Blind Source Separation for Robot Audition using Fixed Beamforming with HRTFs

Mounira Maazaoui (1) , Yves Grenier (1) , Karim Abed-Meraim (1)

1 TSI - Département Traitement du Signal et des Images

We present a two stage blind source separation (BSS) algorithm for robot audition. The algorithm is based on a beamforming preprocessing and a BSS algorithm using a sparsity separation criterion. Before the BSS step, we filter the sensors outputs by beamforming filters to reduce the reverberation and the environmental noise. As we are in a robot audition context, the manifold of the sensor array in this case is hard to model, so we use pre-measured Head Related Transfer Functions (HRTFs) to estimate the beamforming filters. In this article, we show the good performance of this method as compared to a single stage BSS only method.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Mounira Maazaoui, Yves Grenier, Karim Abed-Meraim. Blind Source Separation for Robot Audition using Fixed Beamforming with HRTFs. 12th Annual Conference of the International Speech Communication Association (Interspeech-2011), Sep 2011, Florence, Italy. ⟨hal-00683452⟩