On the use of the Watson mixture model for clustering-based under-determined blind source separation
In this paper, we investigate the application of a generative clustering technique for the estimation of time-frequency source separation masks. Recent advances in time-frequency clustering-based approaches to blind source separation have touched upon the Watson mixture model (WMM) as a tool for sou...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/26988 |
| _version_ | 1848752139541676032 |
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| author | Jafari, I. Togneri, R. Nordholm, Sven |
| author_facet | Jafari, I. Togneri, R. Nordholm, Sven |
| author_sort | Jafari, I. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we investigate the application of a generative clustering technique for the estimation of time-frequency source separation masks. Recent advances in time-frequency clustering-based approaches to blind source separation have touched upon the Watson mixture model (WMM) as a tool for source separation. However, most methods have been frequency bin-wise and have thus required the additional permutation alignment stage, and previous full-band methods which employ the WMM have yet to be applied to the under-determined setting. We propose to evaluate the clustering ability of the WMM within the clustering-based source separation framework. Evaluations confirm the superiority of the WMM against other previously used clustering techniques such as the fuzzy c-means. |
| first_indexed | 2025-11-14T08:03:52Z |
| format | Conference Paper |
| id | curtin-20.500.11937-26988 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:03:52Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-269882017-01-30T12:56:20Z On the use of the Watson mixture model for clustering-based under-determined blind source separation Jafari, I. Togneri, R. Nordholm, Sven In this paper, we investigate the application of a generative clustering technique for the estimation of time-frequency source separation masks. Recent advances in time-frequency clustering-based approaches to blind source separation have touched upon the Watson mixture model (WMM) as a tool for source separation. However, most methods have been frequency bin-wise and have thus required the additional permutation alignment stage, and previous full-band methods which employ the WMM have yet to be applied to the under-determined setting. We propose to evaluate the clustering ability of the WMM within the clustering-based source separation framework. Evaluations confirm the superiority of the WMM against other previously used clustering techniques such as the fuzzy c-means. 2014 Conference Paper http://hdl.handle.net/20.500.11937/26988 restricted |
| spellingShingle | Jafari, I. Togneri, R. Nordholm, Sven On the use of the Watson mixture model for clustering-based under-determined blind source separation |
| title | On the use of the Watson mixture model for clustering-based under-determined blind source separation |
| title_full | On the use of the Watson mixture model for clustering-based under-determined blind source separation |
| title_fullStr | On the use of the Watson mixture model for clustering-based under-determined blind source separation |
| title_full_unstemmed | On the use of the Watson mixture model for clustering-based under-determined blind source separation |
| title_short | On the use of the Watson mixture model for clustering-based under-determined blind source separation |
| title_sort | on the use of the watson mixture model for clustering-based under-determined blind source separation |
| url | http://hdl.handle.net/20.500.11937/26988 |