Multi-Channel Voice Activity Detection Based on Conic Constraints

Unlike single microphone techniques for voice activity detection (VAD), multi-microphone signal processing usually exploits the spatial information of signals received at multiple microphones. In this paper, we propose a VAD algorithm based on conic constraints to achieve robustness against the direction of arrival (DOA) estimation error. The proposed algorithm uses the phase vector as feature and detects the presence of the target speech by comparing the angles between the phase vector of the multi-microphone input signal and two mean phase vectors for target speech+interference period and interference-only period. The proposed algorithm was tested with simulation data generated by real-measured impulse response for seven uniformly distributed microphones. The simulation results showed that the proposed algorithm presents a reliable VAD metric in the presence of competing speech. The results also supported the robustness of the proposed algorithm against the DOA estimation error.