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Produktbild: Multimedia Signal Processing

Multimedia Signal Processing Theory and Applications in Speech, Music and Communications

159,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.11.2007

Verlag

John Wiley & Sons Inc

Seitenzahl

674

Maße (L/B/H)

25,5/17,7/4,5 cm

Gewicht

1295 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-06201-2

Beschreibung

Rezension

"A valuable and accessible text .Suited not only for senior undergraduates and postgraduates but also for researchers and engineers." ( Zentralblatt Math , 2008/17)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.11.2007

Verlag

John Wiley & Sons Inc

Seitenzahl

674

Maße (L/B/H)

25,5/17,7/4,5 cm

Gewicht

1295 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-06201-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Multimedia Signal Processing
  • Preface. Acknowledgement. Symbols. Abbreviations. Part I Basic Digital Signal Processing. 1 Introduction. 1.1 Signals and Information. 1.2 Signal Processing Methods. 1.3 Applications of Digital Signal Processing. 1.4 Summary. 2 Fourier Analysis and Synthesis. 2.1 Introduction. 2.2 Fourier Series: Representation of Periodic Signals. 2.3 Fourier Transform: Representation of Nonperiodic Signals. 2.4 Discrete Fourier Transform. 2.5 Short Time Fourier Transform. 2.6 Fast Fourier Transform (FFT). 2.7 2 D Discrete Fourier Transform (2 D DFT). 2.8 Discrete Cosine Transform (DCT). 2.9 Some Applications of the Fourier Transform. 2.10 Summary. 3 z Transform. 3.1 Introduction. 3.2 Derivation of the z Transform. 3.3 The z Plane and the Unit Circle. 3.4 Properties of z Transform. 3.5 z Transfer Function, Poles (Resonance) and Zeros (Anti resonance). 3.6 z Transform of Analysis of Exponential Transient Signals. 3.7 Inverse z Transform. 3.8 Summary. 4 Digital Filters. 4.1 Introduction. 4.2 Linear Time Invariant Digital Filters. 4.3 Recursive and Non Recursive Filters. 4.4 Filtering Operation: Sum of Vector Products, A Comparison of Convolution and Correlation. 4.5 Filter Structures: Direct, Cascade and Parallel Forms. 4.6 Linear Phase FIR Filters. 4.7 Design of Digital FIR Filter banks. 4.8 Quadrature Mirror Sub band Filters. 4.9 Design of Infinite Impulse Response (IIR) Filters by Pole zero Placements. 4.10 Issues in the Design and Implementation of a Digital Filter. 4.11 Summary. 5 Sampling and Quantisation. 5.1 Introduction. 5.2 Sampling a Continuous Time Signal. 5.3 Quantisation. 5.4 Sampling Rate Conversion: Interpolation and Decimation. 5.5 Summary. Part II Model Based Signal Processing. 6 Information Theory and Probability Models. 6.1 Introduction: Probability and Information Models. 6.2 Random Processes. 6.3 Probability Models of Random Signals. 6.4 Information Models. 6.5 Stationary and Non Stationary Random Processes. 6.6 Statistics (Expected Values) of a Random Process. 6.7 Some Useful Practical Classes of Random Processes. 6.8 Transformation of a Random Process. 6.9 Search Engines: Citation Ranking. 6.10 Summary. 7 Bayesian Inference. 7.1 Bayesian Estimation Theory: Basic Definitions. 7.2 Bayesian Estimation. 7.3 Expectation Maximisation Method. 7.4 Cramer Rao Bound on the Minimum Estimator Variance. 7.5 Design of Gaussian Mixture Models (GMM). 7.6 Bayesian Classification. 7.7 Modelling the Space of a Random Process. 7.8 Summary. 8 Least Square Error, Wiener Kolmogorov Filters. 8.1 Least Square Error Estimation: Wiener Kolmogorov Filter. 8.2 Block Data Formulation of the Wiener Filter. 8.3 Interpretation of Wiener Filter as Projection in Vector Space. 8.4 Analysis of the Least Mean Square Error Signal. 8.5 Formulation of Wiener Filters in the Frequency Domain. 8.6 Some Applications of Wiener Filters. 8.7 Implementation of Wiener Filters. 8.8 Summary. 9 Adaptive Filters: Kalman, RLS, LMS. 9.1 Introduction. 9.2 State Space Kalman Filters. 9.3 Sample Adaptive Filters. 9.4 Recursive Least Square (RLS) Adaptive Filters. 9.5 The Steepest Descent Method. 9.6 LMS Filter. 9.7 Summary. 10 Linear Prediction Models. 10.1 Linear Prediction Coding. 10.2 Forward, Backward and Lattice Predictors. 10.3 Short Term and Long Term Predictors. 10.4 MAP Estimation of Predictor Coefficients. 10.5 Formant Tracking LP Models. 10.6 Sub Band Linear Prediction Model. 10.7 Signal Restoration Using Linear Prediction Models. 10.8 Summary. 11 Hidden Markov Models. 11.1 Statistical Models for Non Stationary Processes. 11.2 Hidden Markov Models. 11.3 Training Hidden Markov Models. 11.4 Decoding Signals Using Hidden Markov Models. 11.5 HMM in DNA and Protein Sequences. 11.6 HMMs for Modelling Speech and Noise. 11.7 Summary. 12 Eigenvector Analysis, Principal Component Analysis and Independent Component Analysis. 12.1 Introduction Linear Systems and Eigenanalysis. 12.2 Eigenvectors and Eigenvalues. 12.3 Principal Component Analysis (PCA). 12.4 Independent Component