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

Joshua Hollick, I. Jafari, Roberto Togneri, S.E. Nordholm

Research output: Chapter in Book/Conference paperConference paper

2 Citations (Scopus)

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. © 2014 IEEE.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages7450-7454
VolumeNA
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014 - Italy, Florence, Italy
Duration: 4 May 20149 May 2014
Conference number: 106632

Conference

Conference2014 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014
Abbreviated titleICASSP 2014
CountryItaly
CityFlorence
Period4/05/149/05/14

Fingerprint

Microphones
Clustering algorithms

Cite this

Hollick, J., Jafari, I., Togneri, R., & Nordholm, S. E. (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 (Vol. NA, pp. 7450-7454). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2014.6855048
Hollick, Joshua ; Jafari, I. ; Togneri, Roberto ; Nordholm, S.E. / Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering. IEEE International Conference on Acoustics, Speech and Signal Processing. Vol. NA USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. pp. 7450-7454
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Hollick, J, Jafari, I, Togneri, R & Nordholm, SE 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. vol. NA, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 7450-7454, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014, Florence, Italy, 4/05/14. https://doi.org/10.1109/ICASSP.2014.6855048

Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering. / Hollick, Joshua; Jafari, I.; Togneri, Roberto; Nordholm, S.E.

IEEE International Conference on Acoustics, Speech and Signal Processing. Vol. NA USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 7450-7454.

Research output: Chapter in Book/Conference paperConference paper

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Hollick J, Jafari I, Togneri R, Nordholm SE. 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. Vol. NA. USA: IEEE, Institute of Electrical and Electronics Engineers. 2014. p. 7450-7454 https://doi.org/10.1109/ICASSP.2014.6855048