Holdings: Advanced digital signal processing and noise reduction

Advanced digital signal processing and noise reduction:

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Bibliographic Details
Main Author:	Vaseghi, Saeed V. (Author)
Format:	Book
Language:	English
Published:	Chichester [u.a.] Wiley 2006
Edition:	3. ed.
Subjects:	Digital filters (Mathematics) Electronic noise Signal processing > Digital techniques Rauschunterdrückung Stochastisches Signal Digitale Signalverarbeitung
Online Access:	Inhaltsverzeichnis Inhaltsverzeichnis
ISBN:	047009494x 9780470094945

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adam_text	ADVANCED DIGITAL SIGNAL PROCESSING AND NOISE REDUCTION THIRD EDITION SAEED V. VASEGHI PROFESSOR OF COMMUNICATIONS AND SIGNAL PROCESSING DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING BRUNEI UNIVERSITY, UK JOHN WILEY &. SONS, LTD CONTENTS PREFACE XVII SYMBOLS ABBREVIATIONS XXI XXV 1 INTRODUCTION 1.1 SIGNALS AND INFORMATION 1.2 SIGNAL PROCESSING METHODS 1.2.1 TRANSFORM-BASED SIGNAL PROCESSING 1.2.2 MODEL-BASED SIGNAL PROCESSING 1.2.3 BAYESIAN SIGNAL PROCESSING 1.2.4 NEURAL NETWORKS 1.3 APPLICATIONS OF DIGITAL SIGNAL PROCESSING 1.3.1 ADAPTIVE NOISE CANCELLATION .3.2 ADAPTIVE NOISE REDUCTION .3.3 BLIND CHANNEL EQUALISATION .3.4 SIGNAL CLASSIFICATION AND PATTERN RECOGNITION .3.5 LINEAR PREDICTION MODELLING OF SPEECH .3.6 DIGITAL CODING OF AUDIO SIGNALS .3.7 DETECTION OF SIGNALS IN NOISE .3.8 DIRECTIONAL RECEPTION OF WAVES: BEAM-FORMING .3.9 DOLBY NOISE REDUCTION 1.3.10 RADAR SIGNAL PROCESSING: DOPPLER FREQUENCY SHIFT 1.4 SAMPLING AND ANALOGUE-TO-DIGITAL CONVERSION 1.4.1 SAMPLING AND RECONSTRUCTION OF ANALOGUE SIGNALS 1.4.2 QUANTISATION BIBLIOGRAPHY 1 1 3 3 4 4 5 5 5 6 7 8 9 10 12 13 15 15 17 18 19 21 VU1 CONTENTS 2 NOISE AND DISTORTION 2.1 INTRODUCTION 2.2 WHITE NOISE 2.2.1 BAND-LIMITED WHITE NOISE 2.3 COLOURED NOISE 2.4 IMPULSIVE NOISE 2.5 TRANSIENT NOISE PULSES 2.6 THERMAL NOISE 2.7 SHOT NOISE 2.8 ELECTROMAGNETIC NOISE 2.9 CHANNEL DISTORTIONS 2.10 ECHO AND MULTIPATH REFLECTIONS 2.11 MODELLING NOISE 2.11.1 ADDITIVE WHITE GAUSSIAN NOISE MODEL 2.11.2 HIDDEN MARKOV MODEL FOR NOISE BIBLIOGRAPHY 23 24 25 26 26 27 29 30 31 31 32 33 33 36 36 37 3 PROBABILITY AND INFORMATION MODELS 3.1 INTRODUCTION 3.2 RANDOM SIGNALS 3.2.1 RANDOM AND STOCHASTIC PROCESSES 3.2.2 THE SPACE OF A RANDOM PROCESS 3.3 PROBABILITY MODELS 3.3.1 PROBABILITY AND RANDOM VARIABLES 3.3.2 PROBABILITY MASS FUNCTION 3.3.3 PROBABILITY DENSITY FUNCTION 3.3.4 PROBABILITY DENSITY