Robotic localization and separation of concurrent sound sources using self-splitting competitive learning

We combine binaural sound-source localization and separation techniques for an effective deployment in humanoid-like robotic hearing systems. Relying on the concept of binaural hearing, where the human auditory 3D percepts are predominantly formed on the basis of the sound-pressure signals at the two eardrums, our robotic 3D localization system uses only two microphones placed inside the ear canals of a robot head equipped with artificial ears and mounted on a torso. The proposed localization algorithm exploits all the binaural cues encapsulated within the so-called Head Related Transfer Functions (HRTFs). Taking advantage of the sparse representations of the ear input signals, the 3D positions of two concurrent sound sources is extracted. The location of the sources is extracted after identifying which HRTFs they have been filtered with using a well-known self-splitting competitive learning clustering algorithm. Once the location of the sources are identified, they are separated using a generic HRTF dataset. Simulation results demonstrated highly accurate 3D localization of the two concurrent sound sources, and a very high Signal-to-Interferenee Ratio (SIR) for the separated sound signals.