Time-frequency clustering with weighted and contextual information for convolutive blind source separation
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 separationmasks; however, these approaches...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers ( IEEE )
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/28674 |
| _version_ | 1848752600125538304 |
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| author | Jafari, I. Atcheson, M. Togneri, R. Nordholm, Sven |
| author2 | Sergios Theodoridis |
| author_facet | Sergios Theodoridis Jafari, I. Atcheson, M. Togneri, R. Nordholm, Sven |
| author_sort | Jafari, I. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 separationmasks; however, these approaches generally disregard the structured nature of speech signals. Motivated by the homogenous behaviour of speech signals, we propose to modify the established fuzzy cmeans 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. |
| first_indexed | 2025-11-14T08:11:12Z |
| format | Conference Paper |
| id | curtin-20.500.11937-28674 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:11:12Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers ( IEEE ) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-286742017-09-13T15:15:33Z Time-frequency clustering with weighted and contextual information for convolutive blind source separation Jafari, I. Atcheson, M. Togneri, R. Nordholm, Sven Sergios Theodoridis fuzzy c-means clustering time-frequency masking observation weights blind source separation contextual information 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 separationmasks; however, these approaches generally disregard the structured nature of speech signals. Motivated by the homogenous behaviour of speech signals, we propose to modify the established fuzzy cmeans 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. 2014 Conference Paper http://hdl.handle.net/20.500.11937/28674 10.1109/SSP.2014.6884599 Institute of Electrical and Electronics Engineers ( IEEE ) restricted |
| spellingShingle | fuzzy c-means clustering time-frequency masking observation weights blind source separation contextual information Jafari, I. Atcheson, M. Togneri, R. Nordholm, Sven Time-frequency clustering with weighted and contextual information for convolutive blind source separation |
| title | Time-frequency clustering with weighted and contextual information for convolutive blind source separation |
| title_full | Time-frequency clustering with weighted and contextual information for convolutive blind source separation |
| title_fullStr | Time-frequency clustering with weighted and contextual information for convolutive blind source separation |
| title_full_unstemmed | Time-frequency clustering with weighted and contextual information for convolutive blind source separation |
| title_short | Time-frequency clustering with weighted and contextual information for convolutive blind source separation |
| title_sort | time-frequency clustering with weighted and contextual information for convolutive blind source separation |
| topic | fuzzy c-means clustering time-frequency masking observation weights blind source separation contextual information |
| url | http://hdl.handle.net/20.500.11937/28674 |