This paper proposes a framework for separating several speech sources in non-ideal, reverberant environments. A movable human dummy head residing in a normal office room is used to model the conditions humans experience when listening to complex auditory scenes. Before the source separation takes place the human dummy head explores the auditory scene and extracts characteristics the same way as humans would do, when entering a new auditory scene. These extracted features are used to support several source separation algorithms that are carried out in parallel. Each of these algorithms estimates a binary time-frequency mask to separate the sources. A combination stage infers a final estimate of the binary mask to demix the source of interest. The presented results show good separation capabilities in auditory scenes consisting of several speech sources.

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