Source and Channel Coding for Speech Transmission and Remote Speech Recognition
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An Introduction to Mpeg-4 Audio Lossless Coding
« ¬ AN INTRODUCTION TO MPEG-4 AUDIO LOSSLESS CODING Tilman Liebchen Technical University of Berlin ABSTRACT encoding process has to be perfectly reversible without loss of in- formation, several parts of both encoder and decoder have to be Lossless coding will become the latest extension of the MPEG-4 implemented in a deterministic way. audio standard. In response to a call for proposals, many com- The MPEG-4 ALS codec uses forward-adaptive Linear Pre- panies have submitted lossless audio codecs for evaluation. The dictive Coding (LPC) to reduce bit rates compared to PCM, leav- codec of the Technical University of Berlin was chosen as refer- ing the optimization entirely to the encoder. Thus, various encoder ence model for MPEG-4 Audio Lossless Coding (ALS), attaining implementations are possible, offering a certain range in terms of working draft status in July 2003. The encoder is based on linear efficiency and complexity. This section gives an overview of the prediction, which enables high compression even with moderate basic encoder and decoder functionality. complexity, while the corresponding decoder is straightforward. The paper describes the basic elements of the codec, points out 2.1. Encoder Overview envisaged applications, and gives an outline of the standardization process. The MPEG-4 ALS encoder (Figure 1) typically consists of these main building blocks: • 1. INTRODUCTION Buffer: Stores one audio frame. A frame is divided into blocks of samples, typically one for each channel. Lossless audio coding enables the compression of digital audio • Coefficients Estimation and Quantization: Estimates (and data without any loss in quality due to a perfect reconstruction quantizes) the optimum predictor coefficients for each of the original signal. -
THÈSE Pour Obtenir Le Titre De Docteur En Sciences De L’UNIVERSITÉ De Nice-Sophia Antipolis Mention : Automatique, Traitement Du Signal Et Des Images
Université de Nice-Sophia Antipolis École doctorale STIC THÈSE pour obtenir le titre de Docteur en Sciences de l’UNIVERSITÉ de Nice-Sophia Antipolis Mention : Automatique, Traitement du Signal et des Images présentée par Marie OGER Model-based techniques for flexible speech and audio coding Thèse dirigée par Marc ANTONINI, Directeur de Recherche CNRS Equipe d’accueil : CReATIVe, Laboratoire I3S, UNSA-CNRS et Stéphane RAGOT Equipe d’accueil : TPS, Laboratoire SSTP, France Télécom R&D Lannion soutenance prévue le 13 décembre 2007 Jury: Robert M. Gray Université de Stanford Rapporteur Pierre Duhamel CNRS Paris Rapporteur Marc Antonini CNRS Sophia Antipolis Directeur de thèse Stéphane Ragot France Télécom Lannion Directeur de thèse Michel Barlaud Université de Nice-Sophia Antipolis Examinateur Geneviève Baudoin ESIEE Paris Examinateur Christine Guillemot INRIA Rennes Examinateur Bastiaan Kleijn KTH Stockholm Examinateur ii Acronyms 3GPP 3rd Generation Partnership Project AAC Advanced Audio Coding AbS Analysis-by -Synthesis ACELP Algebraic Code Excited Linear Prediction ACR Absolute Category Rating ADPCM Adaptive Differential Pulse Code Modulation AMR-WB Adaptive Multi Rate-Wideband AMR-WB+ Extended Adaptive Multi-Rate Wideband ATC Adaptive Transform Codec BER Bit Error Rates BSAC Bit-Sliced Arithmetic Coding CCR Comparative Category Rating CELP Code Excited Linear Predictive CMOS Comparison Mean Opinion Score DCR Degradation Category Rating DCT Discrete Cosine Transform DMOS Degradation Mean Opinion Score DPCM Differential Pulse Code Modulation DWT Discrete Wavelet Transform e-AAC+ Enhanced High Efficiency Advanced Audio Coding EM Expectation Maximization iv FEC Frame Error Concealment FER Frame Error Rate FFT Fast Fourier Transform HE-AAC High Efficiency AAC GMM Gaussian Mixture Model i.i.d. -
Notice: This Material May Be Protected by Copyright Law (Title 17 U.S.C.) No
Notice: This Material may be protected by Copyright Law (Title 17 U.S.C.) no. 0 Data Compression Debra A. Lelewer and Daniel S. Hirschberg < Technical Report No. 87-10 Abstract This paper surveys a variety of data compression methods spanning almost forty years of research, from the work of Shannon, Fano and Huffman in the late 40's to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory, as they relate to the goals and evaluation of data compression methods, are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theo retical and empirical natures are reported and possibilities for future research are suggested. INTRODUCTION 1. FUNDAMENTAL CONCEPTS 1.1 Definitions 1.2 Classification of Methods 1.3 A Data Compression Model 1.4 Motivation 2. SEMANTIC DEPENDENT METHODS 3. STATIC DEFINED-WORD SCHEMES 3.1 Shannon-Fano Code 3.2 Huffman Coding 3.3 Representations of the Integers 4. ADAPTIVE HUFFMAN CODING 4.1 Algorithm FGK 4.2 Algorithm V 5. OTHER ADAPTIVE METHODS 5.1 Lempel-Ziv Codes 5.2 Algorithm BSTW 6. EMPIRICAL RESULTS 7. SUSCEPTIBILITY TO ERROR 7.1 Static Codes 7.2 Adaptive Codes 8. NEW DIRECTIONS 9. SUMMARY INTRODUCTION Data compression is often referred to as coding. Information theory is defined to be the study of efficient coding and its consequences, in the form of speed of transmission and probability of error [Ingels 1971). -
