Time-frequency clustering with weighted and contextual information for convolutive blind source separation

I. Jafari, M. Atcheson, Roberto Togneri, S.E. Nordholm

Research output: Chapter in Book/Conference paperConference paper

Abstract

In this paper we investigate the use of observation weights and contextual time-frequency information for clustering-based blind source separation. Previous clustering-based approaches have successfully used clustering techniques to estimate time-frequency separation masks; however, these approaches generally disregard the structured nature of speech signals. Motivated by the homogenous behavior of speech signals, we propose to modify the established fuzzy c-means algorithm to bias the clustering results in favor of cluster membership homogeneity within localized neighborhoods in the time-frequency space. This problem can be solved by using a two-stage algorithm: firstly, the estimation of data weights to indicate the reliability of each data point, and secondly, the integration of local contextual information into the cluster update equations from neighboring time-frequency slots. The proposed algorithm is evaluated in a three-fold manner using simulated, real recordings and public benchmark data; notable improvement in source separation performance over previous clustering approaches was achieved. © 2014 IEEE.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing
Place of PublicationFlorence, Italy
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages157-160
ISBN (Print)9781479949755
DOIs
Publication statusPublished - 2014
Event2014 IEEE Workshop on Statistical Signal Processing - Gold Coast, Australia
Duration: 29 Jun 20142 Jul 2014

Conference

Conference2014 IEEE Workshop on Statistical Signal Processing
CountryAustralia
CityGold Coast
Period29/06/142/07/14

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