Adaptive blind moving source separation based on intensity vector statistics

Abstract

This paper presents a novel approach to blind moving source separation by detecting, tracking and separating speakers in real-time using intensity vector direction (IVD) statistics. It updates unmixing system parameters swiftly in order to deal with the time-variant mixing parameters. Denoising is carried out to extract reliable speaker estimates using von-Mises modeling of the IVD measurements in space and IIR filtering of the IVD distribution in time. Peaks in the IVD distribution are assigned location expectation values to check for consistency, and consequently high location expectation peaks are declared as active speakers. The location expectation algorithm caters for natural pauses during speech delivery. Speaker movements are tracked by spatial isolation of the detected peaks using time-variant regions of interest. As a result, the proposed moving source separation system is capable of blindly detecting, tracking and separating moving speakers. A real-time demonstration has been developed with the proposed system pipeline, allowing users to listen to active speakers in any desired combination. The system has an advantage of using a small coincident microphone array to separate any number of moving sources utilising the first order Ambisonics signals while assuming source signals to be W-disjoint orthogonal. Being nearly closed-form, the proposed system does not require convergence or initialization of parameters.