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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： M.R. Izquierdo D.S. Reeves Network Hardware Division IBM Corporation NC USA Electrical and Computer Engineering North Carolina State University Raleigh NC USA

We present an MPEG slice layer model for VBR encoded video using linear predictive coding (LPC) and generalized periodic Markov chains. Each slice position within an MPEG frame is modeled using an LPC autoregressive function. The selection of the particular LPC function is governed by a generalized periodic Markov chain; one chain is defined for each I, P, and B frame type. The model is sufficiently modular in that sequences which exclude B frames can eliminate the corresponding Markov chain. We show that the model matches the pseudo-periodic autocorrelation function quite well. We present simulation results of an asynchronous transfer mode (ATM) video transmitter using a FIFO queue and measure the average cell delay. Simulation results showed good agreement with results obtained using actual traces as sources.

关键词： predictive models linear predictive coding Video compression Asynchronous transfer mode Delay Computer networks Bit rate Transform coding Stochastic processes Streaming media

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A speech recognition system using linear predictive coding and dynamic time warping

A speech recognition system using linear predictive coding a...

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

作者： W. Kinsner D. Peters Department of Electrical Engineering University of Manitoba Winnipeg MAN Canada

A system capable of recognizing isolated speech utterances with a rate of 97% and rejection rate of 2% on a 200-wood vocabulary is presented. The system uses linear predictive coding and dynamic time warping, both implemented in software on an Apple Macintosh computer. The Macintosh also provides an environment for a graphics human interface in the authors' speech research. The system is intended for vocal shaping of severely disabled individuals.< >

关键词： Speech recognition linear predictive coding Vocabulary Loudspeakers Synthesizers Speech processing Speech coding Computer graphics Humans Target recognition

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An ASIC design for linear predictive coding of speech signals

An ASIC design for linear predictive coding of speech signal...

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Euro ASIC

作者： Jhing-Fa Wang Liang-Ying Liu Chung-Heng Cheng Ming-Hwa Sheu Yuan-Long Jeang Jau-Yien Lee Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)0818628456

In the real-time speech recognition, the predictor coefficients of speech signals are used as the recognizing features and should be computed faster than the sampling rate. Under such performance constraint, the objective is to design this circuit as cheap as possible. Autocorrelation method is adopted for computing the coefficients because of its stability of the results and regular computations. A two-step pipelined functional unit is designed for calculating those regular computations. For the divisions needed in autocorrelation method, the prune-and-search approach is used to compute them. Because this approach can also be performed by the identical pipelined functional unit, the extra operator for the divisions is not required. Although the authors have designed this chip with least storage element and functional unit, however, the performance is still much faster than the real-time request.< >

关键词： Application specific integrated circuits Signal design linear predictive coding Speech coding Autocorrelation Speech recognition Hardware Feature extraction Sampling methods Circuit stability

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Packet loss concealment for VoIP based on Pitch Waveform Replication and linear predictive coding

Packet loss concealment for VoIP based on Pitch Waveform Rep...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Masahiro Toyoshima Tetsuya Shimamura Graduate School of Science and Engineering Saitama University Sakura-ku Saitama JAPAN

In this paper, we propose a Packet Loss Concealment (PLC) method. Our method is based on Pitch Waveform Replication (PWR) and linear predictive coding (LPC). The estimated packet using LPC is better than that using PWR at a near boundary of the lost packet and the received one. On the other hand, the estimated packet using PWR is better than that using LPC at a distant boundary. Therefore, we combine the two estimated packets by considering the merits and demerits of PWR and LPC. Experimental results show that the proposed method provides better Perceptual Evaluation of Speech Quality (PESQ) scores than the conventional methods. Especially PESQ scores of the proposed method are remarkably excellent in the case of male voice.

关键词： Packet loss Speech Delays Jitter Internet linear predictive coding

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A 5.4 kbps speech coder based on multi-band excitation and linear predictive coding

A 5.4 kbps speech coder based on multi-band excitation and l...

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IEEE Region 10 International Conference TENCON

作者： H. Yang S.-N. Koh P. Sivaprakasapillai S. Doshita School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Information Science Faculty of Engineering Kyoto University Japan

Recent work has shown that multi-band excitation (MBE) is capable of synthesizing high quality speech in the range of 4.8-8.0 kbps. In this paper, the multi-band excitation and linear predictive coding (MBE-LPC) model, which used the LPC analysis to obtain the prediction residual, and uses the MBE model to estimate the residual spectrum is presented. Our motivation is to improve the excitation model of LPC vocoders in the frequency domain. Based on the MBE-LPC model, a 5.4 kbps speech coding is presented. An adaptive postfilter is used to improve the perceptual quality of the decoded speech. Informal listening tests show that the perceptual quality of the decoded speech of the proposed coder is better than that of the 4.15 kbps improved (IMBE) coder.< >

关键词： Speech coding linear predictive coding Speech synthesis Speech analysis Vocoders Signal synthesis Decoding predictive models Signal analysis Frequency estimation

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Improved linear predictive coding method for speech recognition

Improved linear predictive coding method for speech recognit...

