Paper Title :A Signal Separation Method Based on Sparsity Estimation of Source Signals and Non-negative Matrix Factorization
Author :Siyeon Nam, Serin Hong, Deokgyu Yun, Seung Ho Choi
Article Citation :Siyeon Nam ,Serin Hong ,Deokgyu Yun ,Seung Ho Choi ,
(2018 ) " A Signal Separation Method Based on Sparsity Estimation of Source Signals and Non-negative Matrix Factorization " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 53-54,
Volume-6, Issue-2
Abstract : In order to improve the signal separation performance of the non-negative matrix factorization (NMF), sparse
non-negative matrix factorization (Sparse NMF, SNMF) was developed. Existing SNMF algorithm uses arbitrarily
determined sparseness without considering the sparseness of individual sound sources. In this paper, we propose a new
signal separation method that estimates the sparseness according to the characteristics of a sound source and applies it to
SNMF algorithm. Experimental results show that the proposed method has better performance than the existing NMF and
SNMF.
Keywords - Signal separation, Non-negative matrix factorization, Sparseness, Denoising.
Type : Research paper
Published : Volume-6, Issue-2
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-11212
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Copyright: © Institute of Research and Journals
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Published on 2018-04-12 |
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