16.1 Digital “Modes”
Contents 16.1 Digital “Modes” 16.5 Networking Modes 16.1.1 Symbols, Baud, Bits and Bandwidth 16.5.1 OSI Networking Model 16.1.2 Error Detection and Correction 16.5.2 Connected and Connectionless 16.1.3 Data Representations Protocols 16.1.4 Compression Techniques 16.5.3 The Terminal Node Controller (TNC) 16.1.5 Compression vs. Encryption 16.5.4 PACTOR-I 16.2 Unstructured Digital Modes 16.5.5 PACTOR-II 16.2.1 Radioteletype (RTTY) 16.5.6 PACTOR-III 16.2.2 PSK31 16.5.7 G-TOR 16.2.3 MFSK16 16.5.8 CLOVER-II 16.2.4 DominoEX 16.5.9 CLOVER-2000 16.2.5 THROB 16.5.10 WINMOR 16.2.6 MT63 16.5.11 Packet Radio 16.2.7 Olivia 16.5.12 APRS 16.3 Fuzzy Modes 16.5.13 Winlink 2000 16.3.1 Facsimile (fax) 16.5.14 D-STAR 16.3.2 Slow-Scan TV (SSTV) 16.5.15 P25 16.3.3 Hellschreiber, Feld-Hell or Hell 16.6 Digital Mode Table 16.4 Structured Digital Modes 16.7 Glossary 16.4.1 FSK441 16.8 References and Bibliography 16.4.2 JT6M 16.4.3 JT65 16.4.4 WSPR 16.4.5 HF Digital Voice 16.4.6 ALE Chapter 16 — CD-ROM Content Supplemental Files • Table of digital mode characteristics (section 16.6) • ASCII and ITA2 code tables • Varicode tables for PSK31, MFSK16 and DominoEX • Tips for using FreeDV HF digital voice software by Mel Whitten, KØPFX Chapter 16 Digital Modes There is a broad array of digital modes to service various needs with more coming. -
Shannon's Coding Theorems
Saint Mary's College of California Department of Mathematics Senior Thesis Shannon's Coding Theorems Faculty Advisors: Author: Professor Michael Nathanson Camille Santos Professor Andrew Conner Professor Kathy Porter May 16, 2016 1 Introduction A common problem in communications is how to send information reliably over a noisy communication channel. With his groundbreaking paper titled A Mathematical Theory of Communication, published in 1948, Claude Elwood Shannon asked this question and provided all the answers as well. Shannon realized that at the heart of all forms of communication, e.g. radio, television, etc., the one thing they all have in common is information. Rather than amplifying the information, as was being done in telephone lines at that time, information could be converted into sequences of 1s and 0s, and then sent through a communication channel with minimal error. Furthermore, Shannon established fundamental limits on what is possible or could be acheived by a communication system. Thus, this paper led to the creation of a new school of thought called Information Theory. 2 Communication Systems In general, a communication system is a collection of processes that sends information from one place to another. Similarly, a storage system is a system that is used for storage and later retrieval of information. Thus, in a sense, a storage system may also be thought of as a communication system that sends information from one place (now, or the present) to another (then, or the future) [3]. In a communication system, information always begins at a source (e.g. a book, music, or video) and is then sent and processed by an encoder to a format that is suitable for transmission through a physical communications medium, called a channel. -
Information Theory and Coding
Information Theory and Coding Introduction – What is it all about? References: C.E.Shannon, “A Mathematical Theory of Communication”, The Bell System Technical Journal, Vol. 27, pp. 379–423, 623–656, July, October, 1948. C.E.Shannon “Communication in the presence of Noise”, Proc. Inst of Radio Engineers, Vol 37 No 1, pp 10 – 21, January 1949. James L Massey, “Information Theory: The Copernican System of Communications”, IEEE Communications Magazine, Vol. 22 No 12, pp 26 – 28, December 1984 Mischa Schwartz, “Information Transmission, Modulation and Noise”, McGraw Hill 1990, Chapter 1 The purpose of telecommunications is the efficient and reliable transmission of real data – text, speech, image, taste, smell etc over a real transmission channel – cable, fibre, wireless, satellite. In achieving this there are two main issues. The first is the source – the information. How do we code the real life usually analogue information, into digital symbols in a way to convey and reproduce precisely without loss the transmitted text, picture etc.? This entails proper analysis of the source to work out the best reliable but minimum amount of information symbols to reduce the source data rate. The second is the channel, and how to pass through it using appropriate information symbols the source. In doing this there is the problem of noise in all the multifarious ways that it appears – random, shot, burst- and the problem of one symbol interfering with the previous or next symbol especially as the symbol rate increases. At the receiver the symbols have to be guessed, based on classical statistical communications theory. The guessing, unfortunately, is heavily influenced by the channel noise, especially at high rates. -
