Introduction: Time-frequency representations (TFRs) indicate variations of the signal spectral characteristics with time, and they are the natural choice as an analysis tool for the non-stationary signals [1]. The main objectives of the various types of time-frequency distribution functions (e.g. Wigner-Ville distribution (WVD) [1], short-time Fourier transform (STFT) [1], the S-transform [2]) ...

The theory of wavelets has recently undergone a period of rapid development. We introduce a software package called wavethresh that works within the statistical language S to perform oneand two-dimensional discrete wavelet transforms. The transforms and their inverses can be computed using any particular wavelet selected from a range of di erent families of wavelets. Pictures can be drawn of an...

S-transform (ST) is an effective time-frequency representation method with the advantages of short-time Fourier transform and wavelet transform. This paper utilizes the advantages of the power spectral subtraction method and the speech enhancement method to presents a novel speech enhancement algorithm based on discrete ST. Firstly, the speech in time domain is transformed by ST to joint time-f...

Inverting the Laplace transform is a paradigm for exponentially ill-posed problems. For a class of operators, including the Laplace transform, we give forward and inverse formulae that have fast implementations using the fast Fourier transform. These formulae lead easily to regularized inverses whose effects on noise and filtered data can be precisely described. Our results give cogent reasons ...

A generalized S transform based on energy normalized windows is presented, which avoids the weighting effect of existing S transform influenced by the time-frequency spectral energy. The transform can reflect the spectral energy distribution of different frequency components while maintaining the excellent time-frequency resolution. While TT transform is a new approach to process non-stationary...

The s-transform is a variable window of STFT and extension of wavelet. This paper discussed the principle and method of Wavelet De-noising, reduced noise of pulse signal based on wavelet. It is shown that wavelet de-noising can eliminate most noise, and preserve effectively sudden change of signal. This paper analyzed and compared the effect of denoising of pulse signal in different ways all st...

We present an efficient method for computing the discrete orthonormal Stockwell transform (DOST). The Stockwell transform (ST) is a time-frequency decomposition transform that is showing great promise in various applications, but is limited because its computation is infeasible for most applications. The DOST is a nonredundant version of the ST, solving many of the memory and computational issu...

The use of solid state devices and equipments are increasing rapidly in electrical systems. Some of these solid state components introduce harmonics in the system. As a result the signals in electrical systems deviate from being a pure sinusoidal one. Quite often signals become very complex and their equations are unknown. Analysis of these complex signals is mainly accomplished using various k...

where φb,ξ = (2π)MξT−bDξφ, the modulation operator Mξ, the translation operator T−b and the dilation operator D 1 ξ are defined by (Mξh)(x) = e ixξh(x), (T−bh)(x) = h(x− b), (Dξh)(x) = |ξ|h(ξx), for all x in R and all measurable functions h on R. The Stockwell transform is a versatile tool first introduced in [11]. More recent results on the Stockwell transform in the contexts of applications c...