On the use of contextual time frequency information for full-band clustering-based convolutive blind source separation
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In this paper we propose to incorporate contextual time frequency information for clustering-based blind source separation. Previous clustering-based approaches have successfully used clustering techniques to estimate time-frequency separation masks; however, these approaches generally do not consider the contextual information of each time-frequency slot. Motivated by the homogenous behavior of speech signals, we modify the fuzzy c-means clustering to bias the results in favor of cluster membership homogeneity within localized neighborhoods in the time-frequency space. Experimental evaluations in both simulated and real-world underdetermined environments demonstrate improvement in source separation performance over previous clustering approaches.
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Time-frequency clustering with weighted and contextual information for convolutive blind source separationJafari, I.; Atcheson, M.; Togneri, R.; Nordholm, Sven (2014)In this paper we investigate the use of observation weights and contextual time-frequency information for clustering-based blind source separation. Previous clustering-based approaches have successfully used clustering ...
A novel fuzzy clustering algorithm using observation weighting and context information for reverberant blind speech separationKuhne, M.; Togneri, R.; Nordholm, Sven (2009)Time-frequency masking has evolved as a powerful tool for tackling blind source separation problems. In previous work, mask estimation was performed with the help of well-known standard cluster algorithms. Spatial observation ...
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