Source Coding: Part I of Fundamentals of Source and Video Coding Full Text Available At
Full text available at: http://dx.doi.org/10.1561/2000000010 Source Coding: Part I of Fundamentals of Source and Video Coding Full text available at: http://dx.doi.org/10.1561/2000000010 Source Coding: Part I of Fundamentals of Source and Video Coding Thomas Wiegand Berlin Institute of Technology and Fraunhofer Institute for Telecommunications | Heinrich Hertz Institute Germany [email protected] Heiko Schwarz Fraunhofer Institute for Telecommunications | Heinrich Hertz Institute Germany [email protected] Boston { Delft Full text available at: http://dx.doi.org/10.1561/2000000010 Foundations and Trends R in Signal Processing Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 USA Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is T. Wiegand and H. Schwarz, Source Coding: Part I of Fundamentals of Source and Video Coding, Foundations and Trends R in Signal Processing, vol 4, nos 1{2, pp 1{222, 2010 ISBN: 978-1-60198-408-1 c 2011 T. Wiegand and H. Schwarz All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Cen- ter, Inc., 222 Rosewood Drive, Danvers, MA 01923. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by now Publishers Inc for users registered with the Copyright Clearance Center (CCC). -
Fundamental Limits of Video Coding: a Closed-Form Characterization of Rate Distortion Region from First Principles
1 Fundamental Limits of Video Coding: A Closed-form Characterization of Rate Distortion Region from First Principles Kamesh Namuduri and Gayatri Mehta Department of Electrical Engineering University of North Texas Abstract—Classical motion-compensated video coding minimum amount of rate required to encode video. On methods have been standardized by MPEG over the the other hand, a communication technology may put years and video codecs have become integral parts of a limit on the maximum data transfer rate that the media entertainment applications. Despite the ubiquitous system can handle. This limit, in turn, places a bound use of video coding techniques, it is interesting to note on the resulting video quality. Therefore, there is a that a closed form rate-distortion characterization for video coding is not available in the literature. In this need to investigate the tradeoffs between the bit-rate (R) paper, we develop a simple, yet, fundamental character- required to encode video and the resulting distortion ization of rate-distortion region in video coding based (D) in the reconstructed video. Rate-distortion (R-D) on information-theoretic first principles. The concept of analysis deals with lossy video coding and it establishes conditional motion estimation is used to derive the closed- a relationship between the two parameters by means of form expression for rate-distortion region without losing a Rate-distortion R(D) function [1], [6], [10]. Since R- its generality. Conditional motion estimation offers an D analysis is based on information-theoretic concepts, elegant means to analyze the rate-distortion trade-offs it places absolute bounds on achievable rates and thus and demonstrates the viability of achieving the bounds derives its significance. -
Variable-Length Codes for Data Compression David Salomon
Variable-length Codes for Data Compression David Salomon Variable-length Codes for Data Compression ABC Professor David Salomon (emeritus) Computer Science Department California State University Northridge, CA 91330-8281 USA email: david.salomon@ csun.edu British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: ISBN 978-1-84628-958-3 e-ISBN 978-1-84628-959-0 Printed on acid-free paper. °c Springer-Verlag London Limited 2007 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permit- ted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. 9 8 7 6 5 4 3 2 1 Springer Science+Business Media springer.com To the originators and developers of the codes. -
Beyond Traditional Transform Coding
