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检索条件"主题词=Linear predictive coding"

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A mobile hi-fi digital audio broadcasting scheme

A mobile hi-fi digital audio broadcasting scheme

引用

IEEE Conference on Vehicular Technology (VTC)

作者： L. Hanzo X. Lin R. Steele W.T. Webb Department of Electr. and Computer Science University of Southampton UK

The audio quality, robustness and complexity issues of a novel mobile digital audio broadcast (DAB) scheme are addressed. The audio codec is based on a combination of subband coding (SBC) and multipulse excited linear predictive coding (MPLPC), where the bit allocation is dynamically adapted according to both the signal power in different subbands and a perceptual hearing model. Typically a segmental signal to noise ratio (SEGSNR) in excess of 30 dB associated with high fidelity (hi-fi) subjective quality was achieved for 2.67 bits/sample transmissions at a mono bit rate of 86 kbits/s. Four different source-matched forward error correction (FEC) schemes were investigated in order to explore the complexity, bit rate and robustness trade-offs. When using 4 bit/symbol 16-level star-constellation quadrature amplitude modulation (16-StQAM) the overall signalling rate became approximately 30 kBaud, accommodating two stereo DAB channels in a conventional 200 kHz analogue FM channel's bandwidth. The diversity assisted DAB scheme required a channel signal to noise ratio (SNR) of about 25 dB for unimpaired audio quality via the worst-case Rayleigh fading mobile channel, when the mobile speed was 30 mph and the propagation frequency was 1.5 GHz. In case of the stationary Gaussian scenario an SNR of about 20 dB was required.< >

关键词： Digital audio broadcasting Signal to noise ratio Bit rate Forward error correction Robustness Codecs linear predictive coding Auditory system predictive models MONOS devices

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Degraded word recognition based on segmental signal-to-noise ratio weighting

Degraded word recognition based on segmental signal-to-noise...

引用

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

作者： H. Kobatake Y. Matsunoo Faculty of Technology Tokyo University of Agriculture슠and슠Technology Koganei Tokyo Japan

Distance measures robust against noise disturbances are required for reliable recognition of noisy speech. The local signal-to-noise ratio (SNR) of degraded speech varies in a wide range and the characteristics of speech with low SNR tend to be lost. Pattern matching, however, is performed uniformly without taking the local SNR of each analysis frame into account. The behavior of representative LPC distance measures versus segmental SNR is investigated, which shows the necessity of accounting for the effect of the segmental SNR on the distance measure. A double autocorrelation analysis is proposed as a spectrum estimation method. A pattern matching method is also introduced in which the segmental SNR is taken into account as a weight. Experiments of isolated word recognition were performed. The results show the effectiveness of the proposed method.< >

关键词： Degradation Signal to noise ratio Pattern matching Noise measurement Noise robustness Speech recognition Speech enhancement Pattern analysis Performance analysis linear predictive coding

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Spectral quantization of cepstral coefficients

Spectral quantization of cepstral coefficients

引用

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

作者： R. Hagen Department of Information Theory Chalmers University of Technology Gothenburg Sweden

Studies the cepstral coefficients as a suitable representation of the linear prediction filter for spectral coding purposes. Spectral coding methods in predictive speech coders are usually evaluated using the spectral distance measure. The average spectral distance combined with a measure of the percentage of spectra with high distortion are used to predict the perceptual quality when quantizing the prediction filter. The authors show that the spectral distance is equivalent to a squared error in the cepstral domain. Methods for spectral quantization using vector quantization of cepstral coefficients are analyzed. Better results than for quantization of line spectrum frequencies are reported for both single-stage VQ at 11-14 bits as well as 2-stage VQ at 18-22 bits. It is concluded that the cepstral coefficients are the right representation for LPC spectral coding purposes.< >

关键词： Cepstral analysis Speech coding Nonlinear filters linear predictive coding Information filtering Information filters Distortion measurement Vector quantization Frequency predictive models

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M-LCELP speech coding at 4 kbps

M-LCELP speech coding at 4 kbps

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

作者： K. Ozawa M. Serizawa T. Miyano T. Nomura Information Technology Research Laboratories NEC Corporation Limited Kawasaki Kanagawa Japan

This paper presents the M-LCELP (multi-mode learned code excited LPC) speech coder, which has been developed for the North American half-rate digital cellular systems. M-LCELP develops the following techniques to achieve high-quality synthetic speech at 4 kbps: (1) Multimode and multi-codebook coding, (2) Pitch lag differential coding with pitch tracking, (3) A two-stage joint design regular-pulse codebook with common phase structure in voiced frames, (4) An efficient vector quantization for LSP parameters, (5) An adaptive MA type comb filter to suppress excitation signal inter-harmonic noise. The MOS subjective test shows that 4.075 kbps M-LCELP synthetic speech quality is high, and that its quality is mostly equivalent to that for an 8 kbps North American full-rate VSELP coder.< >

关键词： Speech coding Adaptive filters Testing Decoding Information technology National electric code linear predictive coding Speech enhancement Vector quantization Phase noise

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Isolated word recognition using a hybrid neural network

Isolated word recognition using a hybrid neural network

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

作者： V. Tabarabaee B. Azimisadjadi S.B. Zahirazami C. Lucas Electron. Res. Center Sharif Univ. of Technol. Tehran Iran Electronic Research Center Sharif University of Technology Tehran Iran Department of Electrical Engineering University of Tehran Tehran Iran

A hybrid neural network is described. It consists of a Kohonen map and a perceptron. The hybrid is proposed firstly for speaker independent, isolated word recognition. However, it may also be used for other classification problems. The novel idea in this system is the usage of a Kohonen map as the feature extractor which converts phonetic similarities of the speech frames into spatial adjacency in the map. This property simplifies the classification task. The system performance was evaluated for recognition of a limited number of Farsi words (numbers "zero" through "ten"). The overall performance of the recognizer showed to be 93.82%.< >

关键词： Neural networks Speech recognition Feature extraction Neurons linear predictive coding Isolation technology Speech analysis System performance Oral communication Unsupervised learning

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A neural network-based text independent voice recognition system

A neural network-based text independent voice recognition sy...

