Advanced digital signal processing and noise reduction:
Gespeichert in:
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| Format: | Buch |
| Sprache: | English |
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New York
Wiley
2008
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| Ausgabe: | 4. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXX, 514 S. Ill., graph. Darst. |
| ISBN: | 9780470754061 |
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MARC
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| 100 | 1 | |a Vaseghi, Saeed V. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Advanced digital signal processing and noise reduction |c Saeed Vaseghi |
| 250 | |a 4. ed. | ||
| 264 | 1 | |a New York |b Wiley |c 2008 | |
| 300 | |a XXX, 514 S. |b Ill., graph. Darst. | ||
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| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a elektrisk støj | |
| 650 | 4 | |a elektronisk signalbehandling | |
| 650 | 4 | |a Signal processing | |
| 650 | 4 | |a Electronic noise | |
| 650 | 4 | |a Digital filters (Mathematics) | |
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Datensatz im Suchindex
| _version_ | 1804138646505259008 |
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| adam_text | ADVANCED DIGITAL SIGNAL PROCESSING AND NOISE REDUCTION FOURTH EDITION
PROFESSOR SAEED V. VASEGHI PROFESSOR OF COMMUNICATIONS AND SIGNAL
PROCESSING DEPARTMENT OF ELECTRONICS & COMPUTER ENGINEERING BRUNEI
UNIVERSITY, LONDON, UK WILEY A JOHN WILEY AND SONS, LTD, PUBLICATION
CONTENTS PREFACE XIX ACKNOWLEDGEMENTS XXIII SYMBOLS XXV ABBREVIATIONS
XXIX 1 INTRODUCTION 1 1.1 SIGNALS, NOISE AND INFORMATION 1 1.2 SIGNAL
PROCESSING METHODS 3 1.2.1 TRANSFORM-BASED SIGNAL PROCESSING 3 1.2.2
SOURCE-FILTER MODEL-BASED SIGNAL PROCESSING 5 1.2.3 BAYESIAN STATISTICAL
MODEL-BASED SIGNAL PROCESSING 5 1.2.4 NEURAL NETWORKS 6 1.3 APPLICATIONS
OF DIGITAL SIGNAL PROCESSING 6 1.3.1 DIGITAL WATERMARKING 6 1.3.2
BIO-MEDICAL, M1MO, SIGNAL PROCESSING 8 1.3.3 ECHO CANCELLATION 10 1.3.4
ADAPTIVE NOISE CANCELLATION 12 1.3.5 ADAPTIVE NOISE REDUCTION 12 1.3.6
BLIND CHANNEL EQUALISATION 1 3 1.3.7 SIGNAL CLASSIFICATION AND PATTERN
RECOGNITION 13 1.3.8 LINEAR PREDICTION MODELLING OF SPEECH 15 1.3.9
DIGITAL CODING OF AUDIO SIGNALS 16 1.3.10 DETECTION OF SIGNALS IN NOISE
17 1.3.11 DIRECTIONAL RECEPTION OF WAVES: BEAM-FORMING 18 1.3.12
SPACE-TIME SIGNAL PROCESSING 20 1.3.13 DOLBY NOISE REDUCTION 20 1.3.14
RADAR SIGNAL PROCESSING: DOPPLER FREQUENCY SHIFT 21 1.4 A REVIEW OF
SAMPLING AND QUANTISATION 22 1.4.1 ADVANTAGES OF DIGITAL FORMAT 24 1.4.2
DIGITAL SIGNALS STORED AND TRANSMITTED IN ANALOGUE FORMAT 25 1.4.3 THE
EFFECT OF DIGITISATION ON SIGNAL BANDWIDTH 25 1.4.4 SAMPLING A
CONTINUOUS-TIME SIGNAL 25 1.4.5 ALIASING DISTORTION 27 1.4.6 NYQUIST
SAMPLING THEOREM 27 CONTENTS 1.4.7 QUANTISATION 28 1.4.8 NON-LINEAR
QUANTISATION, COMPANDING 30 1.5 SUMMARY 32 BIBLIOGRAPHY 32 NOISE AND
DISTORTION 35 2.1 INTRODUCTION 35 2.1.1 DIFFERENT CLASSES OF NOISE
SOURCES AND DISTORTIONS 3 6 2. /.2 DIFFERENT CLASSES AND
SPECTRAL/TEMPORAL SHAPES OF NOISE 37 2.2 WHITE NOISE 37 2.2.1
BAND-LIMITED WHITE NOISE 38 2.3 COLOURED NOISE; PINK NOISE AND BROWN
NOISE 39 2.4 IMPULSIVE AND CLICK NOISE 39 2.5 TRANSIENT NOISE PULSES 41
2.6 THERMAL NOISE 41 2.7 SHOT NOISE 42 2.8 FLICKER (I//) NOISE 43 2.9
BURST NOISE 44 2.10 ELECTROMAGNETIC (RADIO) NOISE 45 2.10.1 NATURAL
SOURCES OF RADIATION OF ELECTROMAGNETIC NOISE 45 2.10.2 MAN-MADE SOURCES
OF RADIATION OF ELECTROMAGNETIC NOISE 45 2.11 CHANNEL DISTORTIONS 46
2.12 ECHO AND MULTI-PATH REFLECTIONS 47 2.13 MODELLING NOISE 47 2.13.1
FREQUENCY ANALYSIS AND CHARACTERISATION OF NOISE 47 2.13.2 ADDITIVE
WHITE GAUSSIAN NOISE MODEL (AWGN) 48 2. 13.3 HIDDEN MARKOV MODEL AND
GAUSSIAN MIXTURE MODELS FOR NOISE 49 BIBLIOGRAPHY 50 INFORMATION THEORY
