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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.


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TL;DR: An optimal linear coding solution for index coding problem is established and is proved to be the optimal solution from the linear perspective and can be easily utilize for any number of messages.
Abstract: An optimal linear coding solution for index coding problem is established Instead of network coding approach by focus on graph theoric and algebraic methods a linear coding program for solving both unicast and groupcast index coding problem is presented The coding is proved to be the optimal solution from the linear perspective and can be easily utilize for any number of messages The importance of this work is lying mostly on the usage of the presented coding in the groupcast index coding problem
Patent
24 Jul 2013
TL;DR: In this article, an audio coding device that uses a first channel, a second channel, and a plurality of channel prediction coefficients included in a code book, according to which predictive coding is performed on a third-channel signal, is presented.
Abstract: An audio coding device that uses a first-channel signal, a second-channel signal, and a plurality of channel prediction coefficients included in a code book, according to which predictive coding is performed on a third-channel signal, the first-channel signal, the second-channel signal, and the third-channel signal being included in a plurality of channels of an audio signal, the device includes, a determining unit that determines a distribution of error defined by a difference between the third-channel signal before predictive coding and the third-channel signal after predictive coding as a given curved surface according to the first-channel signal, the second-channel signal, and the third-channel signal before predictive coding; and a calculating unit that calculates channel prediction coefficients, included in the code book, that correspond to the first channel and the second channel from the code book.
Book ChapterDOI
01 Jan 1993
TL;DR: A comparison study of CELP coding of wideband speech of 200-3400 kHz and wideband (50-7000 kHz) speech at low bit-rates shows clear trends in quality and efficiency.
Abstract: Since its introduction in 1984, Code Excited Linear Predictive (CELP) [1] coding has received considerable attention for high quality speech coding at low bit-rates. Although most of the research has been focused on coding of narrowband (200-3400 kHz) speech, some recent studies on CELP coding of wideband (50-7000 kHz) speech have been reported [2], [3], [4].
Proceedings ArticleDOI
13 Apr 1994
TL;DR: A practical approach for developing a system incorporated with real-time speech compression are presented and a three-stage technique is used to simulate, evaluate, debug and implement the CCITT G.728 low delay code excited linear prediction (LD-CELP).
Abstract: A practical approach for developing a system incorporated with real-time speech compression are presented. This is a technique used in practice within the industrial sector for selecting and integrating DSP functionality into a large system. A three-stage technique is used to simulate, evaluate, debug and implement the CCITT G.728 low delay code excited linear prediction (LD-CELP) algorithm. In the first stage, the algorithm is evaluated via simulation to determine whether it meets the design criterion. Then, it is implemented in real-time based an object oriented approach. After the algorithm is thoroughly tested, it is further refined to obtain tighter and faster coding. This technique can be applied to other real-time DSP algorithms. >
Book ChapterDOI
01 Jan 2020
TL;DR: This work proposes a seven-stage audio classifier for voiced, unvoiced, transition, multi-speaker, silence, background noise and music signals using neural network by employing Levenberg Marquardt (LM) algorithm.
Abstract: Perceptual quality of audio signals at the receiver and transmission data rate are the major concerns for the speech codec developers. But both these parameters are inversely proportional in general. In the era of 4G, 3GPP launched Enhanced Voice Services (EVS) codec which can operate in multiple data rates with a six-stage speech classifier using threshold-based GMM statistical model. In this work, we propose a seven-stage audio classifier for voiced, unvoiced, transition, multi-speaker, silence, background noise and music signals using neural network by employing Levenberg Marquardt (LM) algorithm. In comparison with conventional statistical approach that requires determination of manual thresholds, the neural network method can simplify the categorization process especially while using a large number of parameters. The categorization is done by using extracted seven features that constitute to a 32-dimensional vector. TIMIT and NOIZEUS databases are used to generate the dataset and a classification accuracy of 94% is obtained. As the network model can perform efficiently using lesser number of neurons, the complexity is also less.

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