引用

IEEE Southeastcon

作者： K. Kuah M. Bodruzzaman S. Zein-Sabatto Center for Neural Engineering Department of Electrical and Computer Engineering Tennessee State University Nashville TN USA

A text-independent voice recognition experiment was conducted using an artificial neural network. The speech data were collected from three different speakers uttering thirteen different words. Each word was repeated ten times. The speech data were then pre-processed for signal conditioning. A total of 12 feature parameters were obtained from Cepstral coefficients via a linear predictive coding (LPC). These feature parameters then served as inputs to the neural network for speaker classification. A standard two-layer feedforward neural network was trained to identify different feature sets associated with the corresponding speakers. The network was tested for the remaining unseen words in text-independent mode. The results were very promising with a voice recognition accuracy of more than 90%. The success rate could be increased by adding more utterances from each speaker.< >

关键词： Neural networks Speech recognition Text recognition linear predictive coding Cepstral analysis predictive models Humans Ear Signal analysis Resonance

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Vector quantization of harmonic magnitudes for low-rate speech coders

Vector quantization of harmonic magnitudes for low-rate spee...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： P. Lupini V. Cuperman School of Engineering Science Simon Fraser University Burnaby BC Canada

Several techniques for speech coding at rates of 4 kb/s and lower require quantization of spectral magnitudes at a set of frequencies which are harmonics of the fundamental pitch period of the talker (for example: multiband excitation coding, sinusoidal transform coding, and time-frequency interpolation). The number of harmonic magnitudes to be quantized depends on the fundamental frequency value and hence is variable, changing from frame to frame. The variable number of components to be quantized makes it difficult to use fixed-dimension vector quantization for harmonic magnitude encoding. In this paper, we introduce a quantization technique called non-square transform vector quantization (NSTVQ) which uses a fixed-dimension vector quantizer combined with a variable-size non-square transform which maps the variable-dimension harmonic magnitude vector into a fixed-dimension vector. The optimal reconstruction procedure for non-square transforms is derived and shown to be equivalent to an optimal least-square estimation procedure. The proposed technique is evaluated experimentally as part of a new coding system called spectral excitation coding (SEC). The results are compared to an existing technique which estimates the spectral shape using all-pole modeling followed by vector quantization of the LSP parameters.

关键词： Vector quantization Interpolation Encoding Frequency estimation Speech coding Transform coding Spectral shape Image reconstruction Time frequency analysis linear predictive coding

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Non-linear short-term prediction in speech coding

Non-linear short-term prediction in speech coding

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

作者： J. Thyssen H. Nielsen S.D. Hansen Tele Denmark Research Horsholm Denmark Electronics Institute Technical University of Denmark Lyngby Denmark

Addresses the question of how to extract the nonlinearities in speech with the prime purpose of facilitating coding of the residual signal in residual excited coders. The short-term prediction of speech in speech coders is extensively based on linear models, e.g. the linear predictive coding technique (LPC), which is one of the most basic elements in modern speech coders. This technique does not allow extraction of nonlinear dependencies. If nonlinearities are absent from speech the technique is sufficient, but if the speech contains nonlinearities the technique is inadequate. The authors give evidence for nonlinearities in speech and propose nonlinear short-term predictors that can substitute the LPC technique. The technique, called nonlinear predictive coding, is shown to be superior to the LPC technique. Two different nonlinear predictors are presented. The first is based on a second-order Volterra filter, and the second is based on a time delay neural network. The latter is shown to be the more suitable for speech coding applications.< >

关键词： Speech coding linear predictive coding Filters Data mining Information analysis Kernel predictive models predictive coding Delay effects Neural networks

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IEE Colloquium on 'Security and Cryptography Applications to Radio Systems' (Digest No.1994/141)

IEE Colloquium on 'Security and Cryptography Applications to...

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IEE Colloquium on Security and Cryptography Applications to Radio Systems

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Noisy adaptive cepstral coefficients and its application to noisy speech recognition

Noisy adaptive cepstral coefficients and its application to ...

引用

International Symposium on Speech, Image Processing and Neural Networks

作者： Lee-Min Lee Jen-Kwang Chen Hsiao-Chuan Wang Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)078031865X

The paper proposes a nonlinear cepstral equalization method for speech recognition. The method is based on AR modeling of clean speech power spectrum and the adding noise power to the speech. A noise ratio is introduced to provide a mechanism for adapting the reference template. An iterative algorithm is proposed to find a near optimal adaptation of reference cepstral parameters. Experiments showed that the proposed method is superior to the projection approach under severe noisy environment.< >

关键词： Cepstral analysis Speech recognition Speech enhancement Working environment noise Signal to noise ratio Noise level Vectors Testing linear predictive coding Speech analysis

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