  Identification and localization of sounds are both integral parts of
computational auditory scene analysis. Although each can be solved separately,
the goal of forming coherent auditory objects and achieving a comprehensive
spatial scene understanding suggests pursuing a joint solution of the two
problems. This work presents an approach that robustly binds localization with
the detection of sound events in a binaural robotic system. Both tasks are
joined through the use of spatial stream segregation which produces
probabilistic time-frequency masks for individual sources attributable to
separate locations, enabling segregated sound event detection operating on
these streams. We use simulations of a comprehensive suite of test scenes with
multiple co-occurring sound sources, and propose performance measures for
systematic investigation of the impact of scene complexity on this segregated
detection of sound types. Analyzing the effect of spatial scene arrangement, we
show how a robot could facilitate high performance through optimal head
rotation. Furthermore, we investigate the performance of segregated detection
given possible localization error as well as error in the estimation of number
of active sources. Our analysis demonstrates that the proposed approach is an
effective method to obtain joint sound event location and type information
under a wide range of conditions.
