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PROCEEDINGS ELMAR

作者： Tykhonov, Vvacheslav A. Kudriavtseva, Nataliia V. Chmelar, Pavel Kharkiv Natl Univ Radioelect Kharkov Ukraine Univ Pardubice Dept Elect Engn Pardubice Czech Republic

ISBN: (纸本)9789531842099

A new approach for a speech signal parametric spectral characterization, based on a new factorization method, is proposed in this paper. The theoretical basis of the factorization method for multimode speech signals spectra is presented in the paper. This method is based on the multiplicative polymodels developed by the authors. We also derived equations for autoregressive coefficients calculation and for parametric power spectrum density of the multiplicative models' calculation. The expressions for factorization of spectra of the speech signal's phoneme using the multiplicative models are also included in the paper. The factorization method for spectral estimations is shown in the examples of speech signals parametric spectral estimations.

关键词： linear prediction model Autoregression Factorization Power Spectrum Density

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A multi-stage linear prediction model for the irregularity of the longitudinal level over unit railway sections

A multi-stage linear prediction model for the irregularity o...

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12th International Conference on Computer System Design and Operation in the Railway and other Transit Systems

作者： Chang, H. Liu, R. Li, Q. Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

ISBN: (纸本)9781845644680

Various researchers, both at home and abroad, have developed models to predict the irregularity of longitudinal level changes, which take into account different influential factors. In order to help the railway maintenance department to more accurately grasp the pattern of irregularity of longitudinal level changes, the present authors have developed a new model that uses historical track geometry inspection data from track inspection cars to predict the irregularity of longitudinal level changes for unit railway sections (which are taken as 200 metres long in this paper). Various factors affect the irregularity of the track. These mainly include train operation factors, track structure and environmental factors. However, for a certain unit railway section, key consideration may be given to the impact of passing tonnage on the irregularity of the longitudinal level. This paper establishes linear regression equations that are only applicable to each particular unit section to fit the functional relationship between the irregularity of the longitudinal level and passing tonnage. The linear fit equations are obtained from standard deviations of inspected track level data at 200 intervals. The change curve of the irregularity of the longitudinal level is divided into several stages and these different stages are fitted with different linear regression equations. A multi-stage broken line is thus formed to approximate the pattern of changes in the irregularity of the longitudinal level. On this basis, the authors put forward a multi-stage linear prediction model for the irregularity of the longitudinal level. Finally, we use inspected data collected from the Beijing-Jiulong Railway Line in 2008 and 2009 to make predictions and validate the model.

关键词： track irregularity of longitudinal level linear prediction model

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Price prediction Techniques For Residential Demand Response Using Support Vector Regression 7

Price Prediction Techniques For Residential Demand Response ...

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7th IEEE Power India International Conference (PIICON)

作者： Pal, Shalini Kumar, Rajesh Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9781467389624

The bidirectional flow of information among utilities and energy customers can be easily adapted to increase awareness for user's involvement in demand response programs. In demand response programs to improve the interaction between utility and customer, price communication plays an important role. If the future prices for next day can be sent to end consumer, so with the prior knowledge of price, the consumer can schedule their appliances in the same accordance to get less amount in the bill. Therefore, to get prior price information prediction technique comes in the scenario. To enhance price prediction capability, it needs a call from optimization techniques. In this paper, we have proposed the price prediction by support vector regression with genetic algorithm (SVRGA) approach. The simulation result has shown the efficiency of proposed approach and proposed technique is also compared with other existing techniques as artificial neural network (ANN) and linear prediction model (LPM).

关键词： Demand Response price prediction linear prediction model artificial neural network support vector regression

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Effective Load Scheduling of Residential Consumers based on Dynamic Pricing with Price prediction Capabilities

Effective Load Scheduling of Residential Consumers based on ...

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International Conference on Power Electronics, Intelligent Control and Energy Systems

作者： Shalini Pal Rajesh Kumar Malviya National Institute of Technology

ISBN: (纸本)9781467385886

Demand response (DR) sustains an influential role in today's smart grid. DR program is an initiative to enhance the performance of electricity price market and the stability of the power system. Price based DR programs have a significant part in the residential customer activities. In the present scenario, the flat tariffs are replaced by real-time pricing (RTP) models due to their economic benefits and the environment supportive behave. The RTP models are capable of providing a chance to customers to reduce their electricity bills. The customers can communicate their demand information to the utility and get back the prices via smart metering technologies. In this paper, an automatic load control approach with dynamic pricing models is implemented for residential consumers. In real time pricing environment, it is necessary to have price prediction capabilities. Here, the linear prediction model (LPM) and artificial neural network are implemented for predicting the prices. For optimization purpose mixed binary linear programming (MBLP) computations are used. To validate the performance of system simulation results has shown the better performance with the different scenario.

