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Abstract
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Article Information:
Blind Audio Source Separation with Sparse Nonnegative Matrix Factorization
Abd Majid Darsono, N.Z. Haron, Shakir Saat, M.M. Ibrahim and N.A. Manap
Corresponding Author: Abd Majid Darsono
Submitted: February 14, 2014
Accepted: April 17, 2014
Published: June 20, 2014 |
Abstract:
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In this study, a new technique in source separation using Two-Dimensional Nonnegative Matrix Factorization (NMF2D) with the Beta-divergence is proposed. The Time-Frequency (TF) profile of each source is modeled as two-dimensional convolution of the temporal code and the spectral basis. In addition, adaptive sparsity constraint was imposed to reduce the ambiguity and provide uniqueness to the solution. 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. Experimental tests have been conducted in audio application to blindly separate the source in musical mixture. Results have shown the effectiveness of the algorithm in separating the audio sources from single channel mixture.
Key words: Beta divergence, blind audio source separation, machine learning, nonnegative matrix factorization, , ,
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Cite this Reference:
Abd Majid Darsono, N.Z. Haron, Shakir Saat, M.M. Ibrahim and N.A. Manap, . Blind Audio Source Separation with Sparse Nonnegative Matrix Factorization. Research Journal of Applied Sciences, Engineering and Technology, (23): 5015-5020.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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