Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A wavelet-based tool for studying non-periodicity
Computers & Mathematics with Applications
Implementation of SYMLET wavelets to removal of Gaussian additive noise from speech signal
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
Wavelet-based multifractal analysis of 1-D and 2-D signals: New results
Analog Integrated Circuits and Signal Processing
Chaos for speech coding and production
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Some notes on nonlinearities of speech
Nonlinear Speech Modeling and Applications
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In recent studies, the chaotic behavior of a signal is confirmed using scalogram analysis of continuous-wavelet transform. Chaotic component of a speech signal can be verified through scalogram analysis, since, it investigates the periodicity content of a signal. The periodicity analysis helps in proving that a signal is not periodic, which is an essential condition on chaotic activity. In this work, a scale-index based on scalogram-analysis is calculated for a set of recordings of Arabic vowels. Also, Largest-Lyapunov Exponents (LLE) are computed for these recordings. The obtained measures are, then, compared. The comparison proves the efficacy of scale index for confirming chaotic behavior even for highly-periodic waveforms which is the case in speech vowels. Additionally, it is noted that both LLE and scale-index exhibit classification ability for Arabic vowels.