An Unsupervised Approach to Close-Talk Speech Enhancement
2014 ◽
Vol 614
◽
pp. 363-366
Keyword(s):
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.
2012 ◽
Vol 229-231
◽
pp. 1738-1741
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Keyword(s):
1995 ◽
pp. 503-508
◽
1999 ◽
Vol 106
(4)
◽
pp. 2238-2238
2018 ◽
Vol 52
(6)
◽
pp. 561-571