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检索条件"主题词=linear predictive coding"

共 2458 条记录，以下是371-380 订阅

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A Fusion Algorithm of Multiple Classifiers for Recognition of Ships Radiated_Noises Based on Many-Person Decision-Makings Theory

A Fusion Algorithm of Multiple Classifiers for Recognition o...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chunyu Kang Xinhua Zhang Research Center of Signal and Information Dalian Naval Academy DaLian China

Recognition of the radiated noises of ships is a very complicated and difficult job. In order to improve the classification performance and dependability of the single classifier, a kind of multi-classifiers decision making model and detailed algorithm based on many-person decision-makings theory was proposed. By adopting the eight categorized results of the BP neural network and the support vector machines which used the Welch spectrum, the linear predictive coding spectrum, the Burg spectrum and the perceptual linear predictive feature, the group decision-makings were done to recognize three different kinds of radiated noises of ships which were collected in many different operating conditions. Results show that the proposed fusion model and algorithm are feasible and the statistical right recognition probability arrives at 96.44% and its classification performance is superior to the performance of the single classifier. The method can be applied to the underwater acoustic target recognition system.

关键词： Marine vehicles Decision making Target recognition Character recognition linear predictive coding Oceans Neural networks Support vector machines Support vector machine classification Underwater acoustics

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Smart Transcoding between CELP Speech Codecs through Voiced Oriented Pitch Mapping

Smart Transcoding between CELP Speech Codecs through Voiced ...

引用

IEEE Workshop on Multimedia Signal Processing

作者： Christophe Beaugeant Nokia Siemens Networks Munich Germany

ISBN: (纸本)9781424412730;1424412730;1424412749

The deployment of incompatible standardized speech codecs implies interoperability issues between telecommunication networks. Transcoding from one codec format to another is necessary at gateways between networks in order to assure the interoperability. This transcoding reduces speech quality, involves computation load and additive delay. Recently, a fair amount of work has been conducted on studying alternative transcoding methods reducing complexity and delay. In this context this paper further elaborates on solutions based on pitch mapping between standardized CELP codecs. It presents an alternative solution to the already published ones by applying a pitch mapping driven by the voiced property of the frames of analysis. A better compromise between reduction of complexity and impact on the quality of the processed speech signal is thus achieved.

关键词： Transcoding Speech codecs Speech analysis linear predictive coding Filters Decoding Additive noise Speech enhancement Delay estimation Narrowband

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Modeling of speech signals using fractional calculus

Modeling of speech signals using fractional calculus

引用

International Symposium on Signal Processing and Its Applications (ISSPA)

作者： Khaled Assaleh Wajdi M. Ahmad Department of Electrical Engineering American University of Sharjah Sharjah UAE Electrical and Computer Engineering Department University of Sharjah Sharjah UAE

In this paper, we present a novel approach for speech signal modeling using fractional calculus. This approach is contrasted with the celebrated linear predictive coding (LPC) approach which is based on integer order models. It is demonstrated via numerical simulations that by using a few integrals of fractional orders as basis functions, the speech signal can be modeled accurately. The new approach has the merit of requiring a smaller number of model parameters, and is demonstrated to be superior to the LPC approach in capturing the details of the modeled signal.

关键词： Fractional calculus Speech analysis linear predictive coding predictive models Differential equations Speech coding Biological system modeling Signal analysis Numerical simulation Application software

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CODDYAC: Connectivity Driven Dynamic Mesh Compression

CODDYAC: Connectivity Driven Dynamic Mesh Compression

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3DTV Conference

作者： Libor Vasa Vaclav Skala Department of Computer Science and Engineering University of West Bohemia Czech Republic

Compression of 3D mesh animations is a topic that has received increased attention in recent years, due to increasing capabilities of modern processing and displaying hardware. In this paper we present an improved approach based on known techniques, such as principal component analysis (PCA) and EdgeBreaker, which allows efficient encoding of highly detailed dynamic meshes, exploiting both spatial and temporal coherence. We present the results of our method compared with similar approaches described in literature, showing that using our approach we can achieve better performance in terms of rate/distortion ratio.

关键词： Principal component analysis Animation Shape linear predictive coding Spatial coherence Computer science Hardware Encoding Rate distortion theory Entropy

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Neural systems and time course of proactive interference in working memory

Neural systems and time course of proactive interference in ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yingchun Du John X Zhang Zhuangwei Xiao Renhua Wu Guangdong Key Laboratory of Medical Molecular Imaging Medical College of Shantou University China Department of Psychology Chinese University of Hong Kong Hong Kong China Second Affiliated Hospital Medical college of Shantou University China

The storage of information in working memory suffers as a function of proactive interference. Many works using neuroimaging technique have been done to reveal the brain mechanism of interference resolution. However, less is yet known about the time course of this process. Event-related potential method(ERP) and standardized low resolution brain electromagnetic tomography method (sLORETA) were used in this study to discover the time course of interference resolution in working memory. The anterior P2 was thought to reflect interference resolution and if so, this process occurred earlier in working memory than in long-term memory.

