@inproceedings {533,
	title = {Modeling the correlation structure of images in the wavelet domain},
	booktitle = {14th Canadian Conference on Electrical and Computer Engineering},
	year = {2001},
	address = {Toronto},
	abstract = {<p>In this paper we investigate the correlation structure of the wavelet coefficients corresponding to random fields. The context of this work is the study of Bayesian approaches to wavelet shrinkage for the purposes of image denoising. This paper concentrates on both within-scale and across-scale statistical dependencies for a variety of wavelets and random fields, with examples provided for both 1-D and 2-D signals. The results show the whitening effect of the wavelet transform to be quite clear-even for particular highly correlated spatial processes the within-scale correlation decays exponentially fast, however the correlation between scales is surprisingly substantial, even for separations several scales apart. Our goal, initiated in this paper, is the development of an efficient random field model, describing these statistical correlations, and the demonstration of its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising</p>
},
	keywords = {1-D signals, 2-D signals, across-scale statistical dependencies, Bayes methods, Bayesian approaches, correlation structure, correlation theory, Image Denoising, image enhancement, random field model, random fields, wavelet coefficients, wavelet domain, wavelet shrinkage, wavelet transforms, white noise, whitening effect, within-scale statistical dependencies},
	doi = {http://dx.doi.org/10.1109/CCECE.2001.933599},
	author = {Z Azimifar and P Fieguth and E Jernigan}
}
@inproceedings {515,
	title = {Speech enhancement using voice source models},
	booktitle = {25th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	year = {1999},
	abstract = {<p>Autoregressive (AR) models have been shown to be effective models of the human vocal tract during voicing. However the most common model of speech for enhancement purposes, AR process excited by white noise, fails to capture the periodic nature of voiced speech. Speech synthesis researchers have long recognized this problem and have developed a variety of sophisticated excitation models, however these models have yet to make an impact in speech enhancement. We have chosen one of the most common excitation models, the four-parameter LF model of Fant, Liljencrants and Lin (1985), and applied it to the enhancement of individual voiced phonemes. Comparing the performance of the conventional white-noise-driven AR, an impulsive-driven AR, and AR based on the LF model shows that the LF model yields a substantial improvement, on the order of 1.3 dB</p>
},
	keywords = {AR models, AR process, autoregressive models, autoregressive processes, excitation models, four-parameter LF model, human vocal tract, impulsive-driven AR, parameter estimation, performance, periodic voiced speech, speech enhancement, speech model, speech synthesis, voice source models, voiced phonemes, voicing, white noise, white-noise-driven AR},
	doi = {http://dx.doi.org/10.1109/ICASSP.1999.759791},
	author = {A Yasmin and P Fieguth and L Deng}
}
