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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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Proceedings ArticleDOI
09 May 1995
TL;DR: Test results of LPC quantization are described for a variable rate phonetically segmented CELP coder and for the synthesis of speech from the prediction residual.
Abstract: Variable rate quantization of the linear predictive coding (LPC) parameters based on phonetic classification of the speech frame results in substantial performance gain. Speech frames are classified as unvoiced or voiced and are separately quantized with VQ codebooks designed for each class. Performance results, including listening tests, show that for transparent quality roughly 9 bits is sufficient for unvoiced frames and 24 bits for voiced frames. Test results of LPC quantization are described for a variable rate phonetically segmented CELP coder and for the synthesis of speech from the prediction residual.

14 citations

Proceedings ArticleDOI
07 Nov 2002
TL;DR: This work proposes a novel LSP-based multiple-description coding method that adapts its number of descriptions to network loss conditions in order to conceal packet losses in transmitting low-bit-rate coded speech over lossy packet networks.
Abstract: A fundamental issue in real-time interactive voice transmissions over unreliable IP networks is the loss or late arrival of packets for playback. Such losses cannot be recovered by retransmissions due to tight time constraints in interactive applications. This problem is especially serious in transmitting low bit-rate coded speech when pervasive dependencies are introduced in a bit stream, leading to the loss of subsequent dependent frames when a single packet is lost or arrives late. We propose a novel LSP-based multiple-description coding method that adapts its number of descriptions to network loss conditions in order to conceal packet losses in transmitting low-bit-rate coded speech over lossy packet networks. Based on high correlations observed in linear predictor parameters, in the form of line spectral pairs (LSPs), of adjacent frames, we generate multiple descriptions in a sender by interleaving LSPs, and reconstruct lost LSPs in a receiver by linear interpolations. Without increasing the transmission bandwidth, our scheme represents a trade-off between the quality of received packets and the ability to reconstruct lost packets. Our experimental results on FS CELP show good performance.

14 citations

Proceedings ArticleDOI
27 Apr 1993
TL;DR: Experimental data show that an appropriately chosen window can reduce the time delay by up to 50% of the analysis frame, and that in many algorithms it can be implemented in the coder without affecting compatibility with previous versions of the same algorithm.
Abstract: The behavior of several windows in relation to time delay and spectral estimation is analyzed in its application to CELP (code excited linear prediction) coders, with emphasis on time delay considerations. From the data, the importance of asymmetric windows in such a context becomes evident. In order to obtain objective conclusions, a perceptually weighted residual energy was used as the comparison criterion. Experimental data show that an appropriately chosen window can reduce the time delay by up to 50% of the analysis frame. Typical reductions in delay will be about 6-8 ms. It is observed that this delay reduction is obtained with no cost in complexity or speech quality, and that in many algorithms it can be implemented in the coder without affecting compatibility with previous versions of the same algorithm. As an example of a specific implementation, an asymmetric window has been introduced in the Federal Standard 1016 CELP algorithm (4.8 kbit/s CELP). It has been shown to reduce the implicit delay of that algorithm by 5.6 ms with no loss in quality. >

14 citations

Patent
21 Dec 2000
TL;DR: In this article, a method and system for improving performance of an echo canceller with low additional complexity is presented, which discloses using information from internal variables of Code Excited Linear Prediction (CELP) based codecs in a digital communication network to significantly improve the rate of convergence of the echo cancellation.
Abstract: A method and system for improving performance of an echo canceller with low additional complexity. Specifically, the present invention discloses using information from internal variables of Code Excited Linear Prediction (CELP) based codecs in a digital communication network to significantly improve the rate of convergence of the echo canceller. In one embodiment of the present invention, an error signal associated with a voice signal is filtered by a transversal filter using Linear Predictive Coefficients (LPC) coefficients to provide filter transfer functions for a Filtered-X Least Mean Squares algorithm. Additionally, an adaptive filter applies the Filtered-X Least Mean Squares algorithm using the pre-filtered voice signal available in a CELP-based decoder to create a synthetic echo signal which is subtracted from the echo signal for attenuation.

14 citations

Proceedings ArticleDOI
14 Apr 1991
TL;DR: An algorithm is derived which offers considerable computational savings over previously proposed codebook methods and is shown how a modified Hopfield neural net is capable of searching such a codebook and selecting a near-optimum code vector.
Abstract: A ternary excitation codebook with a special structure for CELP (code-excited linear prediction) coding has been devised in which the main codebook is divided into several sub-codebooks. It is shown how a modified Hopfield neural net is capable of searching such a codebook and selecting a near-optimum code vector. Hence an algorithm is derived which offers considerable computational savings over previously proposed codebook methods. Results are presented demonstrating the performance of the net and the quality of the coded speech. >

13 citations


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