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检索条件"主题词=Linear predictive coding"

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A novel image edge detection method using linear Prediction

A novel image edge detection method using Linear Prediction

引用

Midwest Symposium on Circuits and Systems (MWSCAS)

作者： James Z. Zhang Peter C. Tay Robert D. Adams Department of Engineering and Technology The Kimmel School Western Carolina University Cullowhee NC USA

Traditionally, linear Prediction is used to predict future values of a signal using past values. The goal is to minimize prediction errors. In this paper, we propose a novel method of utilizing prediction errors to extract edges of images. In this method, smooth prediction errors are minimized while steep changes (larger errors) are amplified. Therefore, when applied to image edge detection, edge information can be accurately extracted. The proposed method is compared with predominant methods such as Sobel and Canny methods. While there is no mathematical proof that the proposed method outperforms predominant methods, however, examples presented in this paper may suggest that the proposed method may perform better for certain applications.

关键词： Image edge detection Data mining Nonlinear filters Computer errors linear predictive coding Equations Filtering Speech processing Feature extraction Text recognition

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Near optimal LQR performance for a compact set of plants

Near optimal LQR performance for a compact set of plants

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IEEE Conference on Decision and Control

作者： D.E. Miller Department of Electrical and Computer Engineering Waterloo ONT Canada

Here we consider the problem of providing near optimal performance for a large set of possible models. We adopt the LQR framework in the single-input single-output (SISO) setting, and prove that given a compact set of controllable and observable plant models of a fixed order, we can construct a single linear periodic controller (LPC) which provides near optimal LQR performance.

关键词： Uncertainty Optimal control Robust control linear predictive coding Adaptive control Control systems Programmable control Costs Transfer functions State feedback

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A 2.4 kbit/s MELP coder candidate for the new U.S. Federal Standard

A 2.4 kbit/s MELP coder candidate for the new U.S. Federal S...

引用

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

作者： A. McCree Kwan Truong E.B. George T.P. Barnwell V. Viswanathan Texas Instruments Personal Systems Laboratory Dallas TX USA Atlanta Signal Processors Inc. Atlanta GA USA

This paper describes our enhanced mixed excitation linear prediction (MELP) speech coder which is a candidate for the new U.S. Federal Standard at 2.4 kbits/s. The new coder is based on the MELP model, and it uses a number of enhancements as well as efficient quantization algorithms to improve performance while maintaining a low bit rate. In addition, the coder has been optimized for performance in acoustic background noise and in channel errors, as well as for efficient real-time implementation. Listening tests confirm that the enhanced 2.4 kbit/s MELP coder performs as well as the higher bit rate 4.8 kbit/s FS1016 CELP standard.

关键词： Code standards linear predictive coding Fourier series Filters Quantization Bit rate Performance evaluation Speech synthesis Signal processing algorithms Vocoders

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Audio Noise Classification using Bark scale features and K-NN Technique

Audio Noise Classification using Bark scale features and K-N...

引用

International Symposium on Communications and Information Technologies (ISCIT)

作者： Cherdchai Eamdeelerd Kraisin Songwatana Department of Telecommunication Engineering Faculty of Engineering King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

This paper presents the audio noise classification using Bark scale features and K-NN technique. This paper uses audio noise signal from NOISEX-92 (12 types). We determine the transfer functions from linear predictive coding (LPC) coefficient of noise signal on Bark scale and use K-NN technique to classify them. The results will be used for optimization of speech recognition model in the presence of noise. The highest average accuracy for audio noise classification is obtained when K=3 and median over 5 consecutive frames.

关键词： Working environment noise Speech recognition Feature extraction Speech analysis Speech enhancement linear predictive coding Background noise Signal analysis Electronic mail Transfer functions

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Speech compression based on exact modeling and structured total least norm optimization

Speech compression based on exact modeling and structured to...

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

作者： P. Lemmerling I. Dologlou S. Van Huffel Department of Electrical Engineering Katholieke Universiteit Leuven Leuven Belgium

We present a new speech coding algorithm, based on an all-pole model of the vocal tract. Whereas current autoregressive (AR) based modeling techniques (e.g. CELP, LPC-10) minimize a prediction error, which is considered to be the input to the all-pole model, our approach determines the closest (in L/sub 2/ norm) signal, which exactly satisfies an all-pole model. Each frame is then encoded by storing the parameters of the complex damped exponentials deduced from the all-pole model and its initial conditions. Decoding is performed by adding the complex damped exponentials based on the transmitted parameters. The new algorithm is demonstrated on a speech signal. The quality is compared with that of a standard coding algorithm at comparable compression ratios, by using the segmental signal-to-noise ratio (SNR).

关键词： Mathematical model predictive models linear predictive coding Speech coding Vocoders White noise Vectors Speech synthesis Integrated circuit modeling Laboratories

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The application of a functional perceptual model of speech to variable-rate LPC systems

The application of a functional perceptual model of speech t...

引用

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

作者： R. Viswanathan J. Makhoul R. Wicke Bolt Beranek and Newman 슠Inc. Cambridge MA USA

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Speaker-independent vowel classification using hidden Markov models and LVQ2

Speaker-independent vowel classification using hidden Markov...

