Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering

Hollick, J.; Jafari, I.; Togneri, R.; Nordholm, Sven

doi:10.1109/ICASSP.2014.6855048

Access Status

Fulltext not available

Authors

Hollick, J.

Jafari, I.

Togneri, R.

Nordholm, Sven

Date

2014

Type

Conference Paper

Metadata

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Citation

Hollick, J. and Jafari, I. and Togneri, R. and Nordholm, S. 2014. Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 4 2014. Florence, Italy: IEEE.

Source Title

Acoustics, Speech and Signal Processing (ICASSP)

Source Conference

2014 IEEE International Conference on Acoustics, Speech, andSignal Processing (ICASSP)

DOI

10.1109/ICASSP.2014.6855048

School

Department of Electrical and Computer Engineering

URI

http://hdl.handle.net/20.500.11937/35254

Collection

Curtin Research Publications

Abstract

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.