关键词： Interference Probes Enterprise resource planning Neuroimaging Psychology Inverse problems linear predictive coding Recruitment Educational institutions Diseases

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An Approach to Formant Frequency Estimation at Low Signal-to-Noise Ratio

An Approach to Formant Frequency Estimation at Low Signal-to...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： S. A. Fattah W.-P. Zhu M. O. Ahmad Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

A new approach for the formant frequency estimation of the voiced speech segments in the presence of noise is presented in this paper. A correlation model for the voiced speech is proposed considering the vocal-tract system as an autoregressive moving average (ARMA) model with a periodic impulse-train excitation. It is shown that the formant frequencies can be directly obtained from the model parameters. An adaptive residue-based least-squares optimization algorithm is proposed to estimate the model parameters, which overcomes the failure of conventional correlation based techniques in estimating formant frequencies at a low signal-to-noise ratio (SNR). The proposed algorithm has been tested on synthetic and natural vowels as well as voiced segments of some naturally spoken sentences from TIMIT database in presence of white Gaussian or babble noises. The experimental results show that the proposed method is more robust to noise than some existing methods even at a low SNR of 0 dB.

关键词： Frequency estimation Signal to noise ratio Autoregressive processes Speech analysis Working environment noise Speech processing Signal processing algorithms Power system modeling linear predictive coding Filters

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Enhanced Speech Recognition with Blind Equalization For Robot "WEVER-R2"

Enhanced Speech Recognition with Blind Equalization For Robo...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Donglin Wang Henry Leung Keun-Chang Kwak Hosub Yoon Department ofElectrical and Computer Engineeing University of Calgary AB Canada ETRI Daejeon South Korea

Ambient noise caused by room acoustics usually degrades quality and reliability of speech recognition for the service robot application. In this paper, we propose enhancing the robustness of speech recognition for home service robots by combining blind equalization with classification. In particular, the linear predictive code (LPC) is used for feature extraction. The constant modulus algorithm (CMA) is combined with the radial basis function (RBF) neural network to form a robust classifier with automatic equalization capability to reduce the room service effect. Using real speech data collected by WEVER-R2 acoustic robot, it is shown that the proposed method can increase the speech recognition rate from 74.46% to 87.23% for age recognition and from 80.95% to 95.09% for gender recognition.

关键词： Speech recognition Blind equalizers Service robots Automatic speech recognition Acoustic applications Degradation Noise robustness linear predictive coding Feature extraction Neural networks

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Determining Efficiency of Speech Feature Groups in Emotion Detection

Determining Efficiency of Speech Feature Groups in Emotion D...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Gokhan Polat Halis Altun Elektrik - Elektronik Mühendisi Ni¿de Üniversitesi Yüksek Lisans Ö¿rencisi. e-posta: gpolat51proje@*** Elektrik-Elektronik Mütihendisi Mütihendislik Mimarlhk Fakültesi Niğde Üniversitesi Nigde Turkey

Features, extract from speech parameter are frequently used in emotion detection problem. Prosodic, MFCC, LPC and band energy feature groups are commonly used in literature to detect emotion in speech. The aim of the ... 详细信息

关键词： Speech linear predictive coding Mel frequency cepstral coefficient Feature extraction Support vector machines Support vector machine classification

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Use of Novel Feature Extraction Technique with Subspace Classifiers for Speech Recognition

Use of Novel Feature Extraction Technique with Subspace Clas...

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IEEE/ACS International Conference on Pervasive Services (ICPS)

作者： Serkan Gunal Rifat Edizkan Department of Computer Engineering Anadolu University Eskisehir Turkiye Department of Electrical and Electronics Engineering Eskisehir Osmangazi University Eskisehir Turkiye

Speech recognition is one of the fast moving research areas in pervasive services requiring human interaction. Like any type of pattern recognition system, selection of the feature extraction method and the classifier play a crucial role for speech recognition in terms of accuracy and speed. In this paper, an efficient wavelet based feature extraction method for speech data is presented. The feature vectors are then fed into three widely used linear subspace classifiers for recognition analysis. These classifiers are Class Featuring Information Compression (CLAFIC), Multiple Similarity Method (MSM) and Common Vector Approach (CVA). TI-DIGIT database is used to evaluate the performance of speaker independent isolated word recognition system designed. Experimental results indicate that the proposed feature extraction method together with the CLAFIC and CVA classifiers give considerably high recognition rates.

关键词： Feature extraction Speech recognition Continuous wavelet transforms Fourier transforms Wavelet transforms Wavelet analysis Humans linear predictive coding Mel frequency cepstral coefficient Data engineering

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Voice Conversion Using Canonical Correlation Analysis Based on Gaussian Mixture Model

Voice Conversion Using Canonical Correlation Analysis Based ...

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

作者： ZhiHua Jian Zhen Yang Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing China

A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the canonical correlation analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after linear prediction (LPC) analysis, the CCA method is more suitable for spectral conversion than MMSE because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both subjective and objective evaluations are conducted. The experimental results demonstrate that the proposed scheme can achieve better performance than the previous method which uses MMSE estimation criterion.

关键词： Speech analysis Loudspeakers linear predictive coding Speech synthesis Artificial neural networks Signal processing algorithms Vectors Analysis of variance Information analysis Hidden Markov models

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