引用

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

作者： D.G. Kimber M.A. Bush G.N. Tajchman Xerox Palo Alto Research Center Palo Alto CA USA

A set of experiments in which the LVQ2 (learning vector quantization) algorithm of T. Kohonen et al (Proc. 1988 IEE Int. Conf. on Neural Networks, p.I-61-68, 1988) is used to generate vector codebooks for a discrete-observation hidden Markov model (HMM) classifier is described. Input feature vectors consist of single-frame linear predictive coding (LPC)-based cepstra and/or differenced cepstra. Classification accuracies using conventional k-means, class-specific k-means, and LVQ2 codebooks are compared for a 16-way speaker-independent vowel classification task. In contrast to speaker-dependent phonetic classification results previously published, no significant performance advantages are observed with LVQ2. These conflicting results are discussed relative to differences in the recognition tasks and the feature sets used. It is also argued that the single-observation Bayesian decision boundaries approximated by LVQ2 are nonoptimal for HMM-based classification involving multiple observations.< >

关键词： Hidden Markov models Vector quantization Neural networks linear predictive coding Speech recognition Bayesian methods

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Frame level entropy based overlapped speech detection as a pre-processing stage for speaker diarization

Frame level entropy based overlapped speech detection as a p...

引用

IEEE Workshop on Machine Learning for Signal Processing

作者： Oshry Ben-Harush Hugo Guterman Itshak Lapidot Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beersheba Israel Department of Elect and Electronics Engineering Sami Shamoon College of Engineering Ashdod Israel

Speaker diarization systems attempt to assign temporal speech segments in a conversation to the appropriate speaker, and non-speech segments to non-speech. Speaker diarization systems basically provide an answer to the question "Who spoke when ?". One inherent deficiency of most current systems is their inability to handle co-channel or overlapped speech. During the past few years, several studies have attempted dealing with the problem of overlapped or co-channel speech detection and separation, however, most of the algorithms suggested perform under unique conditions, require high computational complexity and require both time and frequency domain analysis of the audio data. In this study, frame based entropy analysis of the audio data in the time domain serves as a single feature for an overlapped speech detection algorithm. Identification of overlapped speech segments is performed using Gaussian Mixture Modeling (GMM) along with well known classification algorithms applied on two speaker conversations. By employing this methodology, the proposed method eliminates the need for setting a hard threshold for each conversation or database. LDC CALLHOME American English corpus is used for evaluation of the suggested algorithm. The proposed method successfully detects 60.0% of the frames labeled as overlapped speech by the baseline (ground-truth) segmentation , while keeping a 5% false-alarm rate.

关键词： Entropy Speech analysis Automatic speech recognition Detection algorithms Speech processing Hidden Markov models Autocorrelation linear predictive coding Educational institutions Computational complexity

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4800 and 7200 bit/sec hybrid codebook multipulse coding

4800 and 7200 bit/sec hybrid codebook multipulse coding

引用

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

作者： R.L. Zinser S.R. Koch General Electric Corporate Research and Development Center Schenectady NY USA

The authors describe a low-complexity coding technique that combines multipulse and stochastic excitation. The system, known as hybrid multipulse coding (HMC), provides good quality at 4.8 and 7.2 kb/s. HMC uses efficient pulse excitation for voiced speech and stochastic excitation for unvoiced speech. For the best speech quality, HMC uses an optimization algorithm for simultaneous solution of pitch predictor and excitation parameters, producing a higher signal-to-noise ratio than CELP (code-excited linear prediction) in the 4.8-kb/s configuration. At 7.2 kb/sec, CELP and HMC give diagnostic acceptability measure (DAM) scores close enough so that their standard error limits overlap. In all configurations, HMC requires from 5 to 14 times fewer multiply/accumulate operations than CELP for similarly sized codebooks.< >

关键词： Stochastic processes Acoustic noise Speech coding Signal synthesis Speech synthesis Research and development Signal to noise ratio predictive coding linear predictive coding Computational complexity

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Wavelet based feature extraction for phoneme recognition

Wavelet based feature extraction for phoneme recognition

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International Conference on Spoken Language, ICSLP

作者： C.J. Long S. Datta Department of Electronic and Electrical Engineering Loughborough University of Technology Loughborough UK

In an effort to provide a more efficient representation of the acoustical speech signal in the pre classification stage of a speech recognition system, we consider the application of the Best-Basis Algorithm of R.R. Coifman and M.L. Wickerhauser (1992). This combines the advantages of using a smooth, compactly supported wavelet basis with an adaptive time scale analysis, dependent on the problem at hand. We start by briefly reviewing areas within speech recognition where the wavelet transform has been applied with some success. Examples include pitch detection, formant tracking, phoneme classification. Finally, our wavelet based feature extraction system is described and its performance on a simple phonetic classification problem given.

关键词： Feature extraction Multiresolution analysis Continuous wavelet transforms Discrete wavelet transforms Speech recognition Wavelet analysis Wavelet transforms Time frequency analysis Signal analysis linear predictive coding

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