  This study describes a binaural machine hearing system that is capable of
performing auditory stream segregation in scenarios where multiple sound
sources are present. The process of stream segregation refers to the capability
of human listeners to group acoustic signals into sets of distinct auditory
streams, corresponding to individual sound sources. The proposed computational
framework mimics this ability via a probabilistic clustering scheme for joint
localization and segregation. This scheme is based on mixtures of von Mises
distributions to model the angular positions of the sound sources surrounding
the listener. The distribution parameters are estimated using block-wise
processing of auditory cues extracted from binaural signals. Additionally, the
proposed system can conduct rotational head movements to improve localization
and stream segregation performance. Evaluation of the system is conducted in
scenarios containing multiple simultaneously active speech and non-speech
sounds placed at different positions relative to the listener.
