%0 Journal Article %A Yueming Wang and Zenghui Zhang and Rendong Ying and Peilin Liu %D 2013 %J International Journal of Electronics and Communication Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 80, 2013 %T A Dictionary Learning Method Based On EMD for Audio Sparse Representation %U https://publications.waset.org/pdf/16628 %V 80 %X Sparse representation has long been studied and several dictionary learning methods have been proposed. The dictionary learning methods are widely used because they are adaptive. In this paper, a new dictionary learning method for audio is proposed. Signals are at first decomposed into different degrees of Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD) technique. Then these IMFs form a learned dictionary. To reduce the size of the dictionary, the K-means method is applied to the dictionary to generate a K-EMD dictionary. Compared to K-SVD algorithm, the K-EMD dictionary decomposes audio signals into structured components, thus the sparsity of the representation is increased by 34.4% and the SNR of the recovered audio signals is increased by 20.9%. %P 961 - 966