FUNCTIONS OF RANDOM PROCESSES 3.4 INFORMATION MODELS 3.4.1 ENTROPY 3.4.2 MUTUAL INFORMATION 3.4.3 ENTROPY CODING 3.5 STATIONARY AND NONSTATIONARY RANDOM PROCESSES 3.5.1 STRICT-SENSE STATIONARY PROCESSES 3.5.2 WIDE-SENSE STATIONARY PROCESSES 3.5.3 NONSTATIONARY PROCESSES 3.6 STATISTICS (EXPECTED VALUES) OF A RANDOM PROCESS 3.6.1 THE MEAN VALUE 3.6.2 AUTOCORRELATION 3.6.3 AUTOCOVARIANCE 3.6.4 POWER SPECTRAL DENSITY 3.6.5 JOINT STATISTICAL AVERAGES OF TWO RANDOM PROCESSES 3.6.6 CROSS-CORRELATION AND CROSS-COVARIANCE 3.6.7 CROSS-POWER SPECTRAL DENSITY AND COHERENCE 3.6.8 ERGODIC PROCESSES AND TIME-AVERAGED STATISTICS 3.6.9 MEAN-ERGODIC PROCESSES 3.6.10 CORRELATION-ERGODIC PROCESSES 39 40 41 43 43 44 45 45 47 48 50 51 54 56 59 61 61 62 62 63 63 66 66 68 68 70 70 70 72 CONTENTS IX 3.7 SOME USEFUL CLASSES OF RANDOM PROCESSES 73 3.7.1 GAUSSIAN (NORMAL) PROCESS 73 3.7.2 MULTIVARIATE GAUSSIAN PROCESS 74 3.7.3 MIXTURE GAUSSIAN PROCESS 75 3.7.4 A BINARY-STATE GAUSSIAN PROCESS 76 3.7.5 POISSON PROCESS 77 3.7.6 SHOT NOISE 78 3.7.7 POISSON-GAUSSIAN MODEL FOR CLUTTERS AND IMPULSIVE NOISE 79 3.7.8 MARKOV PROCESSES 80 3.7.9 MARKOV CHAIN PROCESSES 81 3.7.10 GAMMA PROBABILITY DISTRIBUTION 82 3.7.11 RAYLEIGH PROBABILITY DISTRIBUTION 83 3.7.12 LAPLACIAN PROBABILITY DISTRIBUTION 83 3.8 TRANSFORMATION OF A RANDOM PROCESS 83 3.8.1 MONOTONIC TRANSFORMATION OF RANDOM PROCESSES 84 3.8.2 MANY-TO-ONE MAPPING OF RANDOM SIGNALS 86 3.9 SUMMARY 90 BIBLIOGRAPHY 90 BAYESIAN INFERENCE 93 4.1 BAYESIAN ESTIMATION THEORY: BASIC DEFINITIONS 94 4.1.1 DYNAMIC AND PROBABILITY MODELS IN ESTIMATION 95 4.1.2 PARAMETER SPACE AND SIGNAL SPACE 96 4.1.3 PARAMETER ESTIMATION AND SIGNAL RESTORATION 97 4.1.4 PERFORMANCE MEASURES AND DESIRABLE PROPERTIES OF ESTIMATORS 98 4.1.5 PRIOR AND POSTERIOR SPACES AND DISTRIBUTIONS 100 4.2 BAYESIAN ESTIMATION 102 4.2.1 MAXIMUM A POSTERIORI ESTIMATION 103 4.2.2 MAXIMUM-LIKELIHOOD ESTIMATION 104 4.2.3 MINIMUM MEAN SQUARE ERROR ESTIMATION 107 4.2.4 MINIMUM MEAN ABSOLUTE VALUE OF ERROR ESTIMATION 108 4.2.5 EQUIVALENCE OF THE MAP, ML, MMSE AND MAVE FOR GAUSSIAN PROCESSES WITH UNIFORM DISTRIBUTED PARAMETERS 109 4.2.6 THE INFLUENCE OF THE PRIOR ON ESTIMATION BIAS AND VARIANCE 109 4.2.7 THE RELATIVE IMPORTANCE OF THE PRIOR AND THE OBSERVATION 114 4.3 THE ESTIMATE-MAXIMISE METHOD 116 4.3.1 CONVERGENCE OF THE EM ALGORITHM 117 4.4 CRAMER-RAO BOUND ON THE MINIMUM ESTIMATOR VARIANCE 119 4.4.1 CRAMER-RAO BOUND FOR RANDOM PARAMETERS 120 4.4.2 CRAMER-RAO BOUND FOR A VECTOR PARAMETER 121 4.5 DESIGN