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

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

doi:10.1109/ICASSP.2014.6855048

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

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

School of Indigenous Studies

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

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 language	English
Title of host publication	IEEE International Conference on Acoustics, Speech and Signal Processing
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	7450-7454
Volume	NA
ISBN (Print)	9781479928927
DOIs	https://doi.org/10.1109/ICASSP.2014.6855048
Publication status	Published - 2014
Event	2014 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014 - Italy, Florence, Italy Duration: 4 May 2014 → 9 May 2014 Conference number: 106632

Conference

Conference	2014 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014
Abbreviated title	ICASSP 2014
Country/Territory	Italy
City	Florence
Period	4/05/14 → 9/05/14

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10.1109/ICASSP.2014.6855048

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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). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2014.6855048

@inproceedings{2b429aebfd7a40c7bd271aaf48bcdff9,

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

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. {\textcopyright} 2014 IEEE.",

author = "Joshua Hollick and I. Jafari and Roberto Togneri and S.E. Nordholm",

year = "2014",

doi = "10.1109/ICASSP.2014.6855048",

language = "English",

isbn = "9781479928927",

volume = "NA",

pages = "7450--7454",

booktitle = "IEEE International Conference on Acoustics, Speech and Signal Processing",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2014 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",

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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 et al.
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 paper › Conference paper › peer-review

TY - GEN

T1 - Source number estimation in reverberant conditions via full-band weighted, adaptive fuzzy c-means clustering

AU - Hollick, Joshua

AU - Jafari, I.

AU - Togneri, Roberto

AU - Nordholm, S.E.

N1 - Conference code: 106632

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1109/ICASSP.2014.6855048

DO - 10.1109/ICASSP.2014.6855048

M3 - Conference paper

SN - 9781479928927

VL - NA

SP - 7450

EP - 7454

BT - IEEE International Conference on Acoustics, Speech and Signal Processing

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - USA

T2 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014

Y2 - 4 May 2014 through 9 May 2014

ER -

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

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