VECTOR QUANTIZATION AND SCALAR LINEAR PREDICTION FOR WAVEFORM CODING OF SPEECH AT 16 kbls Lloyd Watts B .Sc. (Eng. Phys.), Queen's University, 1984 A THESIS SUBMI'IlXD IN PARTIAL FULmLLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE ( ENGINEERING SCIENCE ) in the School of Engineering Science O Lloyd Watts 1989 Simon Fraser University June 1989 All rights reserved. This thesis may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author. APPROVAL NAME: Lloyd Watts DEGREE: Master of Applied Science (Engineering Science) TITLE OF THESIS: Vector Quantization and Scalar Linear Prediction for Waveform Coding of Speech at 16 kbjs. EXAMINING COMMITTEE: Chairman: Dr. James Cavers br. Vladirnir &man Senior Supervisor John Bird Supervisor I Dr. Paul Ho Examiner DATE APPROVED: ' PART IA L OPYR IGHT L I CENSE I hereby grant to Simon Fraser Unlverslty the r lght to lend my thesis, project or extended essay (the title of which is shown below) to users of the Simon Fraser University Llbrary, and to make partial or single copies only for such users or in response to a request from the library of any other university, or other educational Institution, on its own behalf or for one of its users. I further agree that permission for multiple copying of this work for scholarly purposes may be granted by me or the Dean of Graduate Studies. It is understood that COPY 1 n9 or publication of this work for financial gain shall not be a I lowed without my written permission. Title of Thes i s/Project/Extended Essay n and Sralar T.inPar Preat~on. for Wavefom Coding of Speech at 16kb/s1I Author: (sig' X ature) vd Watts ( name (date) ABSTRACT This thesis is an investigation of Vector Quantization, Scalar Linear Prediction and other related signal processing techniques, with the purpose of providing high quality, low delay speech waveform coding at medium data rates (16 kbls). Speech waveform coding systems based on adaptive scalar prediction and adaptive scalar quantization have been used to provide toll quality coded speech at high rates such as 32 kbls (ADPCM). However, the performance of these systems is known to degrade to sub-toll quality at 16 kbls, due to excessive quantization noise. Vector Quantization (VQ) is well known to provide a significant reduction in quantization noise over scalar quantization; in fact VQ can be shown to have a theoretically optimal rate-distortion per- formance at very large vector dimensions. This suggests that the performance of 16 kbls ADPCM may be sigmf5cantly improved by replacing the scalar quantizer with a vector quantizer. The resulting configuration, called Vector ADPCM, has an inherently high com- plexity; however, techniques are described which reduce the complexity to the level where implementation with commercially available digital hardware is feasible. Vector ADPCM is found to provide a 3-dB performance improvement over scalar ADPCM, with a 15 times increase in complexity, while still maintaining an encodingldecoding delay of less than 2 milliseconds. Adaptive Postfiltering significantly improves the subjective quality of the coded speech. Informal listening tests indicate that the coded speech is of very good communications quality. iii For Ann ACKNOWLEDGEMENTS The author would like to express appreciation to Dr. Vladimir Cuperman for his guidance and supervision, and to Mr. Allan Crawford of Accurex Technology for his gui- dance and financial support. The author would also like to thank Prof. Daniel Cristall, Dr. Donald Watts, and Dr. John Bird for many helpful discussions. TABLE OF CONTENTS Approval ..................................................................................................................... ii Abstract ....................................................................................................................... iii List of Figures ............................................................................................................. vii List of Tables .............................................................................................................. ix Chapter 1: Introduction ............................................................................................... 1 1.1 Motivation for Research ............................................................................... 1 1.2 Background and Research Methodology ...................................................... 3 1.3 Outline of Thesis ........................................................................................... 4 Chapter 2: Digital Speech Waveform Coding for Telecommunications .................... 5 2.1 Digital Speech in the Network Environment ................................................ 5 2.2 From 64 kb/s PCM to 32 kb/s ADPCM ........................................................ 7 2.3 The Future 16 kb/s Speech Coding Standard ................................................ 8 2.4 Low Rate Speech Coding............................ ; ............................................... 8 Chapter 3: Review of Previous Work ......................................................................... 10 3.1 Introduction ................................................................................................... 10 3.2 Scalar Quantization ....................................................................................... 11 3.3 Linear Prediction and Predictive Coding ...................................................... 16 3.4 The CCI'IT 32 kb/s ADPCM Algorithm ..................................................... 29 3.5 Vector Quantization ...................................................................................... 33 3.6 Analysis-by-S ynthesis Techniques ............................................................... 42 3.7 Adaptive Noise-Shaping and Postfiltering .................................................... 44 Chapter 4: Vector ADPCM for 16 kb/s Speech Waveform Coding ........................... 48 4.1 Introduction ................................................................................................... 48 4.2 Basic Configuration ...................................................................................... 49 4.3 Complexity Reduction ........................................... ;...................................... 51 4.4 Predictor Variations ...................................................................................... 54 4.5 Adaptive Postfiltering ................................................................................... 55 4.6 Gain-Adaptive Vector Quantization ............................................................. 56 4.7 Proposed Solution ......................................................................................... 58 Chapter 5: Experimental Results ................................................................................ 61 5.1 Test Conditions ............................................................................................. 61 5.2 Predictor Performance .................................................................................. 63 5.3 Waveform Vector Quantizer Performance ................................................... 67 5.4 Vector ADPCM Performance ....................................................................... 76 5.5 Complexity Estimates ................................................................................... 90 Chapter 6: Conclusions ............................................................................................... 93 List of References ....................................................................................................... 96 vi LIST OF FIGURES Figure 3.1 Adaptive Quantization............................................................................. Figure 3.2 Differential PCM ..................................................................................... Figure 3.3 Adaptive Prediction in DPCM ................................................................ Figure 3.4 Variance of Prediction Error as a function of Coefficient Vector for order 2........................................................................................................................ Figure 3.5 General Pole-Zero Filter .......................................................................... Figure 3.6 CCITI' ADPCM Block Diagram ............................................................. Figure 3.7 Geometric Interpretation of VQ for vector dimension 2..................... .... Figure 3.8 Voronoi Cells for vector dimension 2..................................................... Figure 3.9 Vector Predictive Coding ........................................................................ Figure 3.10 Gain-Adaptive Vector Quantization....................................................... Figure 3.1 1 Code-Excited Linear Prediction............................................................. Figure 3.12 DPCM with Noise-Shaping and Post.Filtering ...................................... Figure 4.1 Vector ADPCM ....................................................................................... Figure 4.2 Reduced Complexity Vector ADPCM .................................................... Figure 4.3 Vector ADPCM Receiver with Postfilter and AGC ................................ Figure 4.4 Vector ADPCM Transmitter with Gain Adaptation ............................... Figure 4.5 Complexity-Reduced Vector ADPCM with Gain Adaptation ................ Figure 4.6 Proposed