OF GAUSSIAN MIXTURE MODELS 121 4.5.1 EM ESTIMATION OF GAUSSIAN MIXTURE MODEL 122 4.6 BAYESIAN CLASSIFICATION 124 4.6.1 BINARY CLASSIFICATION 125 4.6.2 CLASSIFICATION ERROR 127 4.6.3 BAYESIAN CLASSIFICATION OF DISCRETE-VALUED PARAMETERS 128 CONTENTS 4.6.4 MAXIMUM A POSTERIORI CLASSIFICATION 128 4.6.5 MAXIMUM-LIKELIHOOD CLASSIFICATION 129 4.6.6 MINIMUM MEAN SQUARE ERROR CLASSIFICATION 129 4.6.7 BAYESIAN CLASSIFICATION OF FINITE STATE PROCESSES 130 4.6.8 BAYESIAN ESTIMATION OF THE MOST LIKELY STATE SEQUENCE 131 4.7 MODELLING THE SPACE OF A RANDOM PROCESS 132 4.7.1 VECTOR QUANTISATION OF A RANDOM PROCESS 132 4.7.2 VECTOR QUANTISATION USING GAUSSIAN MODELS 133 4.7.3 DESIGN OF A VECTOR QUANTISER: AT-MEANS CLUSTERING 133 4.8 SUMMARY 134 BIBLIOGRAPHY 135 5 HIDDEN MARKOV MODELS 137 5.1 STATISTICAL MODELS FOR NONSTATIONARY PROCESSES 138 5.2 HIDDEN MARKOV MODELS 139 5.2.1 COMPARISON OF MARKOV AND HIDDEN MARKOV MODELS 139 5.2.2 A PHYSICAL INTERPRETATION: HMMS OF SPEECH 141 5.2.3 HIDDEN MARKOV MODEL AS A BAYESIAN MODEL 142 5.2.4 PARAMETERS OF A HIDDEN MARKOV MODEL 143 5.2.5 STATE OBSERVATION PROBABILITY MODELS 143 5.2.6 STATE TRANSITION PROBABILITIES 144 5.2.7 STATE-TIME TRELLIS DIAGRAM 145 5.3 TRAINING HIDDEN MARKOV MODELS 145 5.3.1 FORWARD-BACKWARD PROBABILITY COMPUTATION 147 5.3.2 BAUM-WELCH MODEL RE-ESTIMATION 148 5.3.3 TRAINING HMMS WITH DISCRETE DENSITY OBSERVATION MODELS 149 5.3.4 HMMS WITH CONTINUOUS DENSITY OBSERVATION MODELS 150 5.3.5 HMMS WITH GAUSSIAN MIXTURE PDFS 151 5.4 DECODING OF SIGNALS USING HIDDEN MARKOV MODELS 152 5.4.1 VITERBI DECODING ALGORITHM 154 5.5 HMMS IN DNA AND PROTEIN SEQUENCE MODELLING 155 5.6 HMMS FOR MODELLING SPEECH AND NOISE 156 5.6.1 MODELLING SPEECH WITH HMMS 156 5.6.2 HMM-BASED ESTIMATION OF SIGNALS IN NOISE 156 5.6.3 SIGNAL AND NOISE MODEL COMBINATION AND DECOMPOSITION 158 5.6.4 HIDDEN MARKOV MODEL COMBINATION 159 5.6.5 DECOMPOSITION OF STATE SEQUENCES OF SIGNAL AND NOISE 160 5.6.6 HMM-BASED WIENER FILTERS 160 5.6.7 MODELLING NOISE CHARACTERISTICS 162 5.7 SUMMARY 162 BIBLIOGRAPHY 163 6 LEAST SQUARE ERROR FILTERS 165 6.1 LEAST SQUARE ERROR ESTIMATION: WIENER FILTERS 166 6.2 BLOCK-DATA FORMULATION OF THE WIENER FILTER 170 6.2.1 QR DECOMPOSITION OF THE LEAST SQUARE ERROR EQUATION 171 CONTENTS 6.3 INTERPRETATION OF WIENER FILTERS AS PROJECTIONS IN VECTOR SPACE 172 6.4 ANALYSIS OF THE LEAST MEAN SQUARE ERROR SIGNAL 174 6.5 FORMULATION OF WIENER FILTERS IN THE FREQUENCY DOMAIN 175 6.6 SOME APPLICATIONS OF WIENER FILTERS 177 6.6.1 WIENER FILTERS