustic source. In this paper, a real-time acoustic source separation based on a Kalman filter is proposed. First, the convolutive mixture signals captured by the coincident array geometry are formulated, and the basic principles of acoustic source separation based on intensity vector statistics are introduced. Then, a dynamic source-direction prediction method for real-time blind source separation based on a Kalman filter is proposed to predict the directions of a time sequential signal. Finally, extensive experiments are performed with three-source convolutive mixtures of speech in English and Chinese, whose direction varies in linear and nonlinear motions. The signal-to-distortion and signal-to-interference of the separated signals are calculated, and the experimental results demonstrate the feasibility and validity of the proposed method.