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

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