Beyond Traditional Transform Coding by Vivek K Goyal B.S. (University of Iowa) 1993 B.S.E. (University of Iowa) 1993 M.S. (University of California, Berkeley) 1995 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Engineering|Electrical Engineering and Computer Sciences in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Martin Vetterli, Chair Professor Venkat Anantharam Professor Bin Yu Fall 1998 Beyond Traditional Transform Coding Copyright 1998 by Vivek K Goyal Printed with minor corrections 1999. 1 Abstract Beyond Traditional Transform Coding by Vivek K Goyal Doctor of Philosophy in Engineering|Electrical Engineering and Computer Sciences University of California, Berkeley Professor Martin Vetterli, Chair Since its inception in 1956, transform coding has become the most successful and pervasive technique for lossy compression of audio, images, and video. In conventional transform coding, the original signal is mapped to an intermediary by a linear transform; the final compressed form is produced by scalar quantization of the intermediary and entropy coding. The transform is much more than a conceptual aid; it makes computations with large amounts of data feasible. This thesis extends the traditional theory toward several goals: improved compression of nonstationary or non-Gaussian signals with unknown parameters; robust joint source–channel coding for erasure channels; and computational complexity reduction or optimization. The first contribution of the thesis is an exploration of the use of frames, which are overcomplete sets of vectors in Hilbert spaces, to form efficient signal representations. Linear transforms based on frames give representations with robustness to random additive noise and quantization, but with poor rate–distortion characteristics. -
Universal Coding with Different Modelers in Data Compression By
Universal coding with different modelers in data compression by Reggie ChingPing Kwan A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Montana State University © Copyright by Reggie ChingPing Kwan (1987) Abstract: In data compression systems, most existing codes have integer code length because of the nature of block codes. One of the ways to improve compression is to use codes with noninteger length. We developed an arithmetic universal coding scheme to achieve the entropy of an information source with the given probabilities from the modeler. We show how the universal coder can be used for both the probabilistic and nonprobabilistic approaches in data compression. to avoid a model file being too large, nonadaptive models are transformed into adaptive models simply by keeping the appropriate counts of symbols or strings. The idea of the universal coder is from the Elias code so it is quite close to the arithmetic code suggested by Rissanen and Langdon. The major difference is the way that we handle the precision problem and the carry-over problem. Ten to twenty percent extra compression can be expected as a result of using the universal coder. The process of adaptive modeling, however, may be forty times slower unless parallel algorithms are used to speed up the probability calculating process. UNIVERSAL CODING WITH DIFFERENT MODELERS IN DATA COMPRESSION by Reggie ChingPing Kwan A thesis submitted'in partial fulfillment of the requirements for the degree Of Master of Science ’ in Computer Science MONTANA STATE UNIVERSITY Bozeman, Montana J u l y '1987 MAIN LJtij /V372 /<97? C o p* j_j_ APPROVAL of a thesis submitted by Reggie chingping Kwan This thesis has been read by each member of the thesis committee and has been found to be satisfactory regarding content, English usage, format, citation, bibliographic style, and consistency, and is ready for submission to the College of Graduate studies. -
Sparsity in Linear Predictive Coding of Speech
Sparsity in Linear Predictive Coding of Speech Ph.D. Thesis Daniele Giacobello Multimedia Information and Signal Processing Department of Electronic Systems Aalborg University Niels Jernes Vej 12, 9220 Aalborg Ø, Denmark Sparsity in Linear Predictive Coding of Speech Ph.D. Thesis August 2010 Copyright c 2010 Daniele Giacobello, except where otherwise stated. All rights reserved. Abstract This thesis deals with developing improved techniques for speech coding based on the recent developments in sparse signal representation. In particular, this work is motivated by the need to address some of the limitations of the well- known linear prediction (LP) model currently applied in many modern speech coders. In the first part of the thesis, we provide an overview of Sparse Linear Predic- tion, a set of speech processing tools created by introducing sparsity constraints into the LP framework. This approach defines predictors that look for a sparse residual rather than a minimum variance one with direct applications to coding but also consistent with the speech production model of voiced speech, where the excitation of the all-pole filter can be modeled as an impulse train, i.e., a sparse sequence. Introducing sparsity in the LP framework will also bring to de- velop the concept of high-order sparse predictors. These predictors, by modeling efficiently the spectral envelope and the harmonics components with very few coefficients, have direct applications in speech processing, engendering a joint estimation of short-term and long-term predictors. We also give preliminary results of the effectiveness of their application in audio processing. The second part of the thesis deals with introducing sparsity directly in the linear prediction analysis-by-synthesis (LPAS) speech coding paradigm.