AND PROBABILITY MODELS 51 3.1 INTRODUCTION: PROBABILITY AND INFORMATION
MODELS 52 3.2 RANDOM PROCESSES 53 3.2.1 INFORMATION-BEARING RANDOM
SIGNALS VS DETERMINISTIC SIGNALS 53 3.2.2 PSEUDO-RANDOM NUMBER
GENERATORS (PRNG) 55 3.2.3 STOCHASTIC AND RANDOM PROCESSES 56 3.2.4 THE
SPACE OF VARIATIONS OF A RANDOM PROCESS 56 3.3 PROBABILITY MODELS OF
RANDOM SIGNALS 57 3.3.1 PROBABILITY AS A NUMERICAL MAPPING OF BELIEF 57
3.3.2 THE CHOICE OF ONE AND ZERO AS THE LIMITS OF PROBABILITY 57 3.3.3
DISCRETE, CONTINUOUS AND FINITE-STATE PROBABILITY MODELS 58 3.3.4 RANDOM
VARIABLES AND RANDOM PROCESSES 58 3.3.5 PROBABILITY AND RANDOM VARIABLES
- THE SPACE AND SUHSPACES OF A VARIABLE 58 3.3.6 PROBABILITY MASS
FUNCTION-DISCRETE RANDOM VARIABLES 60 3.3.7 BAY ES RULE 60 3.3.8
PROBABILITY DENSITY FUNCTION - CONTINUOUS RANDOM VARIABLES 61 3.3.9
PROBABILITY DENSITY FUNCTIONS OF CONTINUOUS RANDOM PROCESSES 62 3.3.10
HISTOGRAMS - MODELS OF PROBABILITY 63 3.4 INFORMATION MODELS 64 3.4.1
ENTROPY: A MEASURE OF INFORMATION AND UNCERTAINTY 65 3.4.2 MUTUAL
INFORMATION 68 CONTENTS 3.4.3 ENTROPY CODING - VARIABLE LENGTH CODES 69
3.4.4 HUFFMAN CODING 70 3.5 STATIONARY AND NON-STATIONARY RANDOM
PROCESSES 73 3.5.1 STRICT-SENSE STATIONARY PROCESSES 75 3.5.2 WIDE-SENSE
STATIONARY PROCESSES 75 3.5.3 NON-STATIONARY PROCESSES 76 3.6 STATISTICS
(EXPECTED VALUES) OF A RANDOM PROCESS 76 3.6.1 CENTRAL MOMENTS 11
3.6.1.1 CUMULANTS 77 3.6.2 THE MEAN (OR AVERAGE) VALUE 11 3.6.3
CORRELATION, SIMILARITY AND DEPENDENCY 78 3.6.4 AUTOCOVARIANCE 81 3.6.5
POWER SPECTRAL DENSITY 81 3.6.6 JOINT STATISTICAL AVERAGES OF TWO RANDOM
PROCESSES 83 3.6.7 CROSS-CORRELATION AND CROSS-COVARIANCE 83 3.6.8
CROSS-POWER SPECTRAL DENSITY AND COHERENCE 84 3.6.9 ERGODIC PROCESSES
AND TIME-AVERAGED STATISTICS 85 3.6.10 MEAN-ERGODIC PROCESSES 85 3.6.11
CORRELATION-ERGODIC PROCESSES 86 3.7 SOME USEFUL PRACTICAL CLASSES OF
RANDOM PROCESSES 87 3.7.1 GAUSSIAN (NORMAL) PROCESS 87 3.7.2
MULTIVARIATE GAUSSIAN PROCESS 88 3.7.3 GAUSSIAN MIXTURE PROCESS 89 3.7.4
BINARY-STATE GAUSSIAN PROCESS 90 3.7.5 POISSON PROCESS - COUNTING
PROCESS 91 5.7.6 SHOT NOISE 9 2 3.7.7 POISSON-GAUSSIAN MODEL FOR
CLUTTERS AND IMPULSIVE NOISE 93 3.7.8 MARKOV PROCESSES 94 3.7.9 MARKOV
CHAIN PROCESSES 95 3.7.10 HOMOGENEOUS ANDINHOMOGENEOUS MARKOV CHAINS 96
3.7.11 GAMMA PROBABILITY DISTRIBUTION 96 3.7.12 RAYLEIGH PROBABILITY
DISTRIBUTION 97 3.7.13 CHI DISTRIBUTION 97 3.7.14 LAPLACIAN PROBABILITY
DISTRIBUTION 98 3.8 TRANSFORMATION OF A RANDOM PROCESS 98 3.8.1
MONOTONIE TRANSFORMATION OF RANDOM PROCESSES 99 3.8.2 MANY-TO-ONE
MAPPING OF RANDOM SIGNALS 101 3.9 SEARCH ENGINES: CITATION RANKING 103
3.9.1 CITATION RANKING IN WEB PAGE RANK CALCULATION 104 3.10 SUMMARY 104
BIBLIOGRAPHY 105 BAYESIAN INFERENCE 107 4.1 BAYESIAN ESTIMATION THEORY:
BASIC DEFINITIONS 108 4.1.1 BAYES THEOREM 109 4.1.2 ELEMENTS OF
BAYESIAN INFERENCE 109 4.1.3 DYNAMIC AND PROBABILITY MODELS IN
ESTIMATION 110 4.1.4 PARAMETER SPACE AND SIGNAL SPACE 111 4.1.5
PARAMETER ESTIMATION AND SIGNAL RESTORATION 111 4.7.6 PERFORMANCE
MEASURES AND DESIRABLE PROPERTIES OF ESTIMATORS 112 4.1.7 PRIOR AND
POSTERIOR SPACES AND DISTRIBUTIONS 114 CONTENTS 4.2 BAYESIAN ESTIMATION
117 4.2.1 MAXIMUM A POSTERIORI ESTIMATION 117 4.2.2 MAXIMUM-LIKELIHOOD
(ML) ESTIMATION 118 4.2.3 MINIMUM MEAN SQUARE ERROR ESTIMATION 121 4.2.4
MINIMUM MEAN ABSOLUTE VALUE OF ERROR ESTIMATION 122 4.2.5 EQUIVALENCE OF
THE MAP, ML, MMSE AND MAVE ESTIMATES FOR GAUSSIAN PROCESSES WITH UNIFORM
DISTRIBUTED PARAMETERS 123 4.2.6 INFLUENCE OF THE PRIOR ON ESTIMATION
BIAS AND VARIANCE 123 4.2.7 RELATIVE IMPORTANCE OF THE PRIOR AND THE
OBSERVATION 126 4.3 EXPECTATION-MAXIMISATION (EM) METHOD 128 4.3.1
COMPLETE AND INCOMPLETE DATA 128 4.3.2 MAXIMISATION OF EXPECTATION OF
THE LIKELIHOOD FUNCTION 129 4.3.3 DERIVATION AND CONVERGENCE OF THE EM
