NDLI: Dictionary learning based on M-PCA-N for audio signal sparse representation

Content Provider	IET Digital Library
Author	Yang, Jichen He, Qianhua Li, Yanxiong Liu, Leian Li, Jianhong Feng, Xiaohui
Abstract	The current popular dictionary learning algorithms for sparse representation of signals are K-means Singular Value Decomposition (K-SVD) and K-SVD-extended. Only rank-1 approximation is used to update one atom at a time and it is unable to cope with large dictionary efficiently. In order to tackle these two problems, this study proposes M-Principal Component Analysis-N (M-PCA-N), which is an algorithm for dictionary learning and sparse representation. First, M-Principal Component Analysis (M-PCA) utilised information from the top M ranks of SVD decomposition to update M atoms at a time. Then, in order to further utilise the information from remaining ranks, M-PCA-N is proposed on the basis of M-PCA, by transforming information from the following N non-principal ranks onto the top M principal ranks. The mathematic formula indicates that M-PCA may be seen as a generalisation of K-SVD. Experimental results on the BBC Sound Effects Library show that M-PCA-N not only lowers the MSE between original signal and approximation signal in audio signal sparse representation, but also obtains higher audio signal classification precision than K-SVD.
Starting Page	198
Ending Page	206
Page Count	9
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 2, Apr (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2015.0277
Journal	IET Signal Processing
Publisher Date	2017-09-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Audio Signal Classification Precision Audio Signal Processing Audio Signal Sparse Representation Dictionary Learning K-SVD Generalisation K-SVD-extended M-PCA-N Nonprincipal Ranks Principal Ranks Rank-1 Approximation Signal Classification Signal Representation Singular Value Decomposition Speech And Audio Signal Processing SVD Decomposition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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