The objective of this study was to assess the difference in voice quality as defined by acoustical analysis using sustained vowel in laryngectomized patients in comparison with normal volunteers. This was designed as ...
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The objective of this study was to assess the difference in voice quality as defined by acoustical analysis using sustained vowel in laryngectomized patients in comparison with normal volunteers. This was designed as a retrospective single center cohort study. An adult tertiary referral unit formed the setting of this study. Fifty patients (40 males) who underwent total laryngectomy and 31 normal volunteers (18 male) participated. Group comparisons with the first three formant frequencies (F-1, F-2, and F-3) using linear predictive coding (LPC) (Laryngograph Ltd, London, UK) was performed. The existence of any significant difference of F-1, F-2, and F-3 between the two groups using the sustained vowel /i/ and the effects of other factors namely, tumor stage (T), chemoradiotherapy, pharyngectomy, cricothyroid myotomy, closure of pharyngoesophageal segment, and postoperative complication were analyzed. Formant frequencies F-1, F-2, and F-3 were significantly different in male laryngectomees compared to controls: F-1 (P < 0.001, Mann-Whitney U test), F-2 (P < 0.001, Student's t test), and F-3 (P = 0.008, Student's t test). There was no significant difference between females in both groups for all three formant frequencies. Chemoradiotherapy and postoperative complications (pharyngocutaneous fistula) caused a significantly lower formant F, in men, but showed little effect in F-2 and F-3. Laryngectomized males produced significantly higher formant frequencies, F-1, F-2, and F-3, compared to normal volunteers, and this is consistent with literature. Chemoradiotherapy and postoperative complications significantly influenced the formant scores in the laryngectomee population. This study shows that robust and reliable data could be obtained using electroglottography and LPC in normal volunteers and laryngectomees using a sustained vowel.
We present a linearpredictive compression approach for time-consistent 3D mesh sequences supporting and exploiting scalability. The algorithm decomposes each frame of a mesh sequence in layers employing patch-based m...
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ISBN:
(纸本)9781424407217
We present a linearpredictive compression approach for time-consistent 3D mesh sequences supporting and exploiting scalability. The algorithm decomposes each frame of a mesh sequence in layers employing patch-based mesh simplification techniques. This layered decomposition is consistent in time. Following the predictivecoding paradigm, local temporal and spatial dependencies between layers and frames are exploited for compression. Prediction is performed vertex-wise from coarse to fine layers exploiting the motion of already encoded I-ring neighbor vertices for prediction of the current vertex location. It is shown that a predictive exploitation of the proposed layered configuration of vertices can improve the compression performance upon other state-of-the-art approaches by up to 16% in domains relevant for applications.
Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. Genomic signal processing is a new area ...
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Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies. In this paper, we present a comparative analysis of two genomic signal processing methods namely linear predictive coding and Discrete Wavelet Decomposition for robust microarray data clustering. Vector quantization is applied to the resultant coefficients to provide the clustering of the data samples. Both techniques were validated for standard data sets. Comparative analyses of the results indicate that these methods provide improved clustering accuracy compared to some conventional clustering techniques. Moreover, there classifiers don't require any prior training procedures
Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. In this paper, we present a new clusteri...
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Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. In this paper, we present a new clustering method based on linear predictive coding to provide enhanced microarray data analysis. In this approach, spectral analysis of microarray data is performed to classify samples according to their distortion values. The technique was validated for a standard data set. Comparative analysis of the results indicates that this method provides improved clustering accuracy compared to some conventional clustering techniques. Moreover, our classifier does not require any prior training procedure.
linear predictive coding (LPC) analysis was used to create morphed natural tokens of English voiced stop consonants ranging from /b/ to /d/ and /d/ to /g/ in four vowel contexts (/i/, /ae/, /a/, /u/). Both vowel conso...
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linear predictive coding (LPC) analysis was used to create morphed natural tokens of English voiced stop consonants ranging from /b/ to /d/ and /d/ to /g/ in four vowel contexts (/i/, /ae/, /a/, /u/). Both vowel consonant vowel (VCV) and consonant vowel (CV) stimuli were created. A total of 320 natural-sounding acoustic speech stimuli were created, comprising 16 stimulus series. A behavioral experiment demonstrated that the stimuli varied perceptually from /b/ to /d/ to /g/, and provided useful reference data for the ambiguity of each token. Acoustic analyses indicated that the stimuli compared favorably to standard characteristics of naturally-produced consonants, and that the LPC morphing procedure successfully modulated multiple acoustic parameters associated with place of articulation. The entire set of stimuli is freely available on the Internet (http://***/similar to lholt/php/***) for use in research applications. (C) 2011 Elsevier B.V. All rights reserved.
