Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Information Theory and Reliable Communication

Preface 1 Introduction 1.1 Compression Techniques 1.1.1 Lossless Compression 1.1.2 Lossy Compression 1.1.3 Measures of Performance 1.2 Modeling and Coding 1.3 Organization of This Book 1.4 Summary 1.5 Projects and Problems 2 Mathematical Preliminaries 2.1 Overview 2.2 A Brief Introduction to Information Theory 2.3 Models 2.3.1 Physical Models 2.3.2 Probability Models 2.3.3. Markov Models 2.3.4 Summary 2.5 Projects and Problems 3 Huffman Coding 3.1 Overview 3.2 "Good" Codes 3.3. The Huffman Coding Algorithm 3.3.1 Minimum Variance Huffman Codes 3.3.2 Length of Huffman Codes 3.3.3 Extended Huffman Codes 3.4 Nonbinary Huffman Codes 3.5 Adaptive Huffman Coding 3.5.1 Update Procedure 3.5.2 Encoding Procedure 3.5.3 Decoding Procedure 3.6 Applications of Huffman Coding 3.6.1 Lossless Image Compression 3.6.2 Text Compression 3.6.3 Audio Compression 3.7 Summary 3.8 Projects and Problems 4 Arithmetic Coding 4.1 Overview 4.2 Introduction 4.3 Coding a Sequence 4.3.1 Generating a Tag 4.3.2 Deciphering the Tag 4.4 Generating a Binary Code 4.4.1 Uniqueness and Efficiency of the Arithmetic Code 4.4.2 Algorithm Implementation 4.4.3 Integer Implementation 4.5 Comparison of Huffman and Arithmetic Coding 4.6 Applications 4.6.1 Bi-Level Image Compression-The JBIG Standard 4.6.2 Image Compression 4.7 Summary 4.8 Projects and Problems 5 Dictionary Techniques 5.1 Overview 5.2 Introduction 5.3 Static Dictionary 5.3.1 Diagram Coding 5.4 Adaptive Dictionary 5.4.1 The LZ77 Approach 5.4.2 The LZ78 Approach 5.5 Applications 5.5.1 File Compression-UNIX COMPRESS 5.5.2 Image Compression-the Graphics Interchange Format (GIF) 5.5.3 Compression over Modems-V.42 bis 5.6 Summary 5.7 Projects and Problems 6 Lossless Image Compression 6.1 Overview 6.2 Introduction 6.3 Facsimile Encoding 6.3.1 Run-Length Coding 6.3.2 CCITT Group 3 and 4-Recommendations T.4 and T.6 6.3.3 Comparison of MH, MR, MMR, and JBIG 6.4 Progressive Image Transmission 6.5 Other Image Compression Approaches 6.5.1 Linear Prediction Models 6.5.2 Context Models 6.5.3 Multiresolution Models 6.5.4 Modeling Prediction Errors 6.6 Summary 6.7 Projects and Problems 7 Mathematical Preliminaries 7.1 Overview 7.2 Introduction 7.3 Distortion Criteria 7.3.1 The Human Visual System 7.3.2 Auditory Perception 7.4 Information Theory Revisted 7.4.1 Conditional Entropy 7.4.2 Average Mutual Information 7.4.3 Differential Entropy 7.5 Rate Distortion Theory 7.6 Models 7.6.1 Probability Models 7.6.2 Linear System Models 7.6.3 Physical Models 7.7 Summary 7.8 Projects and Problems 8 Scalar Quantization 8.1 Overview 8.2 Introduction 8.3 The Quantization Problem 8.4 Uniform Quantizer 8.5 Adaptive Quantization 8.5.1 Forward Adaptive Quantization 8.5.2 Backward Adaptive Quantization 8.6 Nonuniform Quantization 8.6.1 pdf-Optimized Quantization 8.6.2 Companded Quantization 8.7 Entropy-Coded Quantization 8.7.1 Entropy Coding of Lloyd-Max Quantizer Outputs 8.7.2 Entropy-Constrained Quantization 8.7.3 High-Rate Optimum Quantization 8.8 Summary 8.9 Projects and Problems 9 Vector Quantization 9.1 Overview 