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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jafari, I. Atcheson, M. Togneri, R. Nordholm, S. |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of EEC Eng., Univ. of Western Australia, Crawley, WA, Australia (Jafari, I.; Atcheson, M.; Togneri, R.) || Dept. of EC Eng., Curtin Univ. of Technol., Perth, WA, Australia (Nordholm, S.) |
| Abstract | 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 techniques to estimate time-frequency separation masks; however, these approaches generally disregard the structured nature of speech signals. Motivated by the homogenous behavior of speech signals, we propose to modify the established fuzzy c-means algorithm to bias the clustering results in favor of cluster membership homogeneity within localized neighborhoods in the time-frequency space. This problem can be solved by using a two-stage algorithm: firstly, the estimation of data weights to indicate the reliability of each data point, and secondly, the integration of local contextual information into the cluster update equations from neighboring time-frequency slots. The proposed algorithm is evaluated in a three-fold manner using simulated, real recordings and public benchmark data; notable improvement in source separation performance over previous clustering approaches was achieved. |
| Starting Page | 157 |
| Ending Page | 160 |
| File Size | 260188 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479949755 |
| DOI | 10.1109/SSP.2014.6884599 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-29 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Time-frequency analysis Speech Signal processing algorithms Reverberation Microphones Blind source separation time-frequency masking blind source separation fuzzy c-means clustering observation weights contextual information |
| Content Type | Text |
| Resource Type | Article |
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