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International Conference on Information, Communications and Signal Processing

作者： Jiang Hai Er Meng Joo School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)0780381858

In this paper, the improved linear predictive coding (LPC) coefficients of the frame are employed in the feature extraction method. In the proposed speech recognition system, the static LPC coefficients + dynamic LPC coefficients of the frame were employed as a basic feature. The framework of linear discriminant analysis (LDA) is used to derive an efficient and reduced-dimension speech parametric speech vector space for the speech recognition system. Using the continuous hidden Markov model (HMM) as the speech recognition model, the speech recognition system was successfully constructed. Experiments are performed on the isolated-word speech recognition task. It is found that the improved LPC feature extraction method is quite efficient.

关键词： linear predictive coding Speech recognition Feature extraction linear discriminant analysis Speech analysis Mel frequency cepstral coefficient Hidden Markov models Vectors Acoustic distortion Cepstral analysis

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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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Acoustic classification using linear predictive coding for wildlife detection systems

Acoustic classification using linear predictive coding for w...

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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Lacrimioara Grama Elena Roxana Buhuş Corneliu Rusu Signal Processing Group Technical University of Cluj-Napoca Cluj-Napoca ROMANIA

In this work we compare different classification algorithms applied on different number of features (linear predictive coding coefficients) in order to detect audio signals from wildlife areas. The final goal is to find the appropriate number of linear predictive coding coefficients to provide the desired accuracy for a certain framework. The experimental results prove that the best classifier is Logistic Model Trees regardless the number of features, having a constant classification accuracy greater than 95%. In the case of a reduced number of features, both Random Forest and Lazy IBk have good results; the classification accuracy is greater than 98%.

关键词： Classification algorithms Logistics linear predictive coding Feature extraction Birds Wildlife Vegetation

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The conversion between Immittance Spectral Pairs pseudo-cepstrum and linear predictive coding coefficients

The conversion between Immittance Spectral Pairs pseudo-ceps...

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International Conference on Test and Measurement (ICTM)

作者： Feng-lian Li Xue-ying Zhang College of Information Engineering Taiyuan University of Technology Taiyuan Shanxi China

ISBN: (纸本)9781424446995

In this paper, we derived a relationship between linear predictive coding (LPC) coefficients and Immittance Spectral Pairs (ISP) pseudo-cepstrum. In Adaptive Multi-Rate Wideband (AMR-WB) speech codec, ISP parameters were used for quantisation and interpolation. This paper first described the representation of ISP pseudo-cepstrum. ISP pseudo-cepstrum was defined as the inverse z-transform of half natural logarithm of immittance function. References [2] and [3] proposed two kinds of new perceptual weighting filters based on ISP pseudo-cepstrum. Spectral tilt is more pronounced in wideband signals due to the wide dynamic range between low and high frequencies. The proposed filters are helpful for solving this problem. Then we gave the conversion method between ISP pseudo-cepstrum and polynomials cepstrum coefficients. Finally we derived the recursive relations between ISP pseudo-cepstrum and LPC coefficients. There are two methods of converting LPC coefficients to ISP pseudo-cepstrum. The one is directly from LPC coefficients to ISP pseudo-cepstrum. The other is from ISP parameters.

关键词： linear predictive coding Wideband Filters Speech codecs Quantization Interpolation Dynamic range Frequency Polynomials Cepstrum

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System Status Feature Extraction Based on Recursive Lattice and Block linear predictive coding Algorithm

System Status Feature Extraction Based on Recursive Lattice ...

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American Control Conference (ACC)

作者： Wei Liu R. Doraiswami Department of Electrical Engineering University of New Brunswick Fredericton NB Canada

The control system status features are extracted by using fast recursive least-squares lattice (RLSL) and block linear predictive coding algorithm (LPCA), respectively. A short time record of control system signal is captured and the future trend of the system is predicted by analyzing the variations of the estimated auto regressive and moving average (ARMA) model parameters. The comparison of two methods applied in system performance monitoring with real-time data cases are given and their performance are evaluated.

关键词： Feature extraction Lattices linear predictive coding Prediction algorithms Control systems Control system synthesis Signal analysis predictive models System performance Monitoring

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