Our goal in this article is to present a quantitative study about speech recognition and the inherent problems of its applications and the computer processing. Our approach is characterized by independent speaker and ...
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ISBN:
(纸本)0819425915
Our goal in this article is to present a quantitative study about speech recognition and the inherent problems of its applications and the computer processing. Our approach is characterized by independent speaker and we made use of pre-processing the concept as wavelets Transform and as pattern recognition an Artificial Neural Network (ANN - Multilayer Perceptron -Backpropagation Algorithm).
We describe experiments that we have performed that address the issue of the relation between the same enunciations by different speakers. Our previous work indicated that frequencies are approximately scaled uniforml...
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ISBN:
(纸本)0819429139
We describe experiments that we have performed that address the issue of the relation between the same enunciations by different speakers. Our previous work indicated that frequencies are approximately scaled uniformly. In this paper we report results addressing possible corrections to uniform scaling. Our results show that the scaling is non uniform, that is the formant frequencies of different speakers scale differently at different frequencies. We discuss how this leads to the mathematical issue of separating the spectrum into a speaker dependent and speaker independent parts. We introduce the concept of a universal scaling function that is aimed at achieving this separation. The fundamental idea is to find a frequency axis transformation (warping function) which transforms the energy density spectrum (the squared absolute value of the Fourier transform of the enunciation) in such a way that a further Fourier transform of the resulting function achieves this separation. We discuss this procedure and relate it to the scale transform. Using real speech data we obtain such a transformation function. The resulting function is very similar to the Mel scale, which has been previously obtained only from psychoacoustic (hearing based) experiments. That similar scales are obtained from both hearing and speech production (as reported here) is fundamental to the understanding of speech and hearing.
We show how periodized wavelet packet transforms and periodized wavelet transforms can be implemented on a quantum computer. Surprisingly, we find that the implementation of wavelet packet transforms is less costly th...
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ISBN:
(纸本)0819432997
We show how periodized wavelet packet transforms and periodized wavelet transforms can be implemented on a quantum computer. Surprisingly, we find that the implementation of wavelet packet transforms is less costly than the implementation of wavelet transforms on a quantum computer.
EMG signals can be considered as the sum of scaled and delayed versions of a single prototype. We have applied the wavelet Transform choosing the mother wavelet so as to match the known shape of the basic component, a...
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ISBN:
(纸本)0819425915
EMG signals can be considered as the sum of scaled and delayed versions of a single prototype. We have applied the wavelet Transform choosing the mother wavelet so as to match the known shape of the basic component, and have compared the results obtained with different wavelets. The results in terms of MUAP detection and resolution are very encouraging even in the presence of asymmetric shape and high levels of additive noise.
The developments in wavelet theory have given rise to the wavelet thresholding method, for extracting a signal from noisy data [1,2]. Multiwavelets, wavelets with several scaling functions, have recently been introduc...
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ISBN:
(纸本)0780376226
The developments in wavelet theory have given rise to the wavelet thresholding method, for extracting a signal from noisy data [1,2]. Multiwavelets, wavelets with several scaling functions, have recently been introduced and they offer simultaneous orthogonality, symmetry and short support;which is not possible with ordinary wavelets, also called scalar wavelets [3]. This property makes multiwavelets more suitable for various signalprocessingapplications, especially compression and denoising. Like scalar wavelets, multiwavelets can be realized as filterbanks, however the filterbanks are now matrix-valued;requiring two or more input streams, which can be accomplished by prefiltering. In this paper, several thresholding methods to be used with different multiwavelets for image denoising are presented. The performances of multiwavelets are compared with those of scalar wavelets. Simulations reveal that multiwavelet based image denoising schemes outperform wavelet based methods both subjectively and objectively.
In this paper, we propose a new algorithm for objective blur estimation using wavelet decomposition. The central idea of our method is to estimate blur as a function of the center of gravity of the average cone ratio ...
