Wavelet Transform based multistage speaker feature tracking identification system using Linear Prediction Coefficient
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In this paper wavelet transform (WT) in its two forms continuous and discrete are used to create text-dependent robust to noise speaker recognition system. The research intends to investigate a high accuracy of identification the speech signal of very difficult nature that is non- stationary. Three methods are used to extract the essential speaker features based on continuous, discrete wavelet transform and linear prediction coefficient (LPC). To have better identification rate three measurement methods are used: percentage rms difference (PRD), correlation coefficient (CC), and statically deformation determination coefficient (SDDC). 95% identification rate is accomplished. The presented system in this paper depends on multi-stage features extracting due to its better accuracy. The system works with excellent capability of features tracking even when the tested signals are very noisy with -32dB SNR. This is accomplished because of multistage features tracking based system using wavelet transform, which is suitable for non-stationary signal.Keywords:
Stationary wavelet transform
Second-generation wavelet transform
SIGNAL (programming language)
Tracking (education)
Continuous wavelet transform
Identification
Feature (linguistics)
Second-generation wavelet transform
Stationary wavelet transform
Harmonic wavelet transform
Lifting Scheme
Cascade algorithm
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The monogenic signal is a multidimensional generalization of the analytic signal. The monogenic signal of the continuous wavelet transform is called the monogenic wavelet transform. Stationary wavelet transform is a redundant discrete wavelet transform, which is translation-invariant. A new method for blind image source separation based on position-scale information using the monogenic wavelet transform discretized by the stationary wavelet transform is presented.
Harmonic wavelet transform
Stationary wavelet transform
Second-generation wavelet transform
Lifting Scheme
Continuous wavelet transform
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Although Optical wavelet transform has some advantages over discrete wavelet transform, but the mother wavelets to used are very few. That limits the signal processing ability of optical wavelet transform. Without scaling functions, the multiresolution analysis of a mother wavelet is not complete. In this paper, almost all the mother wavelets used in discrete wavelet transform are introduced into optical wavelet transform. Based on the analysis, we find whether the mother wavelets have analytical forms is not a necessary condition for implementing them in optical wavelet transform. Optical wavelet transform only needs to obtain the 2D approximations of wavelet functions. Then, with the cascade algorithm, the 1D approximations of scaling and wavelet functions are computed. By the scheme of 2D separable wavelet transform, the approximations of 2D scaling and wavelet functions are constructed. So mother wavelets frequently utilized in discrete wavelet transform are introduced into optical wavelet transform. With the increase of mother wavelet for selection, it is natural to classify optical wavelet transform into separable and non-separable cases as it does in discrete wavelet transform. Since the mothers introduced by the method in this paper are separable, they are included in the separable optical wavelet transform. And the advantages of the separable mothers are listed with corresponding examples.
Stationary wavelet transform
Second-generation wavelet transform
Lifting Scheme
Harmonic wavelet transform
Cascade algorithm
Fast wavelet transform
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Recently, the second generation wavelet which is lifting scheme of the first generation wavelet has attached much attention, because it keeps the good characteristics of the first generation wavelet transform and gets over the limitation of the first generate wavelet transform. This paper expounds of the lifting scheme and the excellent characteristics of the second generation wavelet transform, and makes comparison between the first generation wavelet ransform(DWT) and the second generation wavelet transform(LWT).
Lifting Scheme
Second-generation wavelet transform
Stationary wavelet transform
Harmonic wavelet transform
Cascade algorithm
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Combined use of the X-ray (Radon) transform and the wavelet transform has proved to be useful in application areas such as diagnostic medicine and seismology. In the present paper, the wavelet X-ray transform is introduced. This transform performs 1D wavelet transforms along line in Rn, which are parameterized in the same fashion as for the X-ray transform. It is shown that the transform has the same convenient inversion properties as the wavelet transform. The reconstruction formula receives further attention in order to obtain usable discretizations of the transform. Finally, a connection between the wavelet X-ray transform and the filtered backprojection formula is discussed.
Radon transform
Harmonic wavelet transform
Second-generation wavelet transform
Stationary wavelet transform
S transform
Continuous wavelet transform
Lifting Scheme
Constant Q transform
USable
Integral transform
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The scheme for designing virtual instrument of wavelet transform is described. Mathematic model of wavelet analysis function and typical controls is founded and the algorithm is selected and designed. Based on virtual instrument technology, the virtual instrument of wavelet transform is successfully developed. The instrument consists of continuous wavelet transform, discrete wavelet transform, wavelet package decomposition etc, which provide the engineering signal analysis a new manner. The fact is proved, the instrument has extensive using value.
Second-generation wavelet transform
Lifting Scheme
Stationary wavelet transform
Harmonic wavelet transform
Virtual instrumentation
Fast wavelet transform
Continuous wavelet transform
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The wavelet transform is implemented using an optical multichannel correlator with a bank of wavelet transform filters. This approach provides a shift-invariant wavelet transform with continuous translation and discrete dilation parameters. The wavelet transform filters can be in many cases simply optical transmittance masks. Experimental results show detection of the frequency transition of the input signal by the optical wavelet transform.
Second-generation wavelet transform
Harmonic wavelet transform
Stationary wavelet transform
Lifting Scheme
Fast wavelet transform
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Citations (118)
The work investigates the continuous and discrete wavelet transform to determine the corresponding features of ECG signals with variable temporal and spatial components. Discrete wavelet transform is implement as a filter bank. The approximation and refinement of wavelet coefficients from different frequency sub-bands are used to eliminate high-frequency noise, compress the signals, and there classification. Continuous wavelet transform, presented in the form of a scale diagram, is using to analyse ECG signals and develop a predictive model.
Second-generation wavelet transform
Stationary wavelet transform
Harmonic wavelet transform
Lifting Scheme
Continuous wavelet transform
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Many methods for computer vision applications have been developed using wavelet theory. Almost all of them are based on real-valued discrete wavelet transform. This chapter introduces two computer vision applications, namely moving object segmentation and moving shadow detection and removal, using Daubechies complex wavelet transform. Daubechies complex wavelet transform has advantages over discrete wavelet transform as it is approximately shift-invariant, has a better edge detection, and provides true phase information. Results after applying Daubechies complex wavelet transform on these two applications demonstrate that Daubechies complex wavelet transform-based methods provide better results than other real-valued wavelet transform-based methods, and it also demonstrates that Daubechies complex wavelet transform has the potential to be applied to other computer vision applications.
Daubechies wavelet
Second-generation wavelet transform
Stationary wavelet transform
Lifting Scheme
Harmonic wavelet transform
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This paper is a comparative study of image denoising using previously known wavelet transform and new type of wavelet transform, namely, Diversity enhanced discrete wavelet transform. The Discrete Wavelet Transform (DWT) has two parameters: the mother wavelet and the number of iterations. For every noisy image, there is a best pair of parameters for which we get maximum output Peak Signal to Noise Ratio, PSNR. As the denoising algorithms are sensitive to the parameters of the wavelet transform used, in this paper comparison of DEDWT to DWT has been presented. The diversity is enhanced by computing wavelet transforms with different parameters. After the filtering of each detail coefficient, the corresponding wavelet transforms are inverted and the estimated image, having a higher PSNR, is extracted. To benchmark against the best possible denoising method three thresholding techniques have been compared. In this paper we have presented a more practical, implementation oriented work.
Second-generation wavelet transform
Stationary wavelet transform
Harmonic wavelet transform
Lifting Scheme
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Citations (2)