9.2 Introduction 9.3 Advantages of Vector Quantization over Scalar Quantization 9.4 The Linde-Buzo-Gray Algorithm 9.4.1 Initializing the LBG Algorithm 9.4.2 The Empty Cell Problem 9.4.3 Use of LBG for Image Compression 9.5 Tree-Structured Vector Quantizers 9.5.1 Design of Tree-Structured Vector Quantizers 9.6 Structured Vector Quantizers 9.6.1 Pyramid Vector Quantization 9.6.2 Polar and Spherical Vector Quantizers 9.6.3 Lattice Vector Quantizers 9.7 Variations on the Theme 9.7.1 Gain-Shape Vector Quantization 9.7.2 Mean-Removed Vector Quantization 9.7.3 Classified Vector Quantization 9.7.4 Multistage Vector Quantization 9.7.5 Adaptive Vector Quantization 9.8 Summary 9.9 Projects and Problems 10 Differential Encoding 10.1 Overview 10.2 Introduction 10.3 The Basic Algorithm 10.4 Prediction in DPCM 10.5 Adaptive DPCM (ADPCM) 10.5.1 Adaptive Quantization in DPCM 10.5.2 Adaptive Prediction in DPCM 10.6 Delta Modulation 10.6.1 Constant Factor Adaptive Delta Modulation (CFDM) 10.6.2 Continuously Variable Slope Delta Modulation 10.7 Speech Coding 10.7.1 G.726 10.8 Summary 10.9 Projects and Problems 11 Subband Coding 11.1 Overview 11.2 Introduction 11.3 The Frequency Domain and Filtering 11.3.1 Filters 11.4 The Basic Subband Coding Algorithm 11.4.1 Bit Allocation 11.5 Application to Speech Coding-G.722 11.6 Application to Audio Coding-MPEG Audio 11.7 Application to Image Compression 11.7.1 Decomposing an Image 11.7.2 Coding the Subbands 11.8 Wavelets 11.8.1 Families of Wavelets 11.8.2 Wavelets and Image Compression 11.9 Summary 11.10 Projects and Problems 12 Transform Coding 12.1 Overview 12.2 Introduction 12.3 The Transform 12.4 Transforms of Interest 12.4.1 Karhunen-Loeve Transform 12.4.2 Discrete Cosine Transform 12.4.3 Discrete Sine Transform 12.4.4 Discrete Walsh-Hadamard Transform 12.5 Quantization and Coding of Transform Coefficients 12.6 Application to Image Compression-JPEG 12.6.1 The Transform 12.6.2 Quantization 12.6.3 Coding 12.7 Application to Audio Compression 12.8 Summary 12.9 Projects and Problems 13 Analysis/Synthesis Schemes 13.1 Overview 13.2 Introduction 13.3 Speech Compression 13.3.1 The Channel Vocoder 13.3.2 The Linear Predictive Coder (Gov.Std.LPC-10) 13.3.3 Code Excited Linear Prediction (CELP) 13.3.4 Sinusoidal Coders 13.4 Image Compression 13.4.1 Fractal Compression 13.5 Summary 13.6 Projects and Problems 14 Video Compression 14.1 Overview 14.2 Introduction 14.3 Motion Compensation 14.4 Video Signal Representation 14.5 Algorithms for Videoconferencing and Videophones 14.5.1 ITU_T Recommendation H.261 14.5.2 Model-Based Coding 14.6 Asymmetric Applications 14.6.1 The MPEG Video Standard 14.7 Packet Video 14.7.1 ATM Networks 14.7.2 Compression Issues in ATM Networks 14.7.3 Compression Algorithms for Packet Video 14.8 Summary 14.9 Projects and Problems A Probability and Random Processes A.1 Probability A.2 Random Variables A.3 Distribution Functions A.4 Expectation A.5 Types of Distribution A.6 Stochastic Process A.7 Projects and Problems B A Brief Review of Matrix Concepts B.1 A Matrix B.2 Matrix Operations C Codes for Facsimile Encoding D The Root Lattices Bibliography Index