ALGORITHM 130 4.4 CRAMER-RAO BOUND ON THE MINIMUM ESTIMATOR VARIANCE 131
4.4.1 CRAMER-RAO BOUND FOR RANDOM PARAMETERS 1 33 4.4.2 CRAMER-RAO BOUND
FOR A VECTOR PARAMETER 133 4.5 DESIGN OF GAUSSIAN MIXTURE MODELS (GMMS)
134 4.5.1 EM ESTIMATION OF GAUSSIAN MIXTURE MODEL 134 4.6 BAYESIAN
CLASSIFICATION 136 4.6.1 BINARY CLASSIFICATION 137 4.6.2 CLASSIFICATION
ERROR 139 4.6.3 BAYESIAN CLASSIFICATION OF DISCRETE-VALUED PARAMETERS
139 4.6.4 MAXIMUM A POSTERIORI CLASSIFICATION 140 4.6.5
MAXIMUM-LIKELIHOOD CLASSIFICATION 140 4.6.6 MINIMUM MEAN SQUARE ERROR
CLASSIFICATION 140 4.6.7 BAYESIAN CLASSIFICATION OF FINITE STATE
PROCESSES 141 4.6.8 BAYESIAN ESTIMATION OF THE MOST LIKELY STATE
SEQUENCE 142 4.7 MODELLING THE SPACE OF A RANDOM PROCESS 143 4.7.1
VECTOR QUANTISATION OF A RANDOM PROCESS 143 4.7.2 VECTOR QUANTISATION
USING GAUSSIAN MODELS OF CLUSTERS 143 4.7.3 DESIGN OF A VECTOR
QUANTISER: K-MEANS CLUSTERING 144 4.8 SUMMARY 145 BIBLIOGRAPHY 146
HIDDEN MARKOV MODELS 147 5.1 STATISTICAL MODELS FOR NON-STATIONARY
PROCESSES 147 5.2 HIDDEN MARKOV MODELS 149 5.2.1 COMPARISON OF MARKOV
AND HIDDEN MARKOV MODELS 149 5.2.1.1 OBSERVABLE-STATE MARKOV PROCESS 149
5.2.1.2 HIDDEN-STATE MARKOV PROCESS 149 5.2.2 A PHYSICAL INTERPRETATION:
HMMS OF SPEECH 15 1 5.2.3 HIDDEN MARKOV MODEL AS A BAYESIAN MODEL 152
5.2.4 PARAMETERS OF A HIDDEN MARKOV MODEL 152 5.2.5 STATE OBSERVATION
PROBABILITY MODELS 153 5.2.6 STATE TRANSITION PROBABILITIES 154 5.2.7
STATE-TIME TRELLIS DIAGRAM 154 5.3 TRAINING HIDDEN MARKOV MODELS 155
5.3.1 FORWARD-BACKWARD PROBABILITY COMPUTATION 156 5.3.2 BAUM-WEICH
MODEL RE-ESTIMATION 157 5.3.3 TRAINING HMMS WITH DISCRETE DENSITY
OBSERVATION MODELS 158 CONTENTS 5.3.4 HMMS WITH CONTINUOUS DENSITY
OBSERVATION MODELS 159 5.3.5 HMMS WITH GAUSSIAN MIXTURE PDFS 160 5.4
DECODING SIGNALS USING HIDDEN MARKOV MODELS 161 5.4.I VITERBI DECODING
ALGORITHM 162 5.4.1.1 VITERBI ALGORITHM 163 5.5 HMMS IN DNA AND PROTEIN
SEQUENCES 164 5.6 HMMS FOR MODELLING SPEECH AND NOISE 165 5.6.1
MODELLING SPEECH 165 5.6.2 HMM-BASED ESTIMATION OF SIGNALS IN NOISE 166
5.6.3 SIGNAL AND NOISE MODEL COMBINATION AND DECOMPOSITION 167 5.6.4
HIDDEN MARKOV MODEL COMBINATION 168 5.6.5 DECOMPOSITION OF STATE
SEQUENCES OF SIGNAL AND NOISE 169 5.6.6 HMM-BASED WIENER FILTERS 169
5.6.7 MODELLING NOISE CHARACTERISTICS 170 5.7 SUMMARY 171 BIBLIOGRAPHY
171 LEAST SQUARE ERROR WIENER-KOLMOGOROV FILTERS 173 6. 1 LEAST SQUARE
ERROR ESTIMATION: WIENER-KOLMOGOROV FILTER 173 6. /. 1 DERIVATION OF
WIENER FILTER EQUATION 174 6. 1.2 CALCULATION OF AUTOCORRELATION OF
INPUT AND CROSS-CORRELATION OF INPUT AND DESIRED SIGNALS 111 6.2
BLOCK-DATA FORMULATION OF THE WIENER FILTER 178 6.2.1 QR DECOMPOSITION
OF THE LEAST SQUARE ERROR EQUATION 179 6.3 INTERPRETATION OF WIENER
FILTER AS PROJECTION IN VECTOR SPACE 179 6.4 ANALYSIS OF THE LEAST MEAN
SQUARE ERROR SIGNAL 181 6.5 FORMULATION OF WIENER FILTERS IN THE
FREQUENCY DOMAIN 182 6.6 SOME APPLICATIONS OF WIENER FILTERS 183 6.6.1
WIENER FILTER FOR ADDITIVE NOISE REDUCTION 183 6.6.2 WIENER FILTER AND
SEPARABILITY OF SIGNAL AND NOISE 185 6.6.3 THE SQUARE-ROOT WIENER FILTER
186 6.6.4 WIENER CHANNEL EQUALISER 187 6.6.5 TIME-ALIGNMENT OF SIGNALS
IN MULTI-CHANNEL/MULTI-SENSOR SYSTEMS 187 6.7 IMPLEMENTATION OF WIENER
FILTERS 188 6.7.1 CHOICE OF WIENER FILTER ORDER 189 6.7.2 IMPROVEMENTS
TO WIENER FILTERS 190 6.8 SUMMARY 191 BIBLIOGRAPHY 1 91 ADAPTIVE
FILTERS: KAIMAN, RLS, LMS 193 7.1 INTRODUCTION 194 7.2 STATE-SPACE
KAIMAN FILTERS 195 7.2.1 DERIVATION OF KAIMAN FILTER ALGORITHM 197 7.2.2
RECURSIVE BAYESIAN FORMULATION OF KAIMAN FILTER 200 7.2.3 MARKOVIAN
PROPERTY OF KAIMAN FILTER 201 7.2.4 COMPARISON OF KAIMAN FILTER AND
HIDDEN MARKOV MODEL 202 7.2.5 COMPARISON OF KAIMAN AND WIENER FILTERS
202 7.3 EXTENDED KAIMAN FILTER (EFK) 206 7.4 UNSCENTED KAIMAN FILTER