Although a considerable number of studies have been focused on the analysis of pathological voices using conventional parameters such as jitter, shimmer, and signal-to-noise ratio (SNR), these parameters have been fou...
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Although a considerable number of studies have been focused on the analysis of pathological voices using conventional parameters such as jitter, shimmer, and signal-to-noise ratio (SNR), these parameters have been found to be sensitive to variations in pitch extraction algorithm and cannot analyze severely disordered voice signals which exhibit irregular or aperiodic waveforms. In this paper, higher-order statistics (HOSs) analysis, which is independent of pitch period, is derived from linear predictive coding (LPC) residuals to describe breathy and rough voices. Recordings of a sustained /a/ from 23 individuals with breathy voices and 30 individuals with rough voices were collected from the disordered voice database distributed by the Japanese Society of Logopedics and Phoniatrics. We extracted conventional parameters as well as the HOS-based parameters such as the normalized skewness and the normalized kurtosis. On the other hand, we calculated HOS-based parameters from the LPC residual domain. The results showed that the HOS-based parameters and the HOS-based parameters estimated from the LPC residual are different for rough and breathy voices. Conventional parameters were not distinctive for these voices. Classification and regression tree (CART) was used to combine multiple parameters and to classify breathy and rough voices. Using the HOS-based parameters, the CART achieved an accuracy of 85.0% with the optimal decision tree generated by means of the normalized skewness and kurtosis. When the HOS-based parameters using LPC residual were used, the optimal decision tree was 88.7% accurate and the variances of the normalized skewness and kurtosis were included. (C) 2010 Elsevier Ltd. All rights reserved.
Accurate forecasting of renewable energies such as wind and solar has become one of the most important issues in developing smart grids. Therefore introducing suitable means of weather forecasting with acceptable prec...
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Accurate forecasting of renewable energies such as wind and solar has become one of the most important issues in developing smart grids. Therefore introducing suitable means of weather forecasting with acceptable precision becomes a necessary task in today's changing power world. In this work, an intelligent way for hourly estimation of both wind speed and solar radiation in a typical smart grid has been proposed and its superior performance is compared to those of conventional methods and neural networks (NNs). The methodology is based on linear predictive coding and digital image processing principles using two dimensional (2-D) finite impulse response filters. Meteorological data have been collected during the period 1 January 2009 to 31 December 2009 from Casella automatic weather station (AWS) at Plymouth, UK. Numerical results indicate that a considerable improvement in forecasting process is achieved with 2-D predictive filtering compared to the conventional approaches.
Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next gene...
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Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.
In this paper the SOM is used in an exploratory analysis of transfer phenomena from first language (L1) to the second language (L2) related to word/lexical stress. The basic hypothesis tested is whether the parameteri...
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ISBN:
(数字)9783642215667
ISBN:
(纸本)9783642215667
In this paper the SOM is used in an exploratory analysis of transfer phenomena from first language (L1) to the second language (L2) related to word/lexical stress. The basic hypothesis tested is whether the parameterization of the speech signal of the learner's utterances by standard signal processing techniques, such as linear predictive coding (LPC), used to encode the input of the network results in efficient categorization of speakers by the SOM. Preliminary results indicates that the combination LPC+SOM is indeed able to produce well-defined clusters of speakers that possess similarities regarding the transfer of stress patterns among Brazilian students in learning English as a foreign language.
An advanced spectral encoding method used in combination with independent component analysis (ICA) yields promising results in identifying refinery fractions contained in commercial gasoline mixtures based on infrared...
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An advanced spectral encoding method used in combination with independent component analysis (ICA) yields promising results in identifying refinery fractions contained in commercial gasoline mixtures based on infrared (IR) spectroscopy data. Previous work has shown how the signatures of the gasoline constituents can be recovered by solely relying on the IR spectra of their mixtures using ICA as a blind separation procedure. The present methodology encodes peak information from the spectra in linearpredictive (LP) coefficients which are subsequently transformed into line spectrum frequencies (LSF). Such encoded spectra have a drastically reduced size (to 1/20 of the original size) while preserving the crucial peak information that characterizes each constituent. Source identification is then established by simply computing a Euclidean distance measure between the corresponding LSF of the gasoline constituents predicted by ICA and the LSF available from the spectral library of candidate matches. High correlation scores are associated with successful identification of source spectra, and this indicates that the present methodology can be employed as an effective tool in fingerprinting applications. (c) 2007 Elsevier B.V. All rights reserved.
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