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检索条件"主题词=Linear predictive coding"

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Leak monitoring system for gas pipelines

Leak monitoring system for gas pipelines

引用

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

作者： I. Brodetsky M. Savic Electrical Computer and System Engineering Department Signal and Speech Research Laboratory Rensselaer Polytechnic Institute Troy NY USA

An approach and a solution to the continuous leak monitoring problem in underground gas pipelines are presented. This approach places permanent monitoring units along the pipeline. These units detect acoustic signals in the pipeline and discriminate leak sounds from other man-made or natural nonleak sounds that can occur. The system uses the kNN classifier as the detector with LPC (linear predictive coding) cepstrums as signal features. To increase system performance, pipeline effects on acoustic signals were taken into account during the classifier training phase. Each unit can detect 1/4-in-diameter leaks from a distance of 300 m, yielding 600 m as the maximum distance between units.< >

关键词： Monitoring Pipelines Acoustic signal detection Leak detection linear predictive coding Signal detection Neural networks Detectors Cepstrum System performance

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Snap-drift learning for phrase recognition

Snap-drift learning for phrase recognition

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Sin Wee Lee D. Palmer-Brown Computational Intelligence Group School of Computing Leeds Metropolitan University Leeds UK School of Computing and Technology University of East London London UK

This paper presents a new application of the snap-drift algorithm by S. W. Lee, et al. (2004): phrase recognition using a set of phrases from the Lancaster parsed corpus (LPC) by R. Garside, et al. (1987). The learning algorithm is the classifier version of snap-drift. In this version, along with the complementary concepts of fast minimalist learning (snap) and slow drift towards the input pattern, each node of the snap-drift neural network (SDNN) swaps between snap and drift modes when declining performance is indicated on that particular node. This method enables the SDNN to learn at node level, in the sense that each node has its learning mode toggled independently of the other nodes. Learning on each node is also reinforced by enabling learning with a probability that decreases with increasing performance. The simulations demonstrate that learning is stable, and the results have consistently shown similar classification performance and advantages in terms of speed in comparison with a multilayer perceptron (MLP) and back-propagation by J. Topper, et al. (2002), D. E. Rumelhart, et al. (1986) applied to the same problem.

关键词： Subspace constraints Neural networks linear predictive coding Resonance Fuzzy set theory Computer network management Speech Computational modeling Silicon compounds Computational intelligence

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Speaker verification using temporal decorrelation post-processing

Speaker verification using temporal decorrelation post-proce...

引用

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

作者： L.P. Netsch G.R. Doddington Computer Science Laboratory Texas Instruments Inc. Dallas TX USA SRI International Inc. Menlo Park CA USA

A text-dependent method of speaker verification processing which utilizes the statistical correlation between measured features of speech across whole words is described. The correlation is used in a linear discriminant analysis to define uncorrelated world-level features as a metric. Initial results indicate that this method can significantly reduce the amount of storage necessary for speaker-specific speech information. Furthermore, this method provides promise of improved verification performance compared to methods based on hidden Markov model (HMM) state level observation metrics. Since the linear discriminant analysis yields features which are decorrelated over entire words, this method should be more robust to signal distortions which are consistent over the entire utterance.< >

关键词： Decorrelation Hidden Markov models Speech processing Vectors Statistics Telephone sets Feature extraction linear predictive coding Filter bank Nonlinear filters

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Finite-state vector quantization over noisy channels and its application to LSP parameters

Finite-state vector quantization over noisy channels and its...

引用

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

作者： Y. Hussain N. Farvardin Electrical Engineering Department and Systems Research Center University of Maryland College Park MD USA

The authors describe a noisy channel finite-state vector quantizer (FSVQ) for quantizing speech line spectrum pair (LSP) parameters. First they show that under noiseless channel conditions, the ordinary FSVQ of J. Foster et al (1985), when used to encode the LSP parameters, achieves a 1-dB average spectral distortion at 24 b/frame; each LSP vector is split into three subvectors and an eight-state FSVQ is used for each subvector. However, when channel noise is present, the ordinary FSVQ performance degrades drastically. Therefore, the authors describe a modified FSVQ system that is designed taking into account the channel noise. It is shown by means of simulations that the new system is robust to channel noise and outperforms the channel-optimized VQ and the channel-matched multistage VQ of N. Phamdo et al, (1990) by saving 1.5-4 b/vector, depending on the level of noise in the channel.< >

关键词： Vector quantization Noise robustness Degradation Bit rate Filters Noise level Speech enhancement linear predictive coding Distortion measurement Educational institutions

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A vector quantizer incorporating both LPC shape and energy

A vector quantizer incorporating both LPC shape and energy

引用

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

作者： L. Rabiner M. Sondhi S. Levinson Bell Laboratories Murray Hill NJ USA

The theory of vector quantization (VQ) of linear predictive coding (LPC) coefficients has established a wide variety of techniques for quantizing LPC spectral shape to minimize overall spectral distortion. Such vector quantizers have been widely used in the areas of speech coding and speech recognition. The conventional vector quantizer utilizes only spectral shape information and essentially disregards the energy or gain term associated with the optimal LPC fit to the signal being modelled. In this paper we present a method of incorporating LPC spectral shape and energy into the codebook entries of the vector quantizer. To do this we postulate a distortion measure for comparing two LPC vectors which uses a weighted sum of an LPC shape distortion and a log energy distortion. Based on this combined distortion measure we have designed and studied vector quantizers of several sizes for use in isolated word speech recognition experiments. We have found that a fairly significant correlation exists between LPC shape and signal energy; hence a combined LPC shape plus energy vector quantizer with a given distortion requires far fewer codebook entries than one in which LPC shape and energy are quantized separately. Based on isolated word recognition tests on both a 10-digit and a 129 word airlines vocabulary, we have found improvements in recognition accuracy by using the VQ with both LPC shape and energy over that obtained using a VQ with LPC shape alone.