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ISBN:
(纸本)9780819474988
In this paper, we propose a new algorithm for objective blur estimation using wavelet decomposition. The central idea of our method is to estimate blur as a function of the center of gravity of the average cone ratio (ACR) histogram. The key properties of ACR are twofold: it is powerful in estimating local edge regularity, and it is nearly insensitive to noise. We use these properties to estimate the blurriness of the image, irrespective of the level of noise. In particular, the center of gravity of the ACR histogram is a blur metric. The method is applicable both in case where the reference image is available and when there is no reference. The results demonstrate a consistent performance of the proposed metric for a wide class of natural images and in a wide range of out of focus blurriness. Moreover, the proposed method shows a remarkable insensitivity to noise compared to other wavelet domain methods.
Integration of the nonlinear approaches for system identification is proposed for spectral differentiation and object recognition in this research. Multi-scale nonlinear principal component analysis (NCA) has been imp...
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ISBN:
(纸本)9781424446018
Integration of the nonlinear approaches for system identification is proposed for spectral differentiation and object recognition in this research. Multi-scale nonlinear principal component analysis (NCA) has been implemented to analyze the individual components of approximations and details based on wavelet transform. Neural network training has been applied to NCA while both ID and 2D wavelet transform have been conducted across different scales. At each scale, the principal components are selected in order to reconstruct the intrinsic signal and image. This statistical identification approach is essential to enhance multivariate data processing. Case studies on signal and imageprocessing are both conducted. In addition, quantitative measures are presented to analyze the nonlinear multi-scale approach from the objective perspectives.
Recently, a logarithmic imageprocessing model called Symmetric Logarithmic imageprocessing (S-LIP) has been investigated in the framework of the multiresolution analysis (MRA) performed by wavelet transform. The S-L...
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ISBN:
(纸本)9781479983391
Recently, a logarithmic imageprocessing model called Symmetric Logarithmic imageprocessing (S-LIP) has been investigated in the framework of the multiresolution analysis (MRA) performed by wavelet transform. The S-LIP model is an extension of the Logarithmic imageprocessing (LIP) model. The motivation of this work is to implement classical waveletapplications in the S-LIP framework. The underlying idea is to take advantage of both the multiscale analysis performed by the wavelet transform and the logarithmic processing of the pixels' intensity by the S-LIP model. The S-LIP wavelet transform is introduced and applied to automatic denoising in order to highlight its intrinsic characteristics. As an illustration, signal-to-Noise Ratios for both the linear wavelet transform and S-LIP wavelet transform are calculated for different levels of Gaussian, Poisson, Speckle and salt-and-pepper noises.
image denoising is an active area of research and probably one of the most studied problems in the imageprocessing fields. In this paper we describe a new hybrid image denoising algorithm which combines Gaussian base...
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ISBN:
(纸本)9781479902699;9781479902675
image denoising is an active area of research and probably one of the most studied problems in the imageprocessing fields. In this paper we describe a new hybrid image denoising algorithm which combines Gaussian based neighborhood spatial filter with wavelet transform that based on neighborhood thresholding function which takes the correlation of the magnitude of the wavelet coefficient with its neighbors into consideration to decide whether the coefficient is noisy or noise free. Accordingly, noises are detected with the help of the surrounding information and are removed. Experimental results show that the proposed algorithm can effectively remove the image noises with less processing time as compared with the state-of-the-art denoising algorithm.
In this paper, we propose an image restoration algorithm based on state-of-the-art wavelet domain statistical models. We present an efficient method to estimate the model parameters from the observations, and solve th...
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ISBN:
(纸本)0819441929
In this paper, we propose an image restoration algorithm based on state-of-the-art wavelet domain statistical models. We present an efficient method to estimate the model parameters from the observations, and solve the restoration problem in orthonormal and translation-invariant (TI) wavelet domains. Substantial improvements over previous wavelet-based restoration methods are obtained. The use of a TI wavelet transform further enhances the restoration performance. We study the improvement from the viewpoint of Bayesian estimation theory and show that replacing an estimator with its TI version will reduce the expected risk if the signal and the degradation model are stationary.
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