Audio source separation by activity probability detection with maximum correlation and simplex geometry

Two novel methods for speaker separation of multi-microphone recordings that can also detect speakers with infrequent activity are presented. The proposed methods are based on a statistical model of the probability of activity of the speakers across time. Each method takes a different approach for estimating the activity probabilities. The first method is derived using a linear programming (LP) problem for maximizing the correlation function between different time frames. It is shown that the obtained maxima correspond to frames which contain a single active speaker. Accordingly, we propose an algorithm for successive identification of frames dominated by each speaker. The second method aggregates the correlation values associated with each frame in a correlation vector. We show that these correlation vectors lie in a simplex with vertices that correspond to frames dominated by one of the speakers. In this method, we utilize convex geometry tools to sequentially detect the simplex vertices. The correlation functions associated with single-speaker frames, which are detected by either of the two proposed methods, are used for recovering the activity probabilities. A spatial mask is estimated based on the recovered probabilities and is utilized for separation and enhancement by means of both spatial and spectral processing. Experimental results demonstrate the performance of the proposed methods in various conditions on real-life recordings with different reverberation and noise levels, outperforming a state-of-the-art separation method.