Open AccessUnsupervised Single Channel Source Separation with Nonnegative Matrix Factorization
Author(s) -
A. M. Darsono,
Shakir Saat,
Nik Mohd Zarifie Hashim,
A. A. M. Isa
Publication year - 2015
Language(s) - English
Resource type - Conference proceedings
DOI - 10.15849/icit.2015.0073
Subject(s) - non negative matrix factorization , convolution (computer science) , source separation , multiplicative function , matrix decomposition , computer science , channel (broadcasting) , algorithm , basis (linear algebra) , matrix (chemical analysis) , divergence (linguistics) , blind signal separation , source code , factorization , mixing (physics) , pattern recognition (psychology) , mathematics , artificial intelligence , telecommunications , artificial neural network , quantum mechanics , eigenvalues and eigenvectors , physics , mathematical analysis , linguistics , geometry , materials science , philosophy , composite material , operating system
In this paper, a novel single channel source separation using two-dimensional nonnegative matrix factorization (NMF2D) is proposed. In NMF2D, the time-frequency (TF) profile of each source is modeled as two-dimensional convolution of the temporal code and the spectral basis. The proposed model used Beta-divergence as a cost function and updated by maximizing the joint probability of the mixing spectral basis and temporal codes using the multiplicative update rules. Results have concretely shown the effectiveness of the algorithm in blindly separating the audio sources from single channel mixture. KeywordsBlind Source Separation; Nonnegative Matrix Factorization ; Machine Learning; Beta Divergence.
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