(UFK) 208 7.5 SAMPLE ADAPTIVE FILTERS - LMS, RLS 211 CONTENTS 7.6
RECURSIVE LEAST SQUARE (RLS) ADAPTIVE FILTERS 213 7. 6.1 MATRIX
INVERSION LEMMA 214 7.6.2 RECURSIVE TIME-UPDATE OF FILTER COEFFICIENTS
215 7.7 THE STEEPEST-DESCENT METHOD 217 7.7.1 CONVERGENCE RATE 219 7.7.2
VECTOR-VALUED ADAPTATION STEP SIZE 220 7.8 LEAST MEAN SQUARED ERROR
(LMS) FILTER 220 7.8.1 LEAKY LMS ALGORITHM 220 7.8.2 NORMALISED LMS
ALGORITHM 22 1 7.8.2.1 DERIVATION OF THE NORMALISED LMS ALGORITHM 221
7.8.2.2 STEADY-STATE ERROR IN LMS 222 7.9 SUMMARY 223 BIBLIOGRAPHY 224
LINEAR PREDICTION MODELS 227 8.1 LINEAR PREDICTION CODING 227 8.1.1
PREDICTABILITY, INFORMATION AND BANDWIDTH 228 8.1.2 APPLICATIONS OF LP
MODEL IN SPEECH PROCESSING 229 8.1.3 TIME-DOMAIN DESCRIPTION OF LP
MODELS 229 8.1.4 FREQUENCY RESPONSE OF LP MODEL AND ITS POLES 230 8.1.5
CALCULATION OF LINEAR PREDICTOR COEFFICIENTS 232 8.1.6 EFFECT OF
ESTIMATION OF CORRELATION FUNCTION ON LP MODEL SOLUTION 233 8.1.7 THE
INVERSE FILTER: SPECTRAL WHITENING, DE-CORRELATION 234 8.1.8 THE
PREDICTION ERROR SIGNAL 235 8.2 FORWARD, BACKWARD AND LATTICE PREDICTORS
236 8.2.1 AUGMENTED EQUATIONS FOR FORWARD AND BACKWARD PREDICTORS 238
8.2.2 LEVINSON-DURBIN RECURSIVE SOLUTION 238 8.2.2.1 LEVINSON-DURBIN
ALGORITHM 240 8.2.3 LATTICE PREDICTORS 240 8.2.4 ALTERNATIVE
FORMULATIONS OF LEAST SQUARE ERROR PREDICTION 241 8.2.4.1 BURG S METHOD
241 8.2.5 SIMULTANEOUS MINIMISATION OF THE BACKWARD AND FORWARD
PREDICTION ERRORS 242 8.2.6 PREDICTOR MODEL ORDER SELECTION 242 8.3
SHORT-TERM AND LONG-TERM PREDICTORS 243 8.4 MAP ESTIMATION OF PREDICTOR
COEFFICIENTS 245 8.4.1 PROBABILITY DENSITY FUNCTION OF PREDICTOR OUTPUT
245 8.4.2 USING THE PRIOR PDF OF THE PREDICTOR COEFFICIENTS 246 8.5
FORMANT-TRACKING LP MODELS 247 8.6 SUB-BAND LINEAR PREDICTION MODEL 248
8.7 SIGNAL RESTORATION USING LINEAR PREDICTION MODELS 249 8.7.1
FREQUENCY-DOMAIN SIGNAL RESTORATION USING PREDICTION MODELS 251 8.7.2
IMPLEMENTATION OF SUB-BAND LINEAR PREDICTION WIENER FILTERS 253 8.8
SUMMARY 254 BIBLIOGRAPHY 254 EIGENVALUE ANALYSIS AND PRINCIPAL COMPONENT
ANALYSIS 257 9.1 INTRODUCTION - LINEAR SYSTEMS AND EIGEN ANALYSIS 257
9.1.1 A GEOMETRIC INTERPRETATION OF EIGENVALUES AND EIGENVECTORS 258 9.2
EIGEN VECTORS AND EIGENVALUES 261 9.2.1 MATRIX SPECTRAL THEOREM 263
9.2.2 COMPUTATION OF EIGENVALUES AND EIGEN VECTORS 263 9.3 PRINCIPAL
COMPONENT ANALYSIS (PCA) 264 9.3.1 COMPUTATION OF PCA 265 9.3.2 PCA
ANALYSIS OF IMAGES: EIGEN-IMAGE REPRESENTATION 265 9.3.3 PCA ANALYSIS OF
SPEECH IN WHITE NOISE 26 6 9.4 SUMMARY 269 BIBLIOGRAPHY 270 POWER
SPECTRUM ANALYSIS 271 10. 1 POWER SPECTRUM AND CORRELATION 271 10.2
FOURIER SERIES: REPRESENTATION OF PERIODIC SIGNALS 272 10.2.1 THE
PROPERTIES OF FOURIER S SINUSOIDAL BASIS FUNCTIONS 272 10.2.2 THE BASIS
FUNCTIONS OF FOURIER SERIES 273 10.2.3 FOURIER SERIES COEFFICIENTS 21A
10.3 FOURIER TRANSFORM: REPRESENTATION OF NON-PERIODIC SIGNALS 274
10.3.1 DISCRETE FOURIER TRANSFORM LID 10.3.2 FREQUENCY-TIME RESOLUTIONS:
THE UNCERTAINTY PRINCIPLE 277 10.3.3 ENERGY-SPECTRAL DENSITY AND
POWER-SPECTRAL DENSITY 278 10.4 NON-PARAMETRIC POWER SPECTRUM ESTIMATION
279 10.4.1 THE MEAN AND VARIANCE OF PERIODOGRAMS 279 10.4.2 AVERAGING
PERIODOGRAMS (BARTLETT METHOD) 280 10.4.3 WELCH METHOD: AVERAGING
PERIODOGRAMS FROM OVERLAPPED AND WINDOWED SEGMENTS 280 10.4.4
BLACKMAN-TUKEY METHOD 282 10.4.5 POWER SPECTRUM ESTIMATION FROM
AUTOCORRELATION OF OVERLAPPED SEGMENTS 282 10.5 MODEL-BASED POWER
SPECTRUM ESTIMATION 283 10.5.1 MAXIMUM-ENTROPY SPECTRAL ESTIMATION 283
10.5.2 AUTOREGRESSIVE POWER SPECTRUM ESTIMATION 28 5 10.5.3
MOVING-AVERAGE POWER SPECTRUM ESTIMATION 286 10.5.4 AUTOREGRESSIVE
MOVING-AVERAGE POWER SPECTRUM ESTIMATION 286 10.6 HIGH-RESOLUTION
SPECTRAL ESTIMATION BASED ON SUBSPACE EIGEN-ANALYSIS 287 10.6.1
