Title :
Adaptive Compressive Sensing of Speech Signals Based on Empirical Mode Decomposition
Author :
Shi-kui Wang ; Yu-feng Shao
Author_Institution :
Coll. of Electron. & Inf. Eng., Chongqing Three Gorges Univ., Chongqing, China
Abstract :
In this paper, an adaptive compressive sensing of speech signals is presented based on empirical mode decomposition (EMD). According to the different numbers of intrinsic mode functions (IMFs) produced by EMD, the speech signals is adaptively compressive sampled in the source and then adaptively reconstructed in the receiver. The three key technologies, that is, empirical mode decomposition, construction of adaptive dictionaries and adaptive reconstruction based on Bregman algorithm are discussed.
Keywords :
adaptive signal processing; compressed sensing; iterative methods; receivers; signal reconstruction; signal sampling; speech processing; Bregman algorithm; EMD; IMF; adaptive dictionaries construction; adaptive reconstruction; empirical mode decomposition; intrinsic mode function; receiver; speech signal adaptive compressive sensing; Compressed sensing; Dictionaries; Discrete cosine transforms; Empirical mode decomposition; Sparse matrices; Speech; Speech enhancement; Bregman algorithm; EMD; IMF; adaptive; adaptive reconstruction; compressive sensing;
Conference_Titel :
Wireless Communication and Sensor Network (WCSN), 2014 International Conference on
Print_ISBN :
978-1-4799-7090-2
DOI :
10.1109/WCSN.2014.96
