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We present a new binaural sound-source separation and localization technique for the underdetermined case where the present sound sources outnumber the available microphones. The proposed technique has access to a generic set of head-related transfer functions (HRTFs) and processes input signals obtained from two small microphones placed inside the ear canals of a robot humanoid head equipped with artificial ears and mounted on a torso. By exploiting sparse representations of the ear input signals, the 3D position of three concurrent sound sources is extracted by identifying the HRTFs that have filtered the sound signals. The sought HRTFs are estimated using a well-known self-splitting competitive learning technique for clustering. Simulation results demonstrated the performance of the new technique in localizing both azimuth and elevation angles for three concurrently active sound sources. The proposed method relies purely on auditive cues and provides an easy implementation on robotic platforms.