FOR ADDITIVE NOISE REDUCTION 177 6.6.2 WIENER FILTERS AND SEPARABILITY OF SIGNAL AND NOISE 178 6.6.3 THE SQUARE-ROOT WIENER FILTER 179 6.6.4 WIENER CHANNEL EQUALISER 180 6.6.5 TIME-ALIGNMENT OF SIGNALS IN MULTICHANNEL/MULTISENSOR SYSTEMS 181 6.7 IMPLEMENTATION OF WIENER FILTERS 182 6.7.1 THE CHOICE OF WIENER FILTER ORDER 183 6.7.2 IMPROVEMENTS TO WIENER FILTERS 184 6.8 SUMMARY 185 BIBLIOGRAPHY 185 ADAPTIVE FILTERS 7.1 INTRODUCTION 7.2 STATE-SPACE KALMAN FILTERS 7.2.1 DERIVATION OF THE KALMAN FILTER ALGORITHM 7.3 SAMPLE-ADAPTIVE FILTERS 7.4 RECURSIVE LEAST SQUARE ADAPTIVE FILTERS 7.4.1 THE MATRIX INVERSION LEMMA 7.4.2 RECURSIVE TIME-UPDATE OF FILTER COEFFICIENTS 7.5 THE STEEPEST-DESCENT METHOD 7.5.1 CONVERGENCE RATE 7.5.2 VECTOR-VALUED ADAPTATION STEP SIZE 7.6 THE LMS FILTER 7.6.1 LEAKY LMS ALGORITHM 7.6.2 NORMALISED LMS ALGORITHM 7.7 SUMMARY BIBLIOGRAPHY 187 188 188 190 195 196 198 199 201 203 204 204 205 206 207 208 8 LINEAR PREDICTION MODELS 209 8.1 LINEAR PREDICTION CODING 210 8.1.1 FREQUENCY RESPONSE OF LP MODELS 213 8.1.2 CALCULATION OF PREDICTOR COEFFICIENTS 214 8.1.3 EFFECT OF ESTIMATION OF CORRELATION FUNCTION ON LP MODEL SOLUTION 216 8.1.4 THE INVERSE FILTER: SPECTRAL WHITENING 216 8.1.5 THE PREDICTION ERROR SIGNAL 217 8.2 FORWARD, BACKWARD AND LATTICE PREDICTORS 219 8.2.1 AUGMENTED EQUATIONS FOR FORWARD AND BACKWARD PREDICTORS 220 8.2.2 LEVINSON-DURBIN RECURSIVE SOLUTION 221 8.2.3 LATTICE PREDICTORS 223 8.2.4 ALTERNATIVE FORMULATIONS OF LEAST SQUARE ERROR PREDICTION 224 8.2.5 PREDICTOR MODEL ORDER SELECTION 225 8.3 SHORT- AND LONG-TERM PREDICTORS 226 XII CONTENTS 8.4 MAP ESTIMATION OF PREDICTOR COEFFICIENTS 228 8.4.1 PROBABILITY DENSITY FUNCTION OF PREDICTOR OUTPUT 229 8.4.2 USING THE PRIOR PDF OF THE PREDICTOR COEFFICIENTS 230 8.5 FORMANT-TRACKING LP MODELS 230 8.6 SUB-BAND LINEAR PREDICTION MODEL 232 8.7 SIGNAL RESTORATION USING LINEAR PREDICTION MODELS 233 8.7.1 FREQUENCY-DOMAIN SIGNAL RESTORATION USING PREDICTION MODELS 235 8.7.2 IMPLEMENTATION OF SUB-BAND LINEAR PREDICTION WIENER FILTERS 237 8.8 SUMMARY 238 BIBLIOGRAPHY 238 9 POWER SPECTRUM AND CORRELATION 241 9.1 POWER SPECTRUM AND CORRELATION 242 9.2 FOURIER SERIES: REPRESENTATION OF PERIODIC SIGNALS 243 9.3 FOURIER TRANSFORM: REPRESENTATION OF APERIODIC SIGNALS 245 9.3.1 DISCRETE FOURIER TRANSFORM 246 9.3.2 TIME/FREQUENCY RESOLUTIONS, THE UNCERTAINTY PRINCIPLE 247 9.3.3 ENERGY-SPECTRAL DENSITY AND POWER-SPECTRAL DENSITY 248 9.4 NONPARAMETRIC POWER SPECTRUM ESTIMATION 249 9.4.1 THE MEAN AND VARIANCE OF