A novel fuzzy clustering algorithm using observation weighting and context information for reverberant blind speech separation

DOI10.1016/j.sigpro.2009.08.005zbMath1177.94060OpenAlexW2022767789MaRDI QIDQ1048867

Sven Nordholm, Marco Kühne, Roberto Togneri

Publication date: 8 January 2010

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/20.500.11937/44787

zbMATH Keywords

robustnessblind source separationfuzzy clusteringreverberationadaptive beamformingtime-frequency masking

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (1)

Relative entropy fuzzy \(c\)-means clustering

Uses Software

• AS 136
• BSS Eval
• Soft-LOST
• DARPA TIMIT

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• Underdetermined blind source separation in echoic environments using DESPRIT
• A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources
• Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors
• A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
• Spatial Models for Fuzzy Clustering
• Robust automatic speech recognition with missing and unreliable acoustic data
• Blind Separation of Speech Mixtures via Time-Frequency Masking
• Independent Component Analysis and Blind Signal Separation
• Independent Component Analysis and Blind Signal Separation

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