Journal ArticleDOI
Wavelet Transform With Tunable Q-Factor
TLDR
A discrete-time wavelet transform for which the Q-factor is easily specified and the transform can be tuned according to the oscillatory behavior of the signal to which it is applied, based on a real-valued scaling factor.Abstract:
This paper describes a discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the signal to which it is applied. The transform is based on a real-valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. Two forms of the transform are presented. The first form is defined for discrete-time signals defined on all of Z. The second form is defined for discrete-time signals of finite-length and can be implemented efficiently with FFTs. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e.g., three to four times overcomplete) being sufficient for the analysis/synthesis functions to be well localized.read more
Citations
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Detection of faults in rotating machinery using periodic time-frequency sparsity
TL;DR: The proposed algorithm, which combines the split augmented Lagrangian shrinkage algorithm with majorization–minimization algorithm, is guaranteed to converge for both convex and non-convex formulation and is compared to some state-of-the-art methods.
Journal ArticleDOI
Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques
TL;DR: A nonlinear multi-level feature generation model using cryptographic structure called shuffle box for feature generation and iterative neighborhood component analysis to select the features to achieve high classification performance in speech emotion recognition is presented.
Journal ArticleDOI
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
TL;DR: In this article , a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced, which is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset.
Journal ArticleDOI
Feature extraction method for classification of alertness and drowsiness states EEG signals
TL;DR: The non-stationary characteristic of the EEG signal is explored by tunable Q-factor wavelet transform (TQWT) and shows that the proposed features are effectively discriminative of the alertness and drowsiness states.
Posted Content
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies.
Afshin Shoeibi,Afshin Shoeibi,Navid Ghassemi,Marjane Khodatars,Parisa Moridian,Roohallah Alizadehsani,Assef Zare,Abbas Khosravi,Abdulhamit Subasi,U. Rajendra Acharya,Juan Manuel Górriz +10 more
TL;DR: In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced, which is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset.
References
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Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI
Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
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Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
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Fast Image Recovery Using Variable Splitting and Constrained Optimization
TL;DR: A new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an l2 data-fidelity term and a nonsmooth regularizer is proposed.
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Calculation of a constant Q spectral transform
TL;DR: In this article, a constant Q transform with a constant ratio of center frequency to resolution has been proposed to obtain a constant pattern in the frequency domain for sounds with harmonic frequency components.