PERIODOGRAMS 250 9.4.2 AVERAGING PERIODOGRAMS (BARTLETT METHOD) 250 9.4.3 WELCH METHOD: AVERAGING PERIODOGRAMS FROM OVERLAPPED AND WINDOWED SEGMENTS 251 9.4.4 BLACKMAN-TUKEY METHOD 252 9.4.5 POWER SPECTRUM ESTIMATION FROM AUTOCORRELATION OF OVERLAPPED SEGMENTS 253 9.5 MODEL-BASED POWER SPECTRUM ESTIMATION 254 9.5.1 MAXIMUM-ENTROPY SPECTRAL ESTIMATION 255 9.5.2 AUTOREGRESSIVE POWER SPECTRUM ESTIMATION 257 9.5.3 MOVING-AVERAGE POWER SPECTRUM ESTIMATION 257 9.5.4 AUTOREGRESSIVE MOVING-AVERAGE POWER SPECTRUM ESTIMATION 258 9.6 HIGH-RESOLUTION SPECTRAL ESTIMATION BASED ON SUBSPACE EIGENANALYSIS 259 9.6.1 PISARENKO HARMONIC DECOMPOSITION 259 9.6.2 MULTIPLE SIGNAL CLASSIFICATION SPECTRAL ESTIMATION 261 9.6.3 ESTIMATION OF SIGNAL PARAMETERS VIA ROTATIONAL INVARIANCE TECHNIQUES 264 9.7 SUMMARY 265 BIBLIOGRAPHY 266 10 INTERPOLATION 10.1 INTRODUCTION 10.1.1 INTERPOLATION OF A SAMPLED SIGNAL 10.1.2 DIGITAL INTERPOLATION BY A FACTOR OF / 10.1.3 INTERPOLATION OF A SEQUENCE OF LOST SAMPLES 10.1.4 THE FACTORS THAT AFFECT INTERPOLATION ACCURACY 267 268 268 269 271 273 CONTENTS XILL 10.2 POLYNOMIAL INTERPOLATION 10.2.1 LAGRANGE POLYNOMIAL INTERPOLATION 10.2.2 NEWTON POLYNOMIAL INTERPOLATION 10.2.3 HERMITE POLYNOMIAL INTERPOLATION 10.2.4 CUBIC SPLINE INTERPOLATION 10.3 MODEL-BASED INTERPOLATION 10.3.1 MAXIMUM A POSTERIORI INTERPOLATION 10.3.2 LEAST SQUARE ERROR AUTOREGRESSIVE INTERPOLATION 10.3.3 INTERPOLATION BASED ON A SHORT-TERM PREDICTION MODEL 10.3.4 INTERPOLATION BASED ON LONG- AND SHORT-TERM CORRELATIONS 10.3.5 LSAR INTERPOLATION ERROR 10.3.6 INTERPOLATION IN FREQUENCY-TIME DOMAIN 10.3.7 INTERPOLATION USING ADAPTIVE CODEBOOKS 10.3.8 INTERPOLATION THROUGH SIGNAL SUBSTITUTION 10.4 SUMMARY BIBLIOGRAPHY 274 275 276 278 278 280 281 282 283 286 289 290 293 294 294 295 11 SPECTRAL AMPLITUDE ESTIMATION 11.1 INTRODUCTION 11.1.1 SPECTRAL REPRESENTATION OF NOISY SIGNALS 11.1.2 VECTOR REPRESENTATION OF THE SPECTRUM OF NOISY SIGNALS 11.2 SPECTRAL SUBTRACTION 11.2.1 POWER SPECTRUM SUBTRACTION 11.2.2 MAGNITUDE SPECTRUM SUBTRACTION 11.2.3 SPECTRAL SUBTRACTION FILTER: RELATION TO WIENER FILTERS 11.2.4 PROCESSING DISTORTIONS 11.2.5 EFFECT OF SPECTRAL SUBTRACTION ON SIGNAL DISTRIBUTION 11.2.6 REDUCING THE NOISE VARIANCE 11.2.7 FILTERING OUT THE PROCESSING DISTORTIONS 11.2.8 NONLINEAR SPECTRAL SUBTRACTION 11.2.9 IMPLEMENTATION OF SPECTRAL SUBTRACTION 11.3 BAYESIAN MMSE SPECTRAL AMPLITUDE ESTIMATION 11.4 APPLICATION TO SPEECH RESTORATION AND RECOGNITION 11.5 SUMMARY BIBLIOGRAPHY 297 298 299 299 300 