Advanced Signal Processing and Digital Noise Reduction (häftad)
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Häftad (Paperback / softback)
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Softcover reprint of the original 1st ed. 1996
42 Illustrations, black and white; XIII, 397 S. 42 Abb.
244 x 170 x 22 mm
658 g
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1 Paperback / softback
Advanced Signal Processing and Digital Noise Reduction (häftad)

Advanced Signal Processing and Digital Noise Reduction

Häftad Tyska, 2012-05-31
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Stochastic signal processing plays a central role in telecommunication and information processing systems, and has a wide range of applications in speech technology, audio signal processing, channel equalisation, radar signal processing, pattern analysis, data forecasting, decision making systems etc. The theory and application of signal processing is concerned with the identification, modelling, and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy. Hence, noise reduction and the removal of channel distortions is an important part of a signal processing system. The aim of this book is to provide a coherent and structured presentation of the theory and applications of stochastic signal processing and noise reduction methods. This book is organised in fourteen chapters. Chapter 1 begins with an introduction to signal processing, and provides a brief review of the signal processing methodologies and applications. The basic operations of sampling and quantisation are reviewed in this chapter. Chapter 2 provides an introduction to the theory and applications of stochastic signal processing. The chapter begins with an introduction to random signals, stochastic processes, probabilistic models and statistical measures. The concepts of stationary, non-stationary and ergodic processes are introduced in this chapter, and some important classes of random processes such as Gaussian, mixture Gaussian, Markov chains, and Poisson processes are considered. The effects of transformation of a signal on its distribution are considered.
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Fler böcker av Saeed V Vaseghi

  • Multimedia Signal Processing

    Saeed V Vaseghi

    Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network m...


1 Introduction.- 1.1 Signals and Information.- 1.2 Signal Processing Methods.- 1.2.1 Non-parametric Signal Processing.- 1.2.2 Model-based Signal Processing.- 1.2.3 Bayesian Statistical Signal Processing.- 1.2.4 Neural Networks.- 1.3 Applications of Digital Signal Processing.- 1.3.1 Adaptive Noise Cancellation and Noise Reduction.- 1.3.2 Blind Channel Equalisation.- 1.3.3 Signal Classification and Pattern Recognition.- 1.3.4 Linear Prediction Modelling of Speech.- 1.3.5 Digital Coding of Audio Signals.- 1.3.6 Detection of Signals in Noise.- 1.3.7 Directional Reception of Waves: Beamforming.- 1.4 Sampling and Analog to Digital Conversion.- 1.4.1 Time-Domain Sampling and Reconstruction of Analog Signals.- 1.4.2 Quantisation.- 2 Stochastic Processes.- 2.1 Random Signals and Stochastic Processes.- 2.1.1 Stochastic Processes.- 2.1.2 The Space or Ensemble of a Random Process.- 2.2 Probabilistic Models of a Random Process.- 2.3 Stationary and Nonstationary Random Processes.- 2.3.1 Strict Sense Stationary Processes.- 2.3.2 Wide Sense Stationary Processes.- 2.3.3 Nonstationary Processes.- 2.4 Expected Values of a Stochastic Process.- 2.4.1 The Mean Value.- 2.4.2 Autocorrelation.- 2.4.3 Autocovariance.- 2.4.4 Power Spectral Density.- 2.4.5 Joint Statistical Averages of Two Random Processes.- 2.4.6 Cross Correlation and Cross Covariance.- 2.4.7 Cross Power Spectral Density and Coherence.- 2.4.8 Ergodic Processes and Time-averaged Statistics.- 2.4.9 Mean-ergodic Processes.- 2.4.10 Correlation-ergodic Processes.- 2.5 Some Useful Classes of Random Processes.- 2.5.1 Gaussian (Normal) Process.- 2.5.2 Multi-variate Gaussian Process.- 2.5.3 Mixture Gaussian Process.- 2.5.4 A Binary-state Gaussian Process.- 2.5.5 Poisson Process.- 2.5.6 Shot Noise.- 2.5.7 Poisson-Gaussian Model for Clutters and Impulsive Noise.- 2.5.8 Markov Processes.- 2.6 Transformation of a Random Process.- 2.6.1 Monotonic Transformation of Random Signals.- 2.6.2 Many-to-one Mapping of Random Signals.- Summary.- 3 Bayesian Estimation and Classification.- 3.1 Estimation Theory: Basic Definitions.- 3.1.1 Predictive and Statistical Models in Estimation.- 3.1.2 Parameter Space.- 3.1.3 Parameter Estimation and Signal Restoration.- 3.1.4 Performance Measures.- 3.1.5 Prior, and Posterior Spaces and Distributions.- 3.2 Bayesian Estimation.- 3.2.1 Maximum a Posterior Estimation.- 3.2.2 Maximum Likelihood Estimation.- 3.2.3 Minimum Mean Squared Error Estimation.- 3.2.4 Minimum Mean Absolute Value of Error Estimation.- 3.2.5 Equivalence of MAP, ML, MMSE and MAVE.- 3.2.6 Influence of the Prior on Estimation Bias and Variance.- 3.2.7 The Relative Importance of the Prior and the Observation.- 3.3 Estimate-Maximise (EM) Method.- 3.3.1 Convergence of the EM algorithm.- 3.4 Cramer-Rao Bound on the Minimum Estimator Variance.- 3.4.1 Cramer-Rao Bound for Random Parameters.- 3.4.2 Cramer-Rao Bound for a Vector Parameter.- 3.5 Bayesian Classification.- 3.5.1 Classification of Discrete-valued Parameters.- 3.5.2 Maximum a Posterior Classification.- 3.5.3 Maximum Likelihood Classification.- 3.5.4 Minimum Mean Squared Error Classification.- 3.5.5 Bayesian Classification of Finite State Processes.- 3.5.6 Bayesian Estimation of the Most Likely State Sequence.- 3.6 Modelling the Space of a Random Signal.- 3.6.1 Vector Quantisation of a Random Process.- 3.6.2 Design of a Vector Quantiser: K-Means Algorithm.- 3.6.3 Design of a Mixture Gaussian Model.- 3.6.4 The EM Algorithm for Estimation of Mixture Gaussian Densities.- Summary.- 4 Hidden Markov Models.- 4.1 Statistical Models for Nonstationary Processes.- 4.2 Hidden Markov Models.- 4.2.1 A Physical Interpretation of Hidden Markov Models.- 4.2.2 Hidden Markov Model As a Bayesian Method.- 4.2.3 Parameters of a Hidden Markov Model.- 4.2.4 State Observation Models.- 4.2.5 State Transition Probabilities.- 4.2.6 State-Time Trellis Diagram.- 4.3 Training Hidden Markov Models.- 4.3.1 Forward-Backward Probability Computation.- 4.3.2 Baum-Welch Model Re-Estimation.