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About 1,544 papers for “Linear predictive coding”

Linear Predictive Coding

Muhammad Adil Raja · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2016

Cited by 5Open access

Linear Predictive Coding

A. El-Jaroudi · 2003

Abstract Most real‐world signals carry redundant information from one sample (or snapshot) to the next. The focus of this article is on the use of LP in redundancy removal or data compression for coding applications (also known as linear predictive coding (LPC)). The remainder of the article is organized as follows: LP is formulated and the optimal prediction parameters are derived; the computational aspects and algorithms for the implementation of LP are explored; examples and applications of LPC are presented, and finally, variations on LPC used for coding speech signals are discussed.

Cited by 3

Linear predictive speech coding arrangement

John G. Ackenhusen · The Journal of the Acoustical Society of America · 1990

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation John G. Ackenhusen; Linear predictive speech coding arrangement. J. Acoust. Soc. Am. 1 December 1990; 88 (6): 2913. https://doi.org/10.1121/1.399636 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAcoustical Society of AmericaThe Journal of the Acoustical Society of America Search Advanced Search |Citation Search

Cited by 0

Linear Predictive Coding for Speech Compression

Yasir Saleem, Faisal Hayat, Arslan Iqbal · SHILAP Revista de lepidopterología · 2014

Telecommunication industry is growing and different services are rapidly introduced by different competitors to attract the users. Speech communication and its quality conservation is the most prevalent and common service provided by almost all companies. The objective of this project is the development of a LPC (Linear Predictive Coding) based voice coder. Attributes for speech like pitch, voiced and unvoiced decision and silence were extracted and speech was modeled using LDR (Levinson Durbin Recursion) and SDA (Steepest Descent Algorithm). LPC filter is analyzed and its model is implemented. LPC's different attributes complexity, delay and bitrate are deliberated and tradeoffs are highlighted. The results were analyzed and quality of speech was determined using spectrograph and by listening to the synthesized speech. At the end quality of original and synthesized speech is discussed and shown graphically and a soft comparison between both above mentioned technique is also added.

Cited by 0Open access

Analysis by synthesis linear predictive coding.

Kwan Yee. Lee · Surrey Research Insight Open Access (The University of Surrey) · 1990

Applications such as satellite and digital mobile radio systems (DMR) have gained widespread acceptance in recent years, and efficient digital processing techniques are gradually replacing the older analogue systems. An important subsystem of these applications is voiceband communication, especially digital speech encoding. Digital encoding of speech has been a focus of speech processing research for many years, and recently this activity together with the rapid advances in digital hardware, has begun to produce realistic working algorithms. This is typified by the Pan-European DMR system which operates at 13Kbit/s. For applications operating below this coding capacity, sophisticated algorithms have been developed. A particular class of these, termed Analysis-by-Synthesis Linear Predictive Coding (ABS-LPC), has been a subject of active world-wide research. In this thesis, ABS-LPC algorithms are investigated with particular emphasis on the Code-Excited Linear Predictive coding (CELP) variant. The aim of the research is to produce high communication quality speech at 8Kbit/s and below by considering aspects of quantisation, computational complexity and robustness. The ABS-LPC algorithms operate by exploiting short-term and long-term correlations of speech signals. Line Spectral Frequency (LSF) representation of the short-term correlation is examined and various LSF derivations and quantisation procedures are presented. The variants of ABS-LPC are compared for their advantages and disadvantages to determine an algorithm suitable for in-depth analysis. The particular chosen variant, CELP, was pursued. A study on the importance of the long-term prediction, and the simplification of CELP without sacrificing speech quality is presented. The derived alternative approaches for the computation of the long-term predictor and the filter excitation have enabled the previously unpractical CELP algorithm to produce high communication quality speech at rates below 8Kbit/s, and yet remain implement able in real-time on a single chip. Refinements of the CELP algorithm followed in order to improve the coder towards higher speech quality at 4.8Kbit/s and below. This involved the examination of the weaknesses of the basic CELP algorithm, and alternative strategies to overcome these limitations are presented.

Cited by 5

Predictive Coding for Locally-Linear Control

Rui Shu, Tung Nguyen, Yinlam Chow et al. · arXiv (Cornell University) · 2020

High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks. The Learning Controllable Embedding (LCE) framework addresses these challenges by embedding the observations into a lower dimensional latent space, estimating the latent dynamics, and then performing control directly in the latent space. To ensure the learned latent dynamics are predictive of next-observations, all existing LCE approaches decode back into the observation space and explicitly perform next-observation prediction---a challenging high-dimensional task that furthermore introduces a large number of nuisance parameters (i.e., the decoder) which are discarded during control. In this paper, we propose a novel information-theoretic LCE approach and show theoretically that explicit next-observation prediction can be replaced with predictive coding. We then use predictive coding to develop a decoder-free LCE model whose latent dynamics are amenable to locally-linear control. Extensive experiments on benchmark tasks show that our model reliably learns a controllable latent space that leads to superior performance when compared with state-of-the-art LCE baselines.

