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Code-excited linear prediction

About: Code-excited linear prediction is a research topic. Over the lifetime, 2025 publications have been published within this topic receiving 28633 citations. The topic is also known as: CELP.


Papers
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Journal ArticleDOI
TL;DR: This paper shows that the proposed method using a nonlinear model is especially effective in improving the efficiency of coding of ECGs since the improvement of the coding efficiency is at most 0.1 bit with ECG coding methods using linear transforms.
Abstract: In linear prediction coding methods for ECGs using linear autoregressive models, the prediction accuracy of QRS waves is poor, which is not improved even when the prediction degree is set higher than second or third degree. In this paper, this is attributed to the fact that a QRS wave is produced by a nonlinear occurrence mechanism and ECGs contain nonlinear components that cannot be predicted by linear models. A nonlinear prediction coding method for ECGs using a layered neural network or a Volterra functional series, such as are used frequently to identify nonlinear systems, is proposed as a nonlinear autoregressive model. The accuracy of prediction of QRS complex is improved by using a nonlinear model, the average code length in the bit rate region greater than 3 bits is improved by about 0.1 to 0.3 bit, and superior coding efficiency is realized. This paper shows that the proposed method using a nonlinear model is especially effective in improving the efficiency of coding of ECGs since the improvement of the coding efficiency is at most 0.1 bit with ECG coding methods using linear transforms, such as linear prediction, orthogonal wavelet transforms, and the like. © 2000 Scripta Technica, Syst Comp Jpn, 31(7): 66–74, 2000

1 citations

Proceedings ArticleDOI
15 May 1995
TL;DR: This paper discusses the use of artificial neural learning methods for low bit-rate speech compression, potentially in non-stationary environments and employs two unsupervised learning algorithms: frequency-sensitive competitive learning and Kohonen's self-organizing maps.
Abstract: In this paper, we discuss the use of artificial neural learning methods for low bit-rate speech compression, potentially in non-stationary environments. Unsupervised learning algorithms are particularly well-suited for vector quantization (VQ) which is used in many speech compression applications. We discuss two unsupervised learning algorithms: frequency-sensitive competitive learning and Kohonen's self-organizing maps which have both been investigated for learning the codebook vectors in an adaptive vector quantizer. In contrast with earlier work, we have employed these learning rules in VQ of the linear predictive coding (LPC) prediction residual. The performance of these unsupervised learning algorithms in speaker-dependent and speaker-independent speech compression are presented. Our results compare favourably with those of code-excited linear prediction (CELP) requiring reduced computational power with a tolerable reduction in speech quality. We also explore the effects of limited precision on classification and learning in competitive learning algorithms for low power VLSI implementations.

1 citations

09 Dec 1991
TL;DR: The authors present a novel modification of the C ELP architecture (fast vector quantisation CELP) which greatly reduces the computational complexity while maintaining good speech quality.
Abstract: The currently emerging generation of mobile radio systems employs digital speech transmission techniques to achieve higher system capacity than analogue systems. Therefore methods of transmitting high quality digital speech signals at low bit rates are of great interest in such applications. The CELP (code excited linear prediction) algorithm is a good basis for meeting this requirement. However, in its original form CELP is too computationally complex for implementation in portable equipment with current technology. The authors present a novel modification of the CELP architecture (fast vector quantisation CELP) which greatly reduces the computational complexity while maintaining good speech quality. A codec based on the new algorithm has been submitted as a candidate for the 1/2 rate GSM standard, which is intended to double the capacity of the GSM system by halving the gross bit rate used for speech transmission. >

1 citations

01 Jan 1991
TL;DR: This paper discusses some of the techniques that have been developed for adapting and coding the predictor coefficients in speech coders, and a number of new directions in the application of adaptive prediction in speech coding are discussed.
Abstract: Adaptive linear prediction is commonly used as a key step in digital coding of speech. This paper discusses some of the techniques that have been developed for adapting and coding the predictor coefficients in speech coders. The linear predictors in high quality speech coding often consist of two stages, a short-time span (formant) filter and a long-time span (pitch) filter. The use of such filters in analysis-by-synthesis coders is examined. In addition, backward adaptive strategies can be used to achieve high quality, low delay coding. The filters in these coders can be high-order (50 or more time lags) filters. Computational complexity and numerical stability of the algorithms is of prime concern for these filters. A number of new directions in the application of adaptive prediction in speech coding are also discussed. Keywords adaptive systems, prediction, speech analysis

1 citations

Dissertation
01 Jan 1990
TL;DR: This thesis aims to examine those factors which affect the quality and performance of low bit-rate coding algorithms for speech, based on linear prediction, operating between 4-16kb/s.
Abstract: This thesis aims to examine those factors which affect the quality and performance of low bit-rate coding algorithms for speech, based on linear prediction, operating between 4-16kb/s. While coding algorithms at 64kb/s and 32kb/s are now accepted CCITT standards, and a similar standard will be shortly adopted at 16kb/s, speech coding systems operating below these rates are not yet in wide-spread use, except for one or two specific systems such as GSM. Yet low bit-rate digital speech systems will become an essential part of many of the proposed mobile networks, based on both cellular and satellite technology. Of several possible candidates for low bit-rate applications, it is linear predictive coders that appear to offer the best in terms of quality and efficiency, and many developments, based on linear prediction, have been reported in the literature over the past twenty years. What is less clear is whether there is the potential for linear predictive coders to be developed further with better quality at even lower rates. This thesis sets out to examine some of those issues. The first part of the thesis develops a general theory for speech coding in terms of a hierarchical model of speech communication and identifies a dual function in the redundancies that exist at each layer of the hierarchical structure. The operation of linear predictive coding, in terms of this model is described, and it is shown that the limits to performance are determined by the ability of the encoder to efficiently transfer communication from a lower to a higher level in the hierarchy. The thesis then turns its attention towards the specific performance of linear prediction analysis on speech signals. It is shown that there is a limit to the performance that can be obtained with conventional linear prediction analysis due to the assumptions upon which the theory of linear prediction is based. A range of sub-classes of linear predictive coder are then compared in terms of the general model and the analysis procedures in the encoder stage are identified as being the key to coder performance. The central part of this thesis examines, specifically, a range of pitch determination algorithms which may be employed to enable accurate extraction of pitch correlations from the speech signal. A number of candidates are identified and compared. An investigation into the robustness of these algorithms to noisy speech is presented and a new highly robust algorithm is described. Finally, an investigation into robust linear prediction is reported. This falls into two parts - the performance of linear prediction on noisy speech and the performance of linear prediction during voiced speech. A range of methods for improving linear prediction during voiced speech are compared and the recently proposed method of Lee is examined in depth. Results of the application of Lee's method to speech coding is given and an improved version of the algorithm is described.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20226
20213
20207
201915
201810
201713