关键词： linear predictive coding Distortion measurement Spectral shape Speech recognition Shape measurement Vector quantization Speech coding Energy measurement Size measurement Testing

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Improved phonetically-segmented vector excitation coding at 3.4 kb/s

Improved phonetically-segmented vector excitation coding at ...

引用

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

作者： S. Wang A. Gersho Center for Information Processing Research Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

Several major modifications to the phonetically segmented vector excitation coding (PS-VXC) coder by the authors (1989, 1990) reported previously have resulted in enhanced speech quality while reducing the delay, complexity, and bit rate. Speech is segmented into variable-length phonetic classes and a VXC coding module is tailored to each class. coding techniques include adaptive linear predictive coding (LPC) analysis and interpolation, two-stage excitation coding of onsets, comb filtering, modified perceptual weighting, and pitch contour smoothing. The improved PS-VXC coder operates at a peak rate of 3.4 kb/s with an average rate of 3.0 kb/s and has a subjective performance closely matching that of the 4.8 kb/s DoD CELP coder.< >

关键词： Speech coding linear predictive coding Vectors Speech enhancement Delay Bit rate Interpolation Adaptive filters Nonlinear filters Filtering

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Speaker-independent French digits recognition using word-based vector quantization and hidden Markov models

Speaker-independent French digits recognition using word-bas...

引用

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

作者： A. Tassy L. Miclet MATRA sa. Yvelines France Dpt. SYC UA CRNS 820 ENST Paris France

Vector Quantization has recently been used in the realization of a speaker-independent digit recognizer, based uniquely on the spectral content of the speech signal. On the other hand, the Hidden Markov Models proved their ability in modelling temporal distortions between different utterances of a word pronounced by several speakers. In term of recognition rate, HMMs are as efficient as the conventional DTW matching, but they need less computation and memory. This paper presents a speaker-independent digit recognition system that combines word-based VQ with HMM, the cost of which is low enough to be implemented on a single signal processor available today. It is the first result of a cooperation project between ENST and the MATRA company, financially supported by the French government. The proposed recognizer is structured in two parts. First, a VQ-preprocessor, with one vector codebook per vocabulary word, performs a coding of the short-time spectrum of the speech signal and realizes an initial sorting. Then HMMs are used to take the final recognition decision.

关键词： Vector quantization Hidden Markov models Autocorrelation Speech Training data Vocabulary Computational efficiency linear predictive coding Distortion Gaussian distribution

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Baseband LPC coders for speech transmission over 9.6 kb/s noisy channels

Baseband LPC coders for speech transmission over 9.6 kb/s no...

引用

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

作者： R. Viswanathan A. Higgins W. Russell J. Makhoul Bolt Beranek and Newman Inc. Cambridge Mass. Bolt Beranek and Newman 슠Inc. Cambridge MA USA

This paper presents the results of our investigation of the various aspects of baseband LPC coders with the goal of maximizing the speech quality at a transmission bit-rate of 9.6 kb/s and for channel bit-error rates of up to 1%. Important among these aspects are: baseband width, coding of baseband, high-frequency regeneration, and error protection of important transmission parameters. The paper discusses these and other issues, presents the results of speech-quality tests conducted during the various stages of optimization, and describes the details of the optimized speech coder.

关键词： Baseband linear predictive coding Speech Signal processing Protection Bandwidth Data mining Phase change materials Fasteners Testing

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Improvement of speech enhancement techniques for robust speaker identification in noise

Improvement of speech enhancement techniques for robust spea...

引用

International Conference on Computer and Information Technology (ICCIT)

作者： Md. Rabiul Islam Md. Fayzur Rahman Muhammad Abdul Goffar Khan Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Bangladesh

This paper presents an approach of speech enhancement techniques to improve the performance of the robust speaker identification under noisy environments. Start-end points detection, silence part removal, frame segmentation and windowing technique have been used to pre-process and Wiener filter has been used to remove the silence parts from the speech utterances. To extract the features from the speech various speech parameterization techniques that is LPC, LPCC, RCC, MFCC, ¿MFCC and ¿¿MFCC have been simulated. Finally, to measure the performance of the proposed speech enhancement techniques, genetic algorithm has been used as a classifier for the noise robust automated speaker identification system and various experiments have performed on genetic algorithm to select the optimum parameters. According to the NOIZEOUS speech database, the highest identification rate of 70.31 [%] for text-dependent and of 61.26 [%] for text-independent speaker identification system have been achieved.

关键词： Speech enhancement Noise robustness Working environment noise Genetic algorithms Wiener filter Feature extraction linear predictive coding Mel frequency cepstral coefficient Noise measurement Performance evaluation

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Determination of the LPC coefficients using neural networks

Determination of the LPC coefficients using neural networks

引用

International Conference on Neural Networks

作者： I.I. Ibrahim M.I. El-Adawy Electronics and Communications Department Faculty of Engineering and Technology Cairo Egypt Helwan University Cairo EG Dept. of Electron. & Commun. Helwan Univ. Cairo Egypt

A new technique for the determination of the LPC coefficients is introduced. In this method the LPC coefficients are shown to be the weighting factors of a simple neuron. This makes this technique suitable for a lot of applications that uses LPC as a signal processing tool like speech processing and production. The error introduced by this method is comparable to the error obtained by the conventional methods like the autocorrelation method.

关键词： linear predictive coding Neural networks Differential equations Neurons Autocorrelation Signal processing algorithms Testing Vectors Nonlinear equations Speech processing

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