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Self-adaptive method dealing with the end issue of EMD

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2007年第10期35卷 1944-1948页

作者： Shao, Chen-Xi Wang, Jian Fan, Jin-Feng Yang, Ming Wang, Zi-Cai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei 230026 China Department of Computer Science and Technology University of Science and Technology of China Hefei 230026 China Control and Simulation Center Harbin Institute of Technology Harbin 150001 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

The empirical mode decomposition (EMD) developed by Huang etc. of NASA is an advanced method for signal analysis. But there is an involved end issue in the course of getting two envelops of the data using spline interpolation. A self-adaptive method to dealing with the end issue is proposed. Generally, it extends the externa sequence near the ends of the data by the most suited sequence in the inner data. For the chosen inner sequence has the most similarity of tendency with the ends, the data extension is reasonable. After the extension, the spline does not swing at both ends of the data. The result of experiment proved that the method can be used to solve the end issue effectively.

关键词： Signal analysis

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A Novel Approach for Signal Transduction Networks Simulation at a Mesoscopic Level

A Novel Approach for Signal Transduction Networks Simulation...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Chenxi Shao Hongli Deng MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei Anhui China University of Science and Technology of China Hefei Anhui CN

Signal transduction (ST) networks simulation is important to medical research. However, owing to the complexity of the networks, most methods presented for the simulation are not desirable. Here, based on multi-agent system (MAS), we proposed a novel approach characterized by a multi-level discrete space and mesoscopic molecule-agents for describing ST networks at a mesoscopic level. The space is developed for the representation of the cellular space; it provides a relatively sufficient spatial representation with less computational cost. The mesoscopic molecule-agents model the active components within the networks, which will offer new potentials to overcome the combinatorial complexity as well as make computation feasible. To validate this method, the simulations of a composition/decomposition system and the Mitogen-Activated Protein Kinase (MAPK) signal transduction pathway are implemented. Finally, an in-silico experiment with perturbation in the MAPK pathway is made. The results obtained from the simulations demonstrate the validity of the approach.

关键词： Computational modeling Laboratories Computer networks Computational efficiency Computer simulation multimedia computing Medical simulation Proteins Diseases Predictive models

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Study on The Property of Training Samples and Learning Space with Genetic Algorithms

Study on The Property of Training Samples and Learning Space...

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Jingsong He Jian Tang QianSheng Fang MOE-Microsoft Key Laboratory of Multimedia Computing and Communication Department of Electronic Science and Technology University of Science and Technology China Anhui Institute of Architecture and Industry China

Historically, the empirical risk of a pattern classifier was asked to be made zero, therefor the default property of training samples were limited to a separable ones. Nowadays on the contrary, the major idea of learning classification no longer ask the empirical risk of classifier must be made zero. In this situation, inseparable feature sets may not be detrimental to the performance of classifier. However, so far no experimental studies and analytical results show whether an inseparable feature set is available or not. This paper firstly analyzes the interaction between learning algorithms and feature selection, and gives a proof by both the analytical analysis and experimental studies.

关键词： Genetic algorithms Industrial training Space technology Risk analysis Algorithm design and analysis Law Legal factors Classification tree analysis Software engineering Artificial intelligence

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ParzenWindows Estimation Using Laplace Kernel: A Novel Parametric Analysis with Information Content

ParzenWindows Estimation Using Laplace Kernel: A Novel Param...

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Jingsong He Jian Tang QianSheng Fang MOE-Microsoft Key Laboratory of Multimedia Computing and Communication China Department of Electronic Science and Technology University of Science and Technology China Anhui Institute of Architecture and Industry China

Parzen windows estimation is one of the classical non- parametric methods in the field of machine learning and pattern classification, and usually uses Gaussian density function as the kernel. Although the relation between the kernel density estimation (KDE) and low-pass filtering is well known, it is vary difficult to setting the parameters of the other kinds of density functions. This paper proposes a novel method to deal with the parameters of Laplace kernel through measuring the degree of exchanged information among interpolating points. Experimental results showed that the proposed method can improve the performance of Parzen windows significantly.

关键词： Kernel Information analysis Density functional theory Low pass filters Machine learning Frequency estimation Cutoff frequency Bandwidth Artificial intelligence Pattern classification

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keywords Extracting as Text Chance Discovery

Keywords Extracting as Text Chance Discovery

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhenya Zhang Honemei Cheng Schoold of Electronic and Information Engineering Anhui Institute of Architecture and Industry Hefei China MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology Hefei China Department of AIA Hefei China

keyword auto-extracting is focused by researchers on information retrieval, data mining, chance discovery and others application. In this paper, new algorithm, CCG(Cognition & Concept Graph, for text chance discovery is presented based on cognition with data depth as measurement. When the keywords in a document are treated as chances in the document, those keywords can be extracted by CGC automatically. In CGC, concepts of a document are represented as maximum connected sub graphs of the basic graph for the document and the cognition of reader/author on a term is weighted with data depth. The correlation for word and concept is defined and the formula for the correlation calculating is given. Experimental results show that keywords extracted by CCG can describe the document and author/reader's cognition much better than keywords extracted by others technologies such as frequency accumulating or key Graph.