302 303 303 304 305 306 307 308 310 312 315 315 316 12 IMPULSIVE NOISE 12.1 IMPULSIVE NOISE 12.1.1 AUTOCORRELATION AND POWER SPECTRUM OF IMPULSIVE NOISE 12.2 STATISTICAL MODELS FOR IMPULSIVE NOISE 12.2.1 BERNOULLI-GAUSSIAN MODEL OF IMPULSIVE NOISE 12.2.2 POISSON-GAUSSIAN MODEL OF IMPULSIVE NOISE 12.2.3 A BINARY-STATE MODEL OF IMPULSIVE NOISE 12.2.4 SIGNAL-TO-IMPULSIVE-NOISE RATIO 319 320 322 323 324 324 325 326 CONTENTS 12.3 MEDIAN FILTERS 327 12.4 IMPULSIVE NOISE REMOVAL USING LINEAR PREDICTION MODELS 328 12.4.1 IMPULSIVE NOISE DETECTION 328 12.4.2 ANALYSIS OF IMPROVEMENT IN NOISE DETECTABILITY 330 12.4.3 TWO-SIDED PREDICTOR FOR IMPULSIVE NOISE DETECTION 331 12.4.4 INTERPOLATION OF DISCARDED SAMPLES 332 12.5 ROBUST PARAMETER ESTIMATION 333 12.6 RESTORATION OF ARCHIVED GRAMOPHONE RECORDS 334 12.7 SUMMARY 335 BIBLIOGRAPHY 336 13 TRANSIENT NOISE PULSES 337 13.1 TRANSIENT NOISE WAVEFORMS 337 13.2 TRANSIENT NOISE PULSE MODELS 339 13.2.1 NOISE PULSE TEMPLATES 340 13.2.2 AUTOREGRESSIVE MODEL OF TRANSIENT NOISE PULSES 341 13.2.3 HIDDEN MARKOV MODEL OF A NOISE PULSE PROCESS 342 13.3 DETECTION OF NOISE PULSES 342 13.3.1 MATCHED FILTER FOR NOISE PULSE DETECTION 343 13.3.2 NOISE DETECTION BASED ON INVERSE FILTERING 344 13.3.3 NOISE DETECTION BASED ON HMM 344 13.4 REMOVAL OF NOISE PULSE DISTORTIONS 345 13.4.1 ADAPTIVE SUBTRACTION OF NOISE PULSES 345 13.4.2 AR-BASED RESTORATION OF SIGNALS DISTORTED BY NOISE PULSES 347 13.5 SUMMARY 349 BIBLIOGRAPHY 349 14 ECHO CANCELLATION 351 14.1 INTRODUCTION: ACOUSTIC AND HYBRID ECHOES 352 14.2 TELEPHONE LINE HYBRID ECHO 353 14.2.1 ECHO: THE SOURCES OF DELAY IN TELEPHONE NETWORKS 354 14.2.2 ECHO RETURN LOSS 355 14.3 HYBRID ECHO SUPPRESSION 355 14.4 ADAPTIVE ECHO CANCELLATION 356 14.4.1 ECHO CANCELLER ADAPTATION METHODS 357 14.4.2 CONVERGENCE OF LINE ECHO CANCELLER 358 14.4.3 ECHO CANCELLATION FOR DIGITAL DATA TRANSMISSION 359 14.5 ACOUSTIC ECHO 360 14.6 SUB-BAND ACOUSTIC ECHO CANCELLATION 363 14.7 MULTIPLE-INPUT MULTIPLE-OUTPUT ECHO CANCELLATION 365 14.7.1 STEREOPHONIC ECHO CANCELLATION SYSTEMS 365 14.8 SUMMARY 368 BIBLIOGRAPHY 368 CONTENTS XV 15 CHANNEL EQUALISATION AND BLIND DECONVOLUTION 371 15.1 INTRODUCTION 372 15.1.1 THE IDEAL INVERSE CHANNEL FILTER 373 15.1.2 EQUALISATION ERROR, CONVOLUTIONAL NOISE 374 15.1.3 BLIND EQUALISATION 374 15.1.4 MINIMUM- AND MAXIMUM-PHASE CHANNELS 376 15.1.5 WIENER EQUALISER 377 15.2 BLIND EQUALISATION USING THE CHANNEL INPUT POWER SPECTRUM 379 15.2.1 HOMOMORPHIC EQUALISATION 380 15.2.2 HOMOMORPHIC EQUALISATION