Cited by 4Open access

Linear Predictive Coding Of Myoelectric Signals

Elias S. G. Carotti, Juan Carlos De Martin, Dario Farina et al. · 2006

Despite the great interest towards long term recordings of electromyographic (EMG) signals, which find applications, for example, in telemedicine, only a few studies have dealt with the compression of these signals. We propose a lossy coding technique for surface EMG signals. The technique is based on the linear predictive coding paradigm widely used for speech compression. The algorithm was tested on both simulated and experimental signals. Mean frequency, median frequency, variance, skewness and kurtosis of the EMG signals were preserved with an error less than 3% with respect to the original values for synthetic signals and experimental signals, reducing the bitrate from 24 kbit/s (12 kbit/s after downsampling) to 352 bit/s, with a compression factor of 97.1%. It was concluded that the linear predictive coding paradigm can be effectively used for high rate compression of surface EMG signals when preservation of only the power spectrum of the signal is of interest. This has applications in ergonomics and occupational medicine.

Cited by 16

Sheaf Cohomology of Linear Predictive Coding Networks

Jeffrey Seely · ArXiv.org · 2025

Predictive coding (PC) replaces global backpropagation with local optimization over weights and activations. We show that linear PC networks admit a natural formulation as cellular sheaves: the sheaf coboundary maps activations to edge-wise prediction errors, and PC inference is diffusion under the sheaf Laplacian. Sheaf cohomology then characterizes irreducible error patterns that inference cannot remove. We analyze recurrent topologies where feedback loops create internal contradictions, introducing prediction errors unrelated to supervision. Using a Hodge decomposition, we determine when these contradictions cause learning to stall. The sheaf formalism provides both diagnostic tools for identifying problematic network configurations and design principles for effective weight initialization for recurrent PC networks.

Cited by 0Open access

Efficient implementation of linear predictive coding algorithms

S. Nagarajan, Ravi Sankar · 2002

In linear predictive coding (LPC) analysis, the linear predictors are computed using the classical Levinson-Durbin algorithm. But the Levinson-Durbin algorithm is a processing bottleneck as it involves the addition of inner products in the calculation of the reflection coefficients. This paper develops and reports on modifications to the algorithm used in various speech processing and coding applications for efficient implementations.

Cited by 8

Linear Predictive Coding on the ADSP-2105

Timothy Newsham · 2007

An implemenation of Linear Predictive Coding, a low-bandwidth speech encoding scheme, built around the ADSP-2105 signal processing CPU is described. The hardware schematics and software source code listing are included. 1 Motivation Often time it is necessary to carry voice communiations over a digital channel. For ordinary "toll-quality" communications voice is usually sent by sampling the signal at 8Khz into 8 bit words. This results in a bit rate of 64,000 bits per second. Waveform encoding techniques such as ADPCM can reduce this bit rate typically to 32000 or 24000 bits per second without much noticeable loss of quality. In some situations it is desireable to send speech over a much smaller digital channel. Speech coders take advantage of the characteristics of speech to encode speech in a much smaller bandwidth. Because speech coders are designed specifically for speech communications they do not handle non-speech data such as music well. Code Excited Linear Prediction or CELP c...

Cited by 0

Multichannel linear predictive coding of color images

Petros Maragos, R. Mersereau, Ronald W. Schafer · DSpace - NTUA (National Technical University of Athens) · 2005

This paper reports on a preliminary study of applying single-channel (scalar) and multichannel (vector) 2-D linear prediction to color image modeling and coding. Also, the novel idea of a multi-input single-output 2-D ADPCM coder is introduced. The results of this study indicate that texture information in multispectral images can be represented by linear prediction coefficients or matrices, where as the prediction error conveys edge-information. Moreover, by using a single-channel edge-information we obtained, from original color images of 24 bits/pixel, reconstructed images of good quality at information rates of 1 bit/pixel or less.

Cited by 2Open access

Multichannel Linear Predictive Coding of Color Images,

Petros Maragos, R.M. Mersereau, Ralf Schäfer · 1984

This paper reports on a preliminary study of applying single-channel (scalar) and multichannel (vector) 2-D linear prediction to color image modeling and coding. Also, the novel idea of a multi-input single-output 2-D ADPCM coder is introduced. The results of this study indicate that texture information in multispectral images can be represented by linear prediction coefficients or matrices, where as the prediction error conveys edge-information. Moreover, by using a single-channel edge-information we obtained, from original color images of 24 bits/pixel, reconstructed images of good quality at information rates of 1 bit/pixel or less.

Cited by 2