Paper Info
Title
Application Of Non-Uniform Sampling In Compressed Sensing For Speech Signal
Abstract
Currently, the most widely used Gaussian random observations in compressed sensing require that signals must be discrete, and the signal waveform must be known before observation, which greatly restricts the application of compressive sensing in speech. In response to this problem, this paper draws on the advantages of non-uniform sampling, constructs a non-uniform observation matrix, directly extracts the data from the signal waveform as observations, and gives a corresponding new method of reconstruction. The theoretical analysis and simulation results show that non-uniform observation can directly apply compressed sensing to analog speech signal processing, and the corresponding reconstruction method effectively enriches the means of compressive perception reconstruction.
Year
DOI
Venue
2018
10.1007/978-3-319-95930-6_38
INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT I
Keywords
Field
DocType
Compressed sensing, Non-uniform sampling, Speech signal
Signal processing,Pattern recognition,Computer science,Waveform,Gaussian,Artificial intelligence,Sampling (statistics),Observation matrix,Compressed sensing,Nonuniform sampling
Conference
Volume
ISSN
Citations 
10954
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Changqing Zhang173036.91
Gang Min201.01
Huan Ma392.53
xian li4448.51