In this article, we are interested in the problem of blind source separation (BSS) for the robot audition, we study the performance of blind source separation with a varying number of sensors in a microphone array placed in the head of an infant size dummy. We propose a two stage blind source separation algorithm based on a fixed beamforming preprocessing using the head related transfer functions (HRTF) of the dummy and a separation algorithm using a sparsity criterion. We show that in the case of robot audition, the use of a multisensor array improves significantly the performance of the source separation algorithm, as compared to the binaural case, up to a limit number of microphones studied in this paper.

In this paper, we introduce a modified lp norm blind source separation criterion based on the source sparsity in the time-frequency domain. We study the effect of making the sparsity constraint harder through the optimization process, making the parameter p of the lp norm vary from 1 to nearly 0 according to a sigmoid function. The sigmoid introduces a smooth lp norm variation which avoids the divergence of the algorithm. We compared this algorithm to the regular l1 norm minimization and an ICA based one and we obtained promising results.