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Mar. 2021
Submitted Papers : 80
Accepted Papers : 10
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Paper Published : 1276
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  Journal Paper

Paper Title
A Signal Separation Method Based on Sparsity Estimation of Source Signals and Non-negative Matrix Factorization

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.


Author - Siyeon Nam, Serin Hong, Deokgyu Yun, Seung Ho Choi

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| Published on 2018-04-12
   
   
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