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

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