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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Many speech source separation approaches are based on the assumption of orthogonality of speech sources in the time-frequency domain. The target speech source is demixed from the mixture by applying the ideal binary mask to the mixture. The time-frequency orthogonality of speech sources is investigated in detail only for anechoic and artificially mixed speech mixtures. This paper evaluates how the orthogonality of speech sources decreases when using a realistic reverberant humanoid recording setup and indicates strategies to enhance the separation capabilities of algorithms based on ideal binary masks under these conditions. It is shown that the SIR of the target source demixed from the mixture using the ideal binary mask decreases by approximately 3 dB for reverberation times of T60 = 0.6 s opposed to the anechoic scenario. For humanoid setups, the spatial distribution of the sources and the choice of the correct ear channel introduces differences in the SIR of further 3 dB, which leads to specific strategies to choose the best channel for demixing.

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