关键词： Data mining Cognition Frequency Data engineering Information retrieval Visualization Man machine systems Laboratories multimedia computing Industrial electronics

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Directly modeling of correlation matrices for GMM in speaker identification

Directly modeling of correlation matrices for GMM in speaker...

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18th International Conference on Pattern Recognition (ICPR 2006)

作者： Yao, Zhiqiang Zhou, Xi Dai, Beiqian Liu, Minghui Xie, Yanlu MOE Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China China

ISBN: (纸本)0769525210

In this paper, we present a new framework to model full covariance matrices of Gaussian components. In this framework, directly modeling the full correlation matrix instead of the full covariance matrix is our purpose, as the correlation matrix is the direct description of the correlation of inter feature elements. In order to model full correlation matrices, we share linear transformations among components' full correlation matrices. Thus, the full correlation matrix of each component is represented by a shared linear transformation and a component-specific diagonal correlation matrix. The transformation is used to help the diagonal correlation matrix to model the correlation of inter feature-vector elements more precisely. We evaluate our new framework on a mandarin speaker identification task. Experiments show that above 35% reduction in speaker identification error rate is achieved compared with the best diagonal covariance models. Furthermore, our algorithm achieved better performance than STC does.

关键词： Speech recognition

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Modeling glottal source for high quality voice conversion

Modeling glottal source for high quality voice conversion

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6th World Congress on Intelligent Control and Automation, WCICA 2006

作者： Jun, Sun Beiqian, Dai Jian, Zhang Yanlu, Xie MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China

ISBN: (纸本)1424403324

A novel modeling method for glottal source is proposed for Improving the naturalness and quality of synthetic speech. This paper utilizes the high correlation between vocal tract parameters and glottal source to model glottal source. Vocal tract parameters (LSF) are clustered into some classes. Within each class, a LSF vector closest to centroid and Its corresponding glottal wave derivative are selected as a code vector representing different phonetic class of voiced speech. At the stage of voice conversion or synthesis, we can find the relevant glottal source by virtual of finding the closest matched vocal tract parameters. Experiment results show that this vocal tract related glottal source model significantly outperform Rosenberg model and LF model. Correlation coefficients between vocal tract related glottal source and original glottal source increase 27% and 30.13%, spectral distance between synthetic speech and original speech reduce 50.5% and 51.48% respectively, comparing with Rosenberg model and LF model. © 2006 IEEE.

关键词： Speech synthesis

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Improved two-stage Wiener filter for robust speaker identification

Improved two-stage Wiener filter for robust speaker identifi...

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18th International Conference on Pattern Recognition, ICPR 2006

作者： Yanlu, Xie Minghui, Liu Zhiqiang, Yao Beiqian, Dai MOE Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China

In order to solve the problem of robustness in textindependent speaker identification, two-stage Wiener filter presented in the ETSI standard is used in this paper. However, the performance of the voice activity detection (VAD) block in the standard is not so satisfying under lower SNR. Thus constrained-clustering vector quantization is presented to improve the block in the Wiener filter method. Experiments based on the msRA 's mandarin speech corpora show the good robustness of the improved method to several common additive noises. © 2006 IEEE.

关键词： Speech recognition

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Kurtosis normalization in feature space for robust speaker verification

Kurtosis normalization in feature space for robust speaker v...

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2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006

作者： Yanlu, Xie Beiqian, Dai Zhiqiang, Yao Minghui, Liu MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China

ISBN: (纸本)142440469X

The acoustic mismatch between the training and test environments will lead to the difference of the statistical characteristics of speech parameters. Since the statistical characteristics of the kurtosis can measure the non-Gaussianity of a random variable, kurtosis normalization will make the training and test speech parameters match the standard normal distribution in some sense. In this paper, a kurtosis normalization method using sigmoid functions (logit functions) in feature space is presented for GMM-UBM based text-independent speaker verification system. Experimental results on the 2004 NIST SRE database show that with the new method significant improvement can be achieved in not only equal error rate but also minimum detection cost compared with baseline system (more than 33% relative reduction for long speech). © 2006 IEEE.

关键词： Feature extraction

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Image annotations based on semi-supervised clustering with semantic soft constraints

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PCM 2006: 7th Pacific Rim Conference on multimedia

作者： Xiaoguang, Rui Pingbo, Yuan Nenghai, Yu MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)3540487662

An efficient image annotation and retrieval system is highly desired for the increase of amounts of image information. Clustering algorithms make it possible to represent images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words, have been published for image annotation. But most of these models cluster only using visual features, ignoring semantics of images. In this paper, we propose a novel model based on semi-supervised clustering with semantic soft constraints which can utilize both visual features and semantic meanings. Our method first measures the semantic distance with generic knowledge (e.g. WordNet) between regions of the training images with manual annotations. Then a semi-supervised clustering algorithm with semantic soft constraints is proposed to cluster regions with semantic soft constraints which are formed by semantic distance. The experiment results show that our model improves performance of image annotation and retrieval system. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Image analysis

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