Introduction to data compression

Sphere Packings, Lattices and Groups

This paper describes a new database of binaural room impulse responses (BRIR), referred to as the Aachen Impulse Response (AIR) database. The main field of application of this database is the evaluation of speech enhancement algorithms dealing with room reverberation. The measurements with a dummy head took place in a low-reverberant studio booth, an office room, a meeting room and a lecture room. Due to the different dimensions and acoustic properties, it covers a wide range of situations where digital hearing aids or other hands-free devices can be used. Besides the description of the database, a motivation for using binaural instead of monaural measurements is given. Furthermore an example using a coherence-based dereverberation technique is provided to show the advantage of this database for algorithm evaluation. The AIR database is being made available online.

/pdf/a-binaural-room-impulse-response-database-for-the-evaluation-39enwfnfqj.pdf

A binaural room impulse response database for the evaluation of dereverberation algorithms

This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm.

/pdf/speech-enhancement-by-map-spectral-amplitude-estimation-2dz5xfweg4.pdf

Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model

1 Introduction. 2 Models of Speech Production and Hearing. 2.1 Organs of Speech Production. 2.2 Characteristics of Speech Signals. 2.3 Model of Speech Production. 2.4 Anatomy of Hearing. 2.5 Performance of the Auditory Organs. Bibliography. 3 Spectral Transformations. 3.1 Fourier Transform of Continuous Signals. 3.2 Fourier Transform of Discrete Signals. 3.3 Linear Shift Invariant Systems. 3.4 The z-Transform. 3.5 The Discrete Fourier Transform. 3.6 Fast Convolution. 3.7 Cepstral Analysis. Bibliography. 4 Filter Banks for Spectral Analysis and Synthesis. 4.1 Spectral Analysis Using Narrow-Band Filters. 4.2 Polyphase Network Filter Banks. 4.3 QuadratureMirror Filter Banks. Bibliography. 5 Stochastic Signals and Estimation. 5.1 Basic Concepts. 5.2 Expectations andMoments. 5.3 Bivariate Statistics. 5.4 Probability and Information. 5.5 Multivariate Statistics. 5.6 Stochastic Processes. 5.7 Estimation of Statistical Quantities by Time Averages. 5.8 Power Spectral Densities. 5.9 Estimation of the Power Spectral Density. 5.10 Statistical Properties of Speech Signals. 5.11 Statistical Properties of DFT Coe.cients. 5.12 Optimal Estimation. Bibliography. 6 Linear Prediction. 6.1 Vocal TractModels and Short-TermPrediction. 6.2 Optimal Prediction Coe.cients for Stationary Signals. 6.3 Predictor Adaptation. 6.4 Long-TermPrediction. Bibliography. 7 Quantization. 7.1 Analog Samples and Digital Presentation. 7.2 Uniform Quantization. 7.3 Non-uniformQuantization. 7.4 OptimalQuantization. 7.5 Adaptive Quantization. 7.6 Vector Quantization. 7.6.1 Principle. Bibliography. 8 Speech Coding. 8.1 Classi.cation of Speech Coding Algorithms. 8.2 Model-Based Predictive Coding. 8.3 Di.erentialWaveform Coding. 8.4 Parametric Coding. 8.5 Hybrid Coding. 8.6 Adaptive Post.ltering. Bibliography. 9 Error Concealment and Softbit Decoding. 9.1 Hardbit Source Decoding. 9.2 Conventional Error Concealment. 9.3 Softbits and L-Values. 9.4 Softbit Source Decoding (SD). 9.5 Application toModel Parameters. 9.6 Further Improvements. Bibliography. 10 Bandwidth Extension of Speech Signals (BWE). 10.1 Narrowband versusWideband Telephony. 10.2 Speech Coding with Integrated BWE. 10.3 BWE without Auxiliary Transmission. Bibliography. 11 Single and Dual Channel Noise Reduction. 11.1 Introduction. 11.2 LinearMMSE Estimators. 11.3 Speech Enhancement in the DFT Domain. 11.4 Optimal Non-Linear Estimators. 11.5 Joint Optimum Detection and Estimation of Speech. 11.6 Computation of Likelihood Ratios. 11.7 Estimation of the A Priory Probability of Speech Presence. 11.8 VAD and Noise Estimation Techniques. 11.9 Dual-Channel Noise Reduction. Bibliography. 12 Multi-Channel Noise Reduction. 12.1 Introduction. 12.2 Spatial Sampling of Sound Fields. 12.3 Beamforming. 12.4 PerformanceMeasures and Spatial Aliasing. 12.5 Design of Fixed Beamformers. 12.6 Adaptive Beamformers. Bibliography. 13 Acoustic Echo Control. 13.1 The Echo Control Problem. 13.2 Evaluation Criteria. 13.3 TheWiener Solution. 13.4 The LMS and NLMS Algorithm. 13.5 Convergence Analysis and Control of the LMS Algorithm. 13.6 Geometric Projection Interpretation of the NLMS Algorithm. 13.7 The A.ne Projection Algorithm. 13.8 Least-Squares and Recursive Least-Squares Algorithms. 13.9 Block Processing and Frequency-Domain Adaptive Filters. 13.9.1 Block LMS Algorithm. 13.10 Additional Measures for Echo Control. 13.11 Stereophonic Acoustic Echo Control. A Codec Standards. B Speech Quality Assessment. Bibliography.

Peter Vary

Papers

A binaural room impulse response database for the evaluation of dereverberation algorithms

Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model

Digital Speech Transmission: Enhancement, Coding and Error Concealment

Noise suppression by spectral magnitude estimation —mechanism and theoretical limits—

On artificial bandwidth extension of telephone speech