Abstract
A K-means based unsupervised approach to close-talk speech enhancement is proposed in this paper. With the frame work of computational auditory scene analysis (CASA), the dual-microphone energy difference (DMED) is used as the cue to classify the noise domain time-frequency (T-F) units and target speech domain units. A ratio mask is used to separate the target speech and noise. Experiment results show the robust performance of the proposed algorithm than the Wiener filtering algorithm.
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