Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering
We introduce a novel approach for source number estimation through an adaptive fuzzy c-means clustering. Spatial feature vectors are extracted from microphone observations, weighted for reliability and then clustered in a full-band manner using an adaptive variation on the fuzzy c-means. A number of...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/35254 |
| _version_ | 1848754446767489024 |
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| author | Hollick, J. Jafari, I. Togneri, R. Nordholm, Sven |
| author2 | ICASSP 2014 Publication Chair Maria Sabrina Greco |
| author_facet | ICASSP 2014 Publication Chair Maria Sabrina Greco Hollick, J. Jafari, I. Togneri, R. Nordholm, Sven |
| author_sort | Hollick, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We introduce a novel approach for source number estimation through an adaptive fuzzy c-means clustering. Spatial feature vectors are extracted from microphone observations, weighted for reliability and then clustered in a full-band manner using an adaptive variation on the fuzzy c-means. A number of quality measures are combined to produce a weighted sum which is used to find the optimal number of clusters at each iteration of the clustering algorithm. Experimental evaluations using real-world recordings from a reverberant room (RT60 = 390 ms) demonstrated encouraging performance in both even- and under-determined conditions. |
| first_indexed | 2025-11-14T08:40:33Z |
| format | Conference Paper |
| id | curtin-20.500.11937-35254 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:40:33Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-352542017-09-13T15:32:49Z Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering Hollick, J. Jafari, I. Togneri, R. Nordholm, Sven ICASSP 2014 Publication Chair Maria Sabrina Greco fuzzy c-means clustering source number estimation weights adaptive quality measure We introduce a novel approach for source number estimation through an adaptive fuzzy c-means clustering. Spatial feature vectors are extracted from microphone observations, weighted for reliability and then clustered in a full-band manner using an adaptive variation on the fuzzy c-means. A number of quality measures are combined to produce a weighted sum which is used to find the optimal number of clusters at each iteration of the clustering algorithm. Experimental evaluations using real-world recordings from a reverberant room (RT60 = 390 ms) demonstrated encouraging performance in both even- and under-determined conditions. 2014 Conference Paper http://hdl.handle.net/20.500.11937/35254 10.1109/ICASSP.2014.6855048 IEEE restricted |
| spellingShingle | fuzzy c-means clustering source number estimation weights adaptive quality measure Hollick, J. Jafari, I. Togneri, R. Nordholm, Sven Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering |
| title | Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering |
| title_full | Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering |
| title_fullStr | Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering |
| title_full_unstemmed | Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering |
| title_short | Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering |
| title_sort | source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering |
| topic | fuzzy c-means clustering source number estimation weights adaptive quality measure |
| url | http://hdl.handle.net/20.500.11937/35254 |