PISARENKO HARMONIC DECOMPOSITION 287 10.6.2 MULTIPLE SIGNED
CLASSIFICATION (MUSIC) SPECTRAL ESTIMATION 289 10.6.3 ESTIMATION OF
SIGNAL PARAMETERS VIA ROTATIONAL INVARIANCE TECHNIQUES (ESPRIT) 291 10.7
SUMMARY 293 BIBLIOGRAPHY 293 INTERPOLATION - REPLACEMENT OF LOST SAMPLES
295 11.1 INTRODUCTION 295 11.1.1 IDEAL INTERPOLATION OF A SAMPLED SIGNAL
296 11.1.2 DIGITAL INTERPOLATION BY A FACTOR OF I 297 11.1.3
INTERPOLATION OF A SEQUENCE OF LOST SAMPLES 299 11.1.4 THE FACTORS THAT
AFFECT INTERPOLATION ACCURACY 300 11.2 POLYNOMIAL INTERPOLATION 301
11.2.1 LAGRANGE POLYNOMIAL INTERPOLATION 302 11.2.2 NEWTON POLYNOMIAL
INTERPOLATION 303 11.2.3 HERMITE POLYNOMIAL INTERPOLATION 304 11.2.4
CUBIC SPLINE INTERPOLATION 305 11.3 MODEL-BASED INTERPOLATION 306 11.3.1
MAXIMUM A POSTERIORI INTERPOLATION 307 CONTENTS / 1.3.2 LEAST SQUARE
ERROR AUTOREGRESSIVE INTERPOLATION 308 11.3.3 INTERPOLATION BASED ON A
SHORT-TERM PREDICTION MODEL 309 11.3.4 INTERPOLATION BASED ON LONG-TERM
AND SHORT-TERM CORRELATIONS 312 11.3.5 LSAR INTERPOLATION ERROR 314
11.3.6 INTERPOLATION IN FREQUENCY-TIME DOMAIN 316 11.3.7 INTERPOLATION
USING ADAPTIVE CODE BOOKS 317 11.3.8 INTERPOLATION THROUGH SIGNAL
SUBSTITUTION 318 / 1.3.9 LP-HNM MODEL BASED INTERPOLATION 318 11.4
SUMMARY 319 BIBLIOGRAPHY 319 12 SIGNAL ENHANCEMENT VIA SPECTRAL
AMPLITUDE ESTIMATION 321 12.1 INTRODUETION 321 12.1.1 SPECTRAL
REPRESENTATION OF NOISY SIGNALS 322 12.1.2 VECTOR REPRESENTATION OF
SPECTRUM OF NOISY SIGNALS 323 12.2 SPECTRAL SUBTRACTION 324 12.2.1 POWER
SPECTRUM SUBTRACTION 325 12.2.2 MAGNITUDE SPECTRUM SUBTRACTION 326
12.2.3 SPECTRAL SUBTRACTION FILTER: RELATION TO WIENER FILTERS 326
12.2.4 PROCESSING DISTORTIONS 327 12.2.5 EFFECT OF SPECTRAL SUBTRACTION
ON SIGNAL DISTRIBUTION 32 8 12.2.6 REDUCING THE NOISE VARIANCE 329
12.2.7 FILTERING OUT THE PROCESSING DISTORTIONS 329 12.2.8 NON-LINEAR
SPECTRAL SUBTRACTION 330 12.2.9 IMPLEMENTATION OF SPECTRAL SUBTRACTION
332 12.3 BAYESIAN MMSE SPECTRAL AMPLITUDE ESTIMATION 333 12.4 ESTIMATION
OF SIGNAL TO NOISE RATIOS 335 12.5 APPLICATION TO SPEECH RESTORATION AND
RECOGNITION 336 12.6 SUMMARY 338 BIBLIOGRAPHY 338 13 IMPULSIVE NOISE:
MODELLING, DETECTION AND REMOVAL 341 13.1 IMPULSIVE NOISE 341 13.1.1
DEFINITION OF A THEORETICAL IMPULSE FUNCTION 341 13.1.2 THE SHAPE OF A
REAL IMPULSE IN A COMMUNICATION SYSTEM 342 13.1.3 THE RESPONSE OF A
COMMUNICATION SYSTEM TO AN IMPULSE 343 13.1.4 THE CHOICE OF TIME OR
FREQUENCY DOMAIN FOR PROCESSING OF SIGNALS DEGRADED BY IMPULSIVE NOISE
343 13.2 AUTOCORRELATION AND POWER SPECTRUM OF IMPULSIVE NOISE 344 13.3
PROBABILITY MODELS OF IMPULSIVE NOISE 345 13.3.1 BERNOULLI-GAUSSIAN
MODEL OF IMPULSIVE NOISE 346 13.3.2 POISSON*GAUSSIAN MODEL OF IMPULSIVE
NOISE 346 13.3.3 A BINARY-STATE MODEL OF IMPULSIVE NOISE 347 13.3.4
HIDDEN MARKOV MODEL OF IMPULSIVE AND BURST NOISE 348 13.4 IMPULSIVE
NOISE CONTAMINATION, SIGNAL TO IMPULSIVE NOISE RATIO 349 13.5 MEDIAN
FILTERS FOR REMOVAL OF IMPULSIVE NOISE 350 13.6 IMPULSIVE NOISE REMOVAL
USING LINEAR PREDICTION MODELS 351 13.6.1 IMPULSIVE NOISE DETECTION 352
13.6.2 ANALYSIS OF IMPROVEMENT IN NOISE DETECTABILITY 353 13.6.3
TWO-SIDED PREDICTOR FOR IMPULSIVE NOISE DETECTION 355 13.6.4
INTERPOLATION OF DISCARDED SAMPLES 355 CONTENTS 13.7 ROBUST PARAMETER
ESTIMATION 355 13.8 RESTORATION OF ARCHIVED GRAMOPHONE RECORDS 357 13.9
SUMMARY 358 BIBLIOGRAPHY 358 14 TRANSIENT NOISE PULSES 359 14.1
TRANSIENT NOISE WAVEFORMS 359 14.2 TRANSIENT NOISE PULSE MODELS 361
14.2.1 NOISE PULSE TEMPLATES 361 14.2.2 AUTOREGRESSIVE MODEL OF
TRANSIENT NOISE PULSES 362 14.2.3 HIDDEN MARKOV MODEL OF A NOISE PULSE
PROCESS 363 14.3 DETECTION OF NOISE PULSES 364 14.3.1 MATCHED FILTER FOR
NOISE PULSE DETECTION 364 14.3.2 NOISE DETECTION BASED ON INVERSE
FILTERING 365 14.3.3 NOISE DETECTION BASED ON HMM 365 14.4 REMOVAL OF
