A robust approach to reverberant blind source separation in the presence of noise for arbitrarily arranged sensors
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Considerable attention has been devoted to the reverberant blind source separation problem: in particular, the concept of time-frequency masking. However, realistic acoustic scenarios often comprise not only reverberation, but also additive noise due to factors such as non-ideal channels. This paper presents robust evaluations of a time-frequency masking approach for separation in such realistic conditions. The fuzzy c-means clustering algorithm is used to cluster spatial feature cues into a time-frequency mask. Experimental results demonstrated superiority in separation, with notable improvements in the SNR additionally observed. Not only does this establish the proposed scheme viable for reverberant blind source separation, but also as a credible means of speech enhancement in the presence of additive broadband noise.
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