USING A BANK OF HIGH-PASS FILTERS 382 15.3 EQUALISATION BASED ON LINEAR PREDICTION MODELS 382 15.3.1 BLIND EQUALISATION THROUGH MODEL FACTORISATION 384 15.4 BAYESIAN BLIND DECONVOLUTION AND EQUALISATION 385 15.4.1 CONDITIONAL MEAN CHANNEL ESTIMATION 386 15.4.2 MAXIMUM-LIKELIHOOD CHANNEL ESTIMATION 386 15.4.3 MAXIMUM A POSTERIORI CHANNEL ESTIMATION 386 15.4.4 CHANNEL EQUALISATION BASED ON HIDDEN MARKOV MODELS 387 15.4.5 MAP CHANNEL ESTIMATE BASED ON HMMS 389 15.4.6 IMPLEMENTATIONS OF HMM-BASED DECONVOLUTION 390 15.5 BLIND EQUALISATION FOR DIGITAL COMMUNICATIONS CHANNELS 393 15.5.1 LMS BLIND EQUALISATION 395 15.5.2 EQUALISATION OF A BINARY DIGITAL CHANNEL 397 15.6 EQUALISATION BASED ON HIGHER-ORDER STATISTICS 398 15.6.1 HIGHER-ORDER MOMENTS, CUMULANTS AND SPECTRA 399 15.6.2 HIGHER-ORDER SPECTRA OF LINEAR TIME-INVARIANT SYSTEMS 401 15.6.3 BLIND EQUALISATION BASED ON HIGHER-ORDER CEPSTRA 402 15.7 SUMMARY 406 BIBLIOGRAPHY 406 16 SPEECH ENHANCEMENT IN NOISE 16.1 INTRODUCTION 16.2 SINGLE-INPUT SPEECH-ENHANCEMENT METHODS 16.2.1 AN OVERVIEW OF A SPEECH-ENHANCEMENT SYSTEM 16.2.2 WIENER FILTER FOR DE-NOISING SPEECH 16.2.3 SPECTRAL SUBTRACTION OF NOISE 16.2.4 BAYESIAN MMSE SPEECH ENHANCEMENT 16.2.5 KALMAN FILTER 16.2.6 SPEECH ENHANCEMENT VIA LP MODEL RECONSTRUCTION 16.3 MULTIPLE-INPUT SPEECH-ENHANCEMENT METHODS 16.3.1 BEAM-FORMING WITH MICROPHONE ARRAYS 16.4 SPEECH DISTORTION MEASUREMENTS BIBLIOGRAPHY 409 410 411 411 414 417 418 419 422 425 427 430 431 17 NOISE IN WIRELESS COMMUNICATIONS 17.1 INTRODUCTION TO CELLULAR COMMUNICATIONS 17.2 NOISE, CAPACITY AND SPECTRAL EFFICIENCY 433 434 436 XVI CONTENTS 17.3 COMMUNICATIONS SIGNAL PROCESSING IN MOBILE SYSTEMS 438 17.4 NOISE AND DISTORTION IN MOBILE COMMUNICATIONS SYSTEMS 439 17.4.1 MULTIPATH PROPAGATION OF ELECTROMAGNETIC SIGNALS 440 17.4.2 RAKE RECEIVERS FOR MULTIPATH SIGNALS 441 17.4.3 SIGNAL FADING IN MOBILE COMMUNICATIONS SYSTEMS 442 17.4.4 LARGE-SCALE SIGNAL FADING 443 17.4.5 SMALL-SCALE FAST SIGNAL FADING 444 17.5 SMART ANTENNAS 444 17.5.1 SWITCHED AND ADAPTIVE SMART ANTENNAS 446 17.5.2 SPACE-TIME SIGNAL PROCESSING - DIVERSITY SCHEMES 446 17.6 SUMMARY 447 BIBLIOGRAPHY 448 INDEX 449
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id	DE-604.BV019715222
illustrated	Not Illustrated
indexdate	2024-07-09T20:04:28Z
institution	BVB
isbn	047009494x 9780470094945
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-013042492
oclc_num	63386177
open_access_boolean
owner	DE-29T DE-573 DE-91 DE-BY-TUM DE-634
owner_facet	DE-29T DE-573 DE-91 DE-BY-TUM DE-634