NOISE PULSE DISTORTIONS 366 14.4.1 ADAPTIVE SUBTRACTION OF NOISE PULSES
36 6 14.4.2 AR-BASED RESTORATION OF SIGNALS DISTORTED BY NOISE PULSES
367 14.5 SUMMARY 369 BIBLIOGRAPHY 369 15 ECHO CANCELLATION 371 15.1
INTRODUCTION: ACOUSTIC AND HYBRID ECHO 371 15.2 ECHO RETURN TIME: THE
SOURCES OF DELAY IN COMMUNICATION NETWORKS 373 15.2.1 TRANSMISSION LINK
(ELECTROMAGNETIC WAVE PROPAGATION) DELAY 374 15.2.2 SPEECH
CODING/DECODING DELAY 374 15.2.3 NETWORK PROCESSING DELAY 374 15.2.4
DE-JITTER DELAY 375 15.2.5 ACOUSTIC ECHO DELAY 375 15.3 TELEPHONE LINE
HYBRID ECHO 375 15.3.1 ECHO RETURN LOSS 376 15.4 HYBRID (TELEPHONE LINE)
ECHO SUPPRESSION 377 15.5 ADAPTIVE ECHO CANCELLATION 377 15.5.1 ECHO
CANCELLER ADAPTATION METHODS 379 15.5.2 CONVERGENCE OF LINE ECHO
CANCELLER 380 /5.5.3 ECHO CANCELLATION FOR DIGITAL DATA TRANSMISSION 380
15.6 ACOUSTIC ECHO 381 15.7 SUB-BAND ACOUSTIC ECHO CANCELLATION 384 15.8
ECHO CANCELLATION WITH LINEAR PREDICTION PRE-WHITENING 385 15.9
MULTI-INPUT MULTI-OUTPUT ECHO CANCELLATION 386 15.9.1 STEREOPHONIC ECHO
CANCELLATION SYSTEMS 386 15.9.2 NON-UNIQUENESS PROBLEM IN MIMO ECHO
CHANNEL IDENTIFICATION 38 7 15.9.3 MIMO LN-CABIN COMMUNICATION SYSTEMS
388 15.10 SUMMARY 389 BIBLIOGRAPHY 389 16 CHANNEL EQUALISATION AND BLIND
DECONVOLUTION 391 16.1 INTRODUCTION 391 16.1.1 THE IDEAL INVERSE CHANNEL
FILTER 392 16.1.2 EQUALISATION ERROR, CONVOLUTIONAL NOISE 393 16.1.3
BLIND EQUALISATION 394 CONTENTS 16.1.4 MINIMUM-AND MAXIMUM-PHASE
CHANNELS 396 16.1.5 WIENER EQUALISER 396 16.2 BLIND EQUALISATION USING
CHANNEL INPUT POWER SPECTRUM 398 16.2.1 HOMOMORPHIC EQUALISATION 398
16.2.2 HOMOMORPHIC EQUALISATION USING A BANK OF HIGH-PASS FILTERS 400
16.3 EQUALISATION BASED ON LINEAR PREDICTION MODELS 400 16.3.1 BLIND
EQUALISATION THROUGH MODEL FACTORISATION 401 16.4 BAYESIAN BLIND
DECONVOLUTION AND EQUALISATION 402 16.4.1 CONDITIONAL MEAN CHANNEL
ESTIMATION 403 16.4.2 MAXIMUM-LIKELIHOOD CHANNEL ESTIMATION 403 16.4.3
MAXIMUM A POSTERIORI CHANNEL ESTIMATION 404 16.4.4 CHANNEL EQUALISATION
BASED ON HIDDEN MARKOV MODELS 404 16.4.5 MAP CHANNEL ESTIMATE BASED ON
HMMS 40 6 16.4.6 IMPLEMENTATIONS OF HMM-BASED DECONVOLUTION 407 16.5
BLIND EQUALISATION FOR DIGITAL COMMUNICATION CHANNELS 409 16.5.1 LMS
BLIND EQUALISATION 410 16.5.2 EQUALISATION OF A BINARY DIGITAL CHANNEL
413 16.6 EQUALISATION BASED ON HIGHER-ORDER STATISTICS 414 16.6.1
HIGHER-ORDER MOMENTS, CUMULANTS AND SPECTRA 414 16.6.1.1 CUMULANTS 415
16.6.1.2 HIGHER-ORDER SPECTRA 416 16.6.2 HIGHER-ORDER SPECTRA OF LINEAR
TIME-INVARIANT SYSTEMS 416 16.6.3 BLIND EQUALISATION BASED ON
HIGHER-ORDER CEPSTRA 417 16.6.3.1 BI-CEPSTRUM 418 16.6.3.2 TRI-CEPSTRUM
419 16.6.3.3 CALCULATION OF EQUALISER COEFFICIENTS FROM THE TRI-CEPSTRUM
420 16.7 SUMMARY 420 BIBLIOGRAPHY 421 SPEECH ENHANCEMENT: NOISE
REDUCTION, BANDWIDTH EXTENSION AND PACKET REPLACEMENT 423 17.1 AN
OVERVIEW OF SPEECH ENHANCEMENT IN NOISE 424 17.2 SINGLE-INPUT SPEECH
ENHANCEMENT METHODS 425 17.2.1 ELEMENTS OF SINGLE-INPUT SPEECH
ENHANCEMENT 425 17.2.1.1 SEGMENTATION AND WINDOWING OF SPEECH SIGNALS
426 17.2.1.2 SPECTRAL REPRESENTATION OF SPEECH AND NOISE 426 17.2.1.3
LINEAR PREDICTION MODEL REPRESENTATION OF SPEECH AND NOISE 426 17.2.1.4
INTER-FRAME AND INTRA-FRAME CORRELATIONS 427 17.2.1.5 SPEECH ESTIMATION
MODULE 427 17.2.1.6 PROBABILITY MODELS OF SPEECH AND NOISE 427 17.2.1.7
COST OF ERROR FUNCTIONS IN SPEECH ESTIMATION 428 17.2.2 WIENER FILTER
FOR DE-NOISING SPEECH 428 17.2.2.1 WIENER FILTER BASED ON LINEAR
PREDICTION MODELS 429 17.2.2.2 HMM-BASED WIENER FILTERS 429 17.2.3
SPECTRAL SUBTRACTION OF NOISE 430 17.2.3.1 SPECTRAL SUBTRACTION USING LP
MODEL FREQUENCY RESPONSE 431 17.2.4 BAYESIAN MMSE SPEECH ENHANCEMENT 432
17.2.5 KAIMAN FILTER FOR SPEECH ENHANCEMENT 432 17.2.5.1 KAIMAN
STATE-SPACE EQUATIONS OF SIGNAL AND NOISE MODELS 433 CONTENTS 17.2.6