关键词： Demand Response Real Time Pricing Automatic Load Control linear prediction model Artificial Neural Network Mixed Primary linear Programming

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A Tutorial on Speech Synthesis models

A Tutorial on Speech Synthesis Models

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International Conference on Advanced Wireless Information and Communication Technologies (AWICT)

作者： Youcef, Tabet Mohamed, Boughazi Sadek, Affifi Univ Boumerdes Boumerdes 35000 Algeria Univ Annaba Annaba 23000 Algeria

For Speech Synthesis, the understanding of the physical and mathematical models of speech is essential. Hence, Speech modeling is a large field, and is well documented in literature. The aim in this paper is to provide a background review of several speech models used in speech synthesis, specifically the Source Filter model, linear prediction model, Sinusoidal model, and Harmonic/Noise model. The most important models of speech signals will be described starting from the earlier ones up until the last ones, in order to highlight major improvements over these models. It would be desirable a parametric model of speech, that is relatively simple, flexible, high quality, and robust in re-synthesis. Emphasis will be given in Harmonic / Noise model, since it seems to be more promising and robust model of speech. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： linear prediction model Sinusoidal model Harmonic/Noise model

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A Tutorial on Speech Synthesis models

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Procedia Computer Science 2015年 73卷 48-55页

作者： Tabet Youcef Boughazi Mohamed Affifi Sadek University of Boumerdes 35000 Algeria University of Annaba 23000 Algeria

关键词： linear prediction model Sinusoidal model Harmonic/Noise model

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Factorization of Processes Parametric Spectra on the Base of Multiplicative linear prediction Polymodels

Factorization of Processes Parametric Spectra on the Base of...

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24th International Conference on Radioelektronika (RADIOELEKTRONIKA)

作者： Kudriavtseva, Nataliia V. Univ Pardubice Dept Elect Engn Pardubice Czech Republic

ISBN: (纸本)9781479937158

The linear prediction models can be useful in different tasks of statistical radio engineering. The examples of multimode spectra decomposition on separate components for speech signals, heart rhythmograms, reflected ultrasound signals and hydroacoustic signals have been shown in the paper. The calculation of autoregressive coefficients and parametric power spectrum density of the multiplicative models were derived. Factorization of spectrum estimations is shown using an example of a multiplicative linear prediction model. Using the models that have been developing in our research it is possible to develop the new methods of complex processes analysis. The methods of rhythmogram analysis can be useful for specialists, who create algorithms of cardiogram analysis. More specifically, we consider a method of multimode spectrum factorization in composite process on components using our multiplicative linear prediction polymodels.

关键词： linear prediction model autoregression factorization power spectrum density

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Differences Between LP Orders for Tonal and Noise Parts of Audio Signal

Differences Between LP Orders for Tonal and Noise Parts of A...

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36th International Conference on Telecommunications and Signal Processing (TSP)

作者： Raso, Ondrej Balik, Miroslav Fu, Dongmei Brno Univ Technol Fac Elect Engn & Commun Technol Dept Telecommun Brno 61200 Czech Republic Univ Sci & Technol Beijing Sch Automat & Elect Dept Automat Beijing 100083 Peoples R China

ISBN: (纸本)9781479904020;9781479904037

In this article, advantages of audio signal separation into a tonal and a noise part for the purpose of linear predictive (LP) modeling will be studied. The LP modeling of the whole original audio signal is difficult. Hence, the article deals with the intention of LP modeling of noise part only. It will be studied whether the orders of LP filter for the LP modeling significantly differ for noise/tonal parts and its original audio signal or not. Firstly, a method for determination of an optimal LP order will be shortly described. Secondly, LP optimal orders for tonal/noise and its original tested audio signals will be measured. And finally, statistical analysis over the measurement data will be performed.

关键词： Audio Signal Digital Signal Processing Digital Filter linear prediction model Frame theory

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SPATIAL ERROR CONCEALMENT VIA model BASED COUPLED SPARSE REPRESENTATION

SPATIAL ERROR CONCEALMENT VIA MODEL BASED COUPLED SPARSE REP...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Zhai, Deming Liu, Xianming Zhou, Jiantao Zhao, Debin Gao, Wen Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China Univ Macau Dept CIS Macau Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9781479916030

In this paper, we propose a novel spatial error concealment algorithm through model-based coupled sparse representation. According to the non-local self-similarity property of natural images, we first collect two set of samples by template matching: one is called the latent set corresponding to the current missing patch and the other one is called the template set corresponding to the current template. Using these two sets of samples as the training data, we learn a dictionary pair and a linear prediction model simultaneously. The pair of dictionaries aims to characterize the two structural domains of the two sets, and the linear model is to reveal the intrinsic relationship between the sparse representations of the current missing patches and its template. Finally, we cast the non-local dictionary learning and local correlation model into a unified coupled sparse coding framework to obtain optimal sparse representation and further accurate estimation of the current missing patch. Experimental results demonstrate that the proposed method remarkably outperforms previous approaches.

关键词： Spatial error concealment adaptive dictionary learning linear prediction model coupled sparse representation

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Comparison of Optimum Filter Length in linear prediction between Speech and Musical Signals

Comparison of Optimum Filter Length in Linear Prediction bet...

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34th International Conference on Telecommunications and Signal Processing (TSP)

作者： Raso, Ondrej Balik, Miroslav Brno Univ Technol Fac Elect Engn & Commun Technol Dept Telecommun Brno 61200 Czech Republic

ISBN: (纸本)9781457714115

The optimal filter length of linear prediction (LP) model of musical signals are compared with speech signals in the paper. These musical signals are single musical instruments and vocal. Firstly, the global and segmental optimal orders of LP model are defined then the global optimal orders of LP model are measured for appropriately chosen musical and speech signals and finally statistical tests are performed. These tests determine if the differences between measured optimal orders are statistically significant or not.

关键词： Analysis of Variance Audio Signals Digital Signal Processing Digital Filters linear prediction model

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