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Assessment of the formant frequencies in normal and laryngectomized individuals using linear predictive coding

JOURNAL OF VOICE 2007年第6期21卷 661-668页

作者： Kazi, Rehan A. Prasad, Vyas M. N. Kanagalingam, Jeeve Nutting, Christopher M. Clarke, Peter Rhys-Evans, Peter Harrington, Kevin J. Royal Marsden Hosp Head & Neck Unit London SW3 6JJ England Inst Canc Res London SW3 6JB England

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.

关键词： formant laryngectomy linear predictive coding

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Scalable linear predictive coding of time-consistent 3D mesh sequences

Scalable linear predictive coding of time-consistent 3D mesh...

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1st International Conference on 3DTV

作者： Stefanoski, Nikolce Liu, Xiaoliang Klie, Patrick Ostermann, Joern Leibniz Univ Hannover D-30167 Hannover Germany

ISBN: (纸本)9781424407217

We present a linear predictive 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 predictive coding 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.

关键词： animation compression dynamic 3D mesh coding scalability linear predictive coding time-consistent mesh sequence

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linear predictive coding and Wavelet Decomposition for Robust Microarray Data Clustering

Linear Predictive Coding and Wavelet Decomposition for Robus...

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29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.11

作者： Robert S. H. Istepanian Ala Sungoor Jean-Christophe Nebel Mobile Information and Network Technologies Research Centre (MINT) Kingston University London UK

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

关键词： linear predictive coding Robustness Genomics Bioinformatics Digital signal processing Monitoring Signal analysis Signal processing Wavelet analysis Discrete wavelet transforms

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linear predictive coding for Enhanced Microarray Data Clustering

Linear Predictive Coding for Enhanced Microarray Data Cluste...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Robert S. H. Istepanian Ala Sungoor Jean-Christophe Nebel Mobile Information and Network Technologies Research Centre Kingston University Kingston-Upon-Thames London UK

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.

关键词： linear predictive coding Gene expression Distortion measurement Genomics Bioinformatics Data analysis Spectral analysis Data mining Cardiovascular diseases Signal processing

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A standard set of American-English voiced stop-consonant stimuli from morphed natural speech

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SPEECH COMMUNICATION 2011年第6期53卷 877-888页

作者： Stephens, Joseph D. W. Holt, Lori L. Carnegie Mellon Univ Dept Psychol Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA

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.

关键词： Speech stimuli Consonants linear predictive coding

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An efficient approach using HOS-based parameters in the LPC residual domain to classify breathy and rough voices

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2011年第2期6卷 186-196页

作者： Lee, JiYeoun Jeong, Sangbae Hahn, Minsoo Sprecher, Alicia J. Jiang, Jack J. Univ Wisconsin Sch Med Med Sci Ctr 5745A Div Otolaryngol Head & Neck Surg Madison WI 53706 USA Gyeongsang Natl Univ Sch Elect & Elect Engn Div Elect Engn Jinju 660701 South Korea Korea Adv Inst Sci & Technol Sch Engn Div Multimedia Commun & Proc Taejon 305701 South Korea

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.

关键词： Pathological voice quality Higher-order statistics linear predictive coding Classification and regression tree

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Study of forecasting renewable energies in smart grids using linear predictive filters and neural networks

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IET RENEWABLE POWER GENERATION 2011年第6期5卷 470-480页

作者： Moghaddam, A. Anvari Seifi, A. R. Shiraz Univ Dept Power & Control Sch Elect & Comp Engn Engn Fac 1 Shiraz Iran

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.

关键词： Other power apparatus and electric machines linear predictive filters meteorological data power engineering computing Energy resources two dimensional finite impulse response filters Neural computing techniques weather forecasting wind speed Power engineering computing smart grids Energy resources and fuels linear codes neural networks neural nets solar radiation smart power grids renewable energies forecasting digital image processing principles power filters Casella automatic weather station 2D FIR filters linear predictive coding FIR filters renewable energy sources

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Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2011年第4期10卷 225-238页

作者： Istepanian, Robert S. H. Sungoor, Ala Nebel, Jean-Christophe Kingston Univ London Mobile Informat & Network Technol Res Ctr Kingston Upon Thames KT1 2EE Surrey England Kingston Univ London Fac Sci Engn & Comp Kingston Upon Thames KT1 2EE Surrey England

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.

关键词： Discrete wavelet fractal dimension genomic signal processing linear predictive coding microarray clustering vector quantization

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A SOM-Based Analysis of Early Prosodic Acquisition of English by Brazilian Learners: Preliminary Results

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8th International Workshop on Self-Organizing Maps (WSOM)

作者： Silva, Ana Cristina C. Macedo, Ana Cristina P. Barreto, Guilherme A. Univ Estadual Piaui Dept Linguist Corrente Brazil Univ Fed Ceara Dept Linguist Fortaleza Ceara Brazil Univ Fed Ceara Dept Teleinformat Engn Fortaleza Ceara Brazil

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.

关键词： Self-organizing map word/lexical stress linear predictive coding U-matrix

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linear predictive spectral coding and independent component analysis in identifying gasoline constituents using infrared spectroscopy

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2007年第1期89卷 51-58页

作者： Kardamakis, Andreas A. Mouchtaris, Athanasios Pasadakis, Nikos Tech Univ Crete Mineral Resources Engn Dept Khania 73100 Greece Fdn Res & Technol Hellas Inst Comp Sci Iraklion 71110 Greece

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 linear predictive (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.

关键词： linear predictive coding gasoline fingerprinting independent component analysis infrared spectroscopy spectral encoding

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