SPEECH ENHANCEMENT USING LP-HNM MODEL 435 17.2.6.1 OVERVIEW OF LP-HNM
ENHANCEMENT SYSTEM 436 17.2.6.2 FORMANT ESTIMATION FROM NOISY SPEECH 437
17.2.6.3 INITIAL-CLEANING OF NOISY SPEECH 437 17.2.6.4 FORMANT TRACKING
437 17.2.6.5 HARMONIC PLUS NOISE MODEL (HNM) OF SPEECH EXCITATION 438
17.2.6.6 FUNDAMENTAL FREQUENCY ESTIMATION 439 17.2.6.7 ESTIMATION OF
AMPLITUDES HARMONICS OF HNM 439 17.2.6.8 ESTIMATION OF NOISE COMPONENT
OF HNM 440 17.2.6.9 KAIMAN SMOOTHING OF TRAJECTORIES OF FORMANTS AND
HARMONICS 440 17.3 SPEECH BANDWIDTH EXTENSION-SPECTRAL EXTRAPOLATION 442
17.3.1 LP-HNM MODEL OF SPEECH 443 17.3.2 EXTRAPOLATION OF SPECTRAL
ENVELOPE OF LP MODEL AAA 17.3.2.1 PHASE ESTIMATION 445 17.3.2.2 CODEBOOK
MAPPING OF THE GAIN 445 17.3.3 EXTRAPOLATION OF SPECTRUM OF EXCITATION
OF LP MODEL 446 17.3.3.1 SENSITIVITY TO PITCH 446 17.4 INTERPOLATION OF
LOST SPEECH SEGMENTS-PACKET LOSS CONCEALMENT 447 17.4.1 PHASE PREDICTION
450 17.4.2 CODEBOOK MAPPING 452 17.4.2.1 EVALUATION OF LP-HNM
INTERPOLATION 453 17.5 MULTI-INPUT SPEECH ENHANCEMENT METHODS 455 17.5.1
BEAM-FORMING WITH MICROPHONE ARRAYS 457 17.5.1.1 SPATIAL CONFIGURATION
OF ARRAY AND THE DIRECTION OF RECEPTION 458 17.5.1.2 DIRECTIONAL OF
ARRIVAL (DO A) AND TIME OF ARRIVAL (TO A) 459 17.5.1.3 STEERING THE
ARRAY DIRECTION: EQUALISATION OF THE TOAS AT THE SENSORS 459 17.5.1.4
THE FREQUENCY RESPONSE OF A DELAY-SUM BEAMFORMER 460 17.6 SPEECH
DISTORTION MEASUREMENTS 462 17.6.1 SIGNAL-TO-NOISE RATIO - SNR 462
17.6.2 SEGMENTAL SIGNAL TO NOISE RATIO - SNR SEE 462 17.6.3
ITAKURA-SAITO DISTANCE - ISD 463 17.6.4 HARMONICITY DISTANCE - HD 463
17.6.5 DIAGNOSTIC RHYME TEST - DRT 463 17.6.6 MEAN OPINION SCORE - MOS
464 17.6.7 PERCEPTUAL EVALUATION OF SPEECH QUALITY - PESQ 464
BIBLIOGRAPHY 464 18 MULTIPLE-INPUT MULTIPLE-OUTPUT SYSTEMS, INDEPENDENT
COMPONENT ANALYSIS 467 18.1 INTRODUCTION 467 18.2 A NOTE ON COMPARISON
OF BEAM-FORMING ARRAYS AND ICA 469 18.3 MIMO SIGNAL PROPAGATION AND
MIXING MODELS 469 18.3.1 INSTANTANEOUS MIXING MODELS 469 18.3.2
ANECHOIC, DELAY AND ATTENUATION, MIXING MODELS 470 18.3.3 CONVOLUTIONAL
MIXING MODELS ALL 18.4 INDEPENDENT COMPONENT ANALYSIS 472 18.4.1 A NOTE
ON ORTHOGONAL, ORTHONORMAL AND INDEPENDENT 473 18.4.2 STATEMENT OF ICA
PROBLEM AL A 18.4.3 BASIC ASSUMPTIONS IN INDEPENDENT COMPONENT ANALYSIS
475 18.4.4 THE LIMITATIONS OF INDEPENDENT COMPONENT ANALYSIS AL 5 XVIII
CONTENTS 18.4.5 WHY A MIXTURE OF TWO GAUSSIAN SIGNALS CANNOT BE
SEPARATED? 476 18.4.6 THE DIFFERENCE BETWEEN INDEPENDENT AND
UNCORRELATED 476 18.4.7 INDEPENDENCE MEASURES; ENTROPY AND MUTUAL
INFORMATION ALL 18.4.7.1 DIFFERENTIAL ENTROPY 477 18.4.7.2 MAXIMUM VALUE
OF DIFFERENTIAL ENTROPY 477 18.4.7.3 MUTUAL INFORMATION 478 18.4.7.4 THE
EFFECT OF A LINEAR TRANSFORMATION ON MUTUAL INFORMATION 479 18.4.7.5
NON-GAUSSIANITY AS A MEASURE OF INDEPENDENCE 480 18.4.7.6 NEGENTROPY: A
MEASURE OF NON-GAUSSIANITY AND INDEPENDENCE 480 18.4.7.7 FOURTH ORDER
MOMENTS - KURTOSIS 481 18.4.7.8 KURTOSIS-BASED CONTRAST FUNCTIONS -
APPROXIMATIONS TO ENTROPIE CONTRAST 481 18.4.8 SUPER-GAUSSIAN AND
SUB-GAUSSIAN DISTRIBUTIONS 482 18.4.9 FAST-ICA METHODS 482 18.4.9.1
GRADIENT SEARCH OPTIMISATION METHOD 483 18.4.9.2 NEWTON OPTIMISATION
METHOD 483 18.4.10 FIXED-POINT FAST ICA 483 18.4.11 CONTRAST FUNCTIONS
AND INFLUENCE FUNCTIONS 484 18.4.12 ICA BASED ON KURTOSIS MAXIMIZATION -
PROJECTION PURSUIT GRADIENT ASCENT 485 18.4.13 JADE ALGORITHM -
ITERATIVE DIAGONALISATION OF CUMULANT MATRICES 487 18.5 SUMMARY 490
BIBLIOGRAPHY 490 19 SIGNAL PROCESSING IN MOBILE COMMUNICATION 491 19.1
INTRODUCTION TO CELLULAR COMMUNICATION 491 19.1.1 A BRIEF HISTORY OF
RADIO COMMUNICATION 492 19.1.2 CELLULAR MOBILE PHONE CONCEPT 493 19.1.3
OUTLINE OF A CELLULAR COMMUNICATION SYSTEM 494 19.2 COMMUNICATION SIGNAL
PROCESSING IN MOBILE SYSTEMS 497 19.3 CAPACITY, NOISE, AND SPECTRAL
