Adaptive blind source separation with HRTFs beamforming preprocessing and varying number of sources

Abstract : We propose an adaptive blind source separation algorithm in the context of robot audition using a microphone array. Our algorithm presents two steps: a fixed beamforming step to reduce the reverberation and the background noise and a source separation step. In the fixed beamforming preprocessing, we build the beamforming filters using the Head Related Transfer Functions (HRTFs) which allows us to take into consideration the effect of the robot’s head on the near acoustic field. In the source separation step, we use a separation algorithm based on the l1 norm minimization. We evaluate the performance of the proposed algorithm in a total adaptive way with real data and varying number of sources and show good separation and source number estimation results.