Advanced Digital Signal Processing and Noise Reduction

摘要: Contents Symbols Abbreviations 1 Introduction 1.1 Signals, Noise and Information 1.2 Signal Processing Methods 1.3 Applications of Digital 1.4 A Review Sampling Quantisation 1.5 Summary Bibliography 2 Distortion 2.1 2.2 White 2.3 Coloured Pink Brown 2.4 Impulsive Click 2.5 2.6 Thermal 2.7 Shot 2.8 Flicker (I/f) 2.9 Burst 2.10 Electromagnetic (Radio) 2.11 Channel Distortions 2.12 Echo Multi-path Reflections 2.13 Modelling 2.14 3 Theory Probability Models 3.1 Introduction: 3.2 Random Processes 3.3 3.4 3.5 Stationary Non-stationary 3.6 Expected Values a Process 3.7 Some Useful Classes 3.8 Transformation 3.9 Search Engines: Citation Ranking 3.10 4 Baseyian Inference 4.1 Bayesian Estimation Theory: Basic Definitions 4.2 4.3 The Estimate-Maximise Method 4.4 Cramer-Rao Bound on the Minimum Estimator Variance 4.5 Design Gaussian Mixture 4.6 Classification 4.7 Modeling Space 4.8 5 Hidden Markov 5.1 Statistical for Non-Stationary 5.2 5.3 Training 5.4 Decoding Signals Using 5.5 HMM In DNA Protein Sequence 5.6 HMMs Speech 5.7 6 Least Square Error Wiener-Kolmogorov Filters 6.1 Estimation: Filter 6.2 Block-Data Formulation Wiener 6.3 Interpretation as Projection in Vector 6.4 Analysis Mean 6.5 Frequency Domain 6.6 6.7 Implementation 6.8 7 Adaptive Filters, Kalman, RLS, LMS 7.1 7.2 State-Space Kalman 7.3 Extended 7.4 Unscented 7.5 Sample-Adaptive 7.6 Recursive Square(RLS) 7.7 Steepest-Descent 7.8 7.9 8 Linear Prediction 8.1 Coding 8.2 Forward, Backward Lattice Predictors 8.3 Short-term Long-Term 8.4 MAP Predictor Coefficients 8.5 Formant-Tracking LP 8.6 Sub-Band 8.7 .i.Signal Restoration 8.8 9 Eigenvalue Principal Component 9.1 9.2 Eigen 9.3 9.4 10 Power Spectrum 10.1 Correlation 10.2 Fourier Series: Representation Periodic 10.3.3 Energy-Spectral Density Power-Spectral 10.3 Transform: Aperiodic 10.4 Non-Parametric 10.5 Model-Based Spectral 10.6 High Resolution Based Subspace Eigen-Analysis 10.7 11. Interpolation - Replacement Lost Samples 11.1 11.2 11.3 11.4 12 Enhancement via Amplitude 12.1Introduction 12.2 Noisy 12.3 12.4 Subtraction 12.5 MMSE 12.6 to Ratios 12.7 Application Recognition 12.8 13 Noise: Modelling, Detection Removal 13.1 13.2 Autocorrelation 13.3 13.4 Impulse contamination, Ratio 13.5 Median 13.6 13.7 Robust Parameter 13.8 Archived Gramophone Records 13.9 14 Transient Pulses 14.1 Waveforms 14.2 Pulse 14.3 14.4 14.5 15 Cancellation 15.1 Acoustic Hybrid.i.Hybrid Echoes 15.2 Return Time: Sources Delay Communication Networks 15.3 Telephone Line Hybrid 15.4 Suppression 15.5 .i.Adaptive 15.6 .i.Echo 15.7 .i.Sub-band 15.8 .i. with Pre-whitening 15.9 Multiple-Input Multiple-Output (MIMO) 15.10 16 Equalisation Blind Deconvolution 16.1 16.2 Blind-Deconvolution Input 16.3 16.4 16.5 Channels 16.6 Higher-Order Statistics 16.7 16.8 17 Enhancement: Reduction, Bandwidth Extension Packet 17.1 An Overview 17.2 Single-Input 17.3 17.4 Segments 17.5 17.6 Measurements 17.7 17.8 18 Systems, Independent 18.1 18.2 MIMO Propagation Mixing 18.3 18.4 19 Mobile 19.1 Cellular 19.2 Systems 19.3 Noise, Capacity Efficiency 19.4 Fading 19.5 Smart Beam-forming Antennas 19.6 Index