Applications in Time-Frequency Signal Processing
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acoustic echo detection
advanced time-frequency application guide
Alan R. Lindsey
Average Local Variances
Bilinear Distributions
Binomial Kernel
biomedical signal modeling
Boashash Boualem
Category=UYS
Dale Groutage
David Bennink
Dominant Spectral Peak
E. Chassande-Mottin
EEG Data
EEG Seizure
EEG Signal
Energy Density
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eq_computing
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eq_isMigrated=2
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F. Auger
Fourier transforms
Franz Hlawatsch
geophysical data processing
Gerald Matz
Gold Code
Gps Signal
Instantaneous Frequency
instantaneous frequency estimation
James Droppo
Jan Erik Odegard
Leon Cohen
Les Atlas
Liang Zhao
Linear FM
Mesbah Mostefa
MFCC Feature
Moeness Amin
Narrowband Spectrogram
Neonatal EEG
nonstationary signal analysis
P. Flandrin
Patrick Loughlin
PN Sequence
Positive Distribution
Richard G. Baraniuk
Seismic Cross Section
Seismic Sequence
Seizure Detection
signal analysis
spectrogram
speech recognition algorithms
Steeghs Philippe
TF Analysis
TF Distribution
TF Plane
TF Representation
time-frequency signal processing
Wigner Distribution
William J. Williams
Product details
- ISBN 9780849300653
- Weight: 758g
- Dimensions: 156 x 234mm
- Publication Date: 29 Oct 2002
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Because most real-world signals, including speech, sonar, communication, and biological signals, are non-stationary, traditional signal analysis tools such as Fourier transforms are of limited use because they do not provide easily accessible information about the localization of a given frequency component. A more suitable approach for those studying non-stationary signals is the use of time frequency representations that are functions of both time and frequency.
Applications in Time-Frequency Signal Processing investigates the use of various time-frequency representations, such as the Wigner distribution and the spectrogram, in diverse application areas. Other books tend to focus on theoretical development. This book differs by highlighting particular applications of time-frequency representations and demonstrating how to use them. It also provides pseudo-code of the computational algorithms for these representations so that you can apply them to your own specific problems.
Written by leaders in the field, this book offers the opportunity to learn from experts. Time-Frequency Representation (TFR) algorithms are simplified, enabling you to understand the complex theories behind TFRs and easily implement them. The numerous examples and figures, review of concepts, and extensive references allow for easy learning and application of the various time-frequency representations.
