New sampling formulae related to linear canonical transform
Signal Processing
Uncertainty principles for linear canonical transform
IEEE Transactions on Signal Processing
On amplitude and frequency demodulation using energy operators
IEEE Transactions on Signal Processing
Eigenfunctions of linear canonical transform
IEEE Transactions on Signal Processing
Discrete Generalized Fresnel Functions and Transforms in an Arbitrary Discrete Basis
IEEE Transactions on Signal Processing
The fractional Fourier transform and time-frequency representations
IEEE Transactions on Signal Processing
Tracking model of an adaptive lattice filter for a linear chirp FMsignal in noise
IEEE Transactions on Signal Processing
Uncertainty Principle for Real Signals in the Linear Canonical Transform Domains
IEEE Transactions on Signal Processing - Part I
AM-FM energy detection and separation in noise using multibandenergy operators
IEEE Transactions on Signal Processing
Digital Computation of Linear Canonical Transforms
IEEE Transactions on Signal Processing
Estimation of amplitude and phase parameters of multicomponentsignals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Multicomponent AM–FM Representations: An Asymptotically Exact Approach
IEEE Transactions on Audio, Speech, and Language Processing
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As is well known, speech signal processing is one of the hottest signal processing directions. There are exist lots of speech signal models, such as speech sinusoidal model, straight speech model, AM-FM model, gaussian mixture model and so on. This paper investigates AM-FM speech model by the linear canonical transform (LCT). The LCT can be considered as a generalization of traditional Fourier transform and fractional Fourier transform, and proved to be one of the powerful tools for non-stationary signal processing. This has opened up the possibility of a new range of potentially promising and useful applications based on the LCT. Firstly, two novel recovery methods of speech based on the AM-FM model are presented in this paper: one depends on the LCT domain filtering; the other one is based on the chirp signal parameter estimation to restore the speech signal in LCT domain. Then, experiments results are presented to verify the performance of the proposed methods. Finally, the summarization and the conclusion of the paper is given.