publishDate	2006
publishDateSearch	2006
publishDateSort	2006
publisher	Wiley
record_format	marc
spelling	Vaseghi, Saeed V. Verfasser aut Advanced digital signal processing and noise reduction Saeed V. Vaseghi 3. ed. Chichester [u.a.] Wiley 2006 txt rdacontent n rdamedia nc rdacarrier Digital filters (Mathematics) Electronic noise Signal processing Digital techniques Rauschunterdrückung (DE-588)4177102-3 gnd rswk-swf Stochastisches Signal (DE-588)4140374-5 gnd rswk-swf Digitale Signalverarbeitung (DE-588)4113314-6 gnd rswk-swf Stochastisches Signal (DE-588)4140374-5 s Digitale Signalverarbeitung (DE-588)4113314-6 s DE-604 Rauschunterdrückung (DE-588)4177102-3 s http://www.ulb.tu-darmstadt.de/tocs/127875964.pdf Inhaltsverzeichnis HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013042492&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Vaseghi, Saeed V. Advanced digital signal processing and noise reduction Digital filters (Mathematics) Electronic noise Signal processing Digital techniques Rauschunterdrückung (DE-588)4177102-3 gnd Stochastisches Signal (DE-588)4140374-5 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd
subject_GND	(DE-588)4177102-3 (DE-588)4140374-5 (DE-588)4113314-6
title	Advanced digital signal processing and noise reduction
title_auth	Advanced digital signal processing and noise reduction
title_exact_search	Advanced digital signal processing and noise reduction
title_full	Advanced digital signal processing and noise reduction Saeed V. Vaseghi
title_fullStr	Advanced digital signal processing and noise reduction Saeed V. Vaseghi
title_full_unstemmed	Advanced digital signal processing and noise reduction Saeed V. Vaseghi
title_short	Advanced digital signal processing and noise reduction
title_sort	advanced digital signal processing and noise reduction
topic	Digital filters (Mathematics) Electronic noise Signal processing Digital techniques Rauschunterdrückung (DE-588)4177102-3 gnd Stochastisches Signal (DE-588)4140374-5 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd
topic_facet	Digital filters (Mathematics) Electronic noise Signal processing Digital techniques Rauschunterdrückung Stochastisches Signal Digitale Signalverarbeitung
url	http://www.ulb.tu-darmstadt.de/tocs/127875964.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013042492&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT vaseghisaeedv advanceddigitalsignalprocessingandnoisereduction

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