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 |
| Summary: | 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. |
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