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140122 ||| ger |
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|a 9783322927736
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| 100 |
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|a Vaseghi, Saeed V.
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| 245 |
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|a Advanced Signal Processing and Digital Noise Reduction
|h Elektronische Ressource
|c von Saeed V. Vaseghi
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| 250 |
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|a 1st ed. 1996
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| 260 |
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|a Wiesbaden
|b Vieweg+Teubner Verlag
|c 1996, 1996
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| 300 |
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|a XIII, 397 S. 42 Abb
|b online resource
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|a 12.4 Removal of Noise Pulse Distortions -- Summary -- 13 Echo Cancellation -- 13.1 Telephone Line Echoes -- 13.2 Adaptive Echo Cancellation -- 13.3 Acoustic Feedback Coupling -- 13.4 Sub-band Acoustic Echo Cancellation -- Summary -- 14 Blind Deconvolution and Channel Equalisation -- 14.1 Introduction -- 14.2 Blind Equalisation Using Channel Input Power Spectrum -- 14.3 Equalisation Based on Linear Prediction Models -- 14.4 Bayesian Blind Deconvolution and Equalisation -- 14.5 Blind Equalisation for Digital Communication Channels -- 14.6 Equalisation Based on Higher-Order Statistics -- Summary -- Frequently used Symbols and Abbreviations
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| 505 |
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|a 1 Introduction -- 1.1 Signals and Information -- 1.2 Signal Processing Methods -- 1.3 Applications of Digital Signal Processing -- 1.4 Sampling and Analog to Digital Conversion -- 2 Stochastic Processes -- 2.1 Random Signals and Stochastic Processes -- 2.2 Probabilistic Models of a Random Process -- 2.3 Stationary and Nonstationary Random Processes -- 2.4 Expected Values of a Stochastic Process -- 2.5 Some Useful Classes of Random Processes -- 2.6 Transformation of a Random Process -- Summary -- 3 Bayesian Estimation and Classification -- 3.1 Estimation Theory: Basic Definitions -- 3.2 Bayesian Estimation -- 3.3 Estimate-Maximise (EM) Method -- 3.4 Cramer-Rao