Title :
Sparseness Measure of Signal
Author :
He, Zhaoshui ; Xie, Shengli ; Fu, Yuli
Author_Institution :
Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou
Abstract :
In this paper generalized Gaussian distribution is employed to discuss sparseness measure for signals. At first we established a mathematical formula to calculate the sparseness measure of signals. According to this measure formula, the sparseness measure value of the Laplacian signal is 1, and Gaussian signal is 2. Given a signal, from its sparseness measure value, by reference to Laplacian signal and Gaussian signal, we can very intuitively know how sparse it is. Two examples are given to illustrate the fact that, only when the source signals are sparse enough, we can achieve undetermined BSS by sparse representation
Keywords :
Gaussian distribution; blind source separation; Gaussian distribution; Gaussian signal; Laplacian signal; blind source separation; signal sparseness measure; Acoustic imaging; Acoustic signal processing; Gaussian distribution; Helium; Independent component analysis; Inverse problems; Laplace equations; Noise reduction; Signal processing; Source separation;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1615002
