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

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.