EFFICIENCY 498 19.3.1 SPECTRAL EFFICIENCY IN MOBILE COMMUNICATION
SYSTEMS 500 19.4 MULTI-PATH AND FADING IN MOBILE COMMUNICATION 500
19.4.1 MULTI-PATH PROPAGATION OF ELECTROMAGNETIC SIGNALS 50 1 19.4.2
RAKE RECEIVERS FOR MULTI-PATH SIGNALS 502 19.4.3 SIGNAL FADING IN MOBILE
COMMUNICATION SYSTEMS 502 19.4.4 LARGE-SCALE SIGNAL FADING 504 19.4.5
SMALL-SCALE FAST SIGNAL FADING 504 19.5 SMART ANTENNAS - SPACE-TIME
SIGNAL PROCESSING 505 19.5.1 SWITCHED AND ADAPTIVE SMART ANTENNAS 506
19.5.2 SPACE-TIME SIGNAL PROCESSING - DIVERSITY SCHEMES 506 19.6 SUMMARY
508 BIBLIOGRAPHY 508 INDEX 509
|
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| author | Vaseghi, Saeed V. |
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| bvnumber | BV035333370 |
| callnumber-first | T - Technology |
| callnumber-label | TK5102 |
| callnumber-raw | TK5102.9 |
| callnumber-search | TK5102.9 |
| callnumber-sort | TK 45102.9 |
| callnumber-subject | TK - Electrical and Nuclear Engineering |
| classification_rvk | ZN 6040 |
| ctrlnum | (OCoLC)488641574 (DE-599)BVBBV035333370 |
| dewey-full | 621.382/2 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.382/2 |
| dewey-search | 621.382/2 |
| dewey-sort | 3621.382 12 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | 4. ed. |
| format | Book |
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| id | DE-604.BV035333370 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:31:30Z |
| institution | BVB |
| isbn | 9780470754061 |
| language | English |
| lccn | 2008027448 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017137781 |
| oclc_num | 488641574 |
| open_access_boolean | |
| owner | DE-1050 DE-29T DE-19 DE-BY-UBM DE-83 |
| owner_facet | DE-1050 DE-29T DE-19 DE-BY-UBM DE-83 |
| physical | XXX, 514 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Wiley |
| record_format | marc |
| spelling | Vaseghi, Saeed V. Verfasser aut Advanced digital signal processing and noise reduction Saeed Vaseghi 4. ed. New York Wiley 2008 XXX, 514 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index elektrisk støj elektronisk signalbehandling Signal processing Electronic noise Digital filters (Mathematics) Stochastisches Signal (DE-588)4140374-5 gnd rswk-swf Rauschunterdrückung (DE-588)4177102-3 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 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017137781&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Vaseghi, Saeed V. Advanced digital signal processing and noise reduction elektrisk støj elektronisk signalbehandling Signal processing Electronic noise Digital filters (Mathematics) Stochastisches Signal (DE-588)4140374-5 gnd Rauschunterdrückung (DE-588)4177102-3 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd |
| subject_GND | (DE-588)4140374-5 (DE-588)4177102-3 (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 Vaseghi |
| title_fullStr | Advanced digital signal processing and noise reduction Saeed Vaseghi |
| title_full_unstemmed | Advanced digital signal processing and noise reduction Saeed Vaseghi |
| title_short | Advanced digital signal processing and noise reduction |
| title_sort | advanced digital signal processing and noise reduction |
| topic | elektrisk støj elektronisk signalbehandling Signal processing Electronic noise Digital filters (Mathematics) Stochastisches Signal (DE-588)4140374-5 gnd Rauschunterdrückung (DE-588)4177102-3 gnd Digitale Signalverarbeitung (DE-588)4113314-6 gnd |
| topic_facet | elektrisk støj elektronisk signalbehandling Signal processing Electronic noise Digital filters (Mathematics) Stochastisches Signal Rauschunterdrückung Digitale Signalverarbeitung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017137781&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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