A six-band filter structure derived by using admissible wavelet
packets for the extraction of the features for recognition of noisy
speech is proposed. A simple compensation for white Gaussian noise is
carried out and the recognition performance is compared with the
features based on Mel scale cepstral coefficients (MFCC) and 24-band
admissible wavelet packet filter structure
"The decomposition process is recursively applied to both the low and high frequency sub-bands to generate the next level of the hierarchy. If an orthonormal wavelet basis has been chosen, the coefficients computed are independent and possess a distinct feature of the original signal . Wavelet packets can be described by the following collection of basis functions: "
[Show abstract][Hide abstract] ABSTRACT: The existence of vocal fold edema or the formation of nodules
and polyp are one of the conventional types of benign vocal
fold lesions that can affect the speech signal quality of
patients. This paper proposes a non-invasive method in order
to discriminate these three types of vocal fold diseases and
classify them into their corresponding group of vocal fold
inflammation by processing the speech signal of patients.
Experiments on the basis of two different methods of feature
extraction, wavelet packet sub bands and Mel frequency
scaled filter banks, are carried out with 83 voiced signals,
uttered by individuals of both sexes, aged from 19 to 81, each
suffering from one of these three special cases of vocal fold
swelling. As the similarity of these three groups of vocal fold
disorder leads to highly correlated groups of extracted features
for each class, genetic algorithm is applied to find the most
separable feature vector indexes. The classification done
through using support vector machine as a nonlinear classifier
showed that extracted feature vectors on the basis of entropy
definition, as an expression of vocal fold irregularities, under
some specific wavelet packet sub-bands results in the best
classification percentage of 91.18% for these three classes of
vocal fold pathology.
International Conference on Speech and Computer; 09/2005
"Second, filter banks implementing wavelet transforms are typically dyadic, splitting the spectrum in half. We examine tree-structured filter banks, which were previously proposed as potential solution, allowing iteration at both high and low channels of a 2-channel filter bank . We then look into rational filter banks to obtain finer frequency resolution and naturally simulate the critical bands of the human auditory system . "
[Show abstract][Hide abstract] ABSTRACT: Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (p. 87-91). by Ghinwa F. Choueiter. S.M.
[Show abstract][Hide abstract] ABSTRACT: A new pre-processing stage based on wavelet denoising is proposed to extract robust features in the presence of additive white Gaussian noise. Recognition performance is compared with the commonly used Mel frequency cepstral coefficients with and without this preprocessing stage. The word recognition accuracy is found to improve using the proposed technique by 2 to 28% for signal-to-noise ratio in the range of 20 to 0 dB.
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