A Novel Overcomplete Dictionary Training Based on Empirical Mode Decomposition and Its Performance Analysis

DOI

10.2991/isci-15.2015.69How to use a DOI?

Keywords

overcomplete dictionary; EMD; sparse representation; K-SVD algorithm; DCT

Abstract

In this paper, a novel overcomplete dictionary training method which is based on empirical mode decomposition is presented. The IMFs by empirical mode decomposition take part in the training of overcomplete dictionary, and K-SVD algorithm is adopted in the training process. Simulation results show that, compared with the dictionary trained directly from the original speech signals, the overcomplete dictionary has sparser representation for the speech signals, and thus has higher reconstructed speech quality.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shikui Wang
AU  - Yufeng Shao
PY  - 2015/01
DA  - 2015/01
TI  - A Novel Overcomplete Dictionary Training Based on Empirical Mode Decomposition and Its Performance Analysis
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 515
EP  - 522
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.69
DO  - 10.2991/isci-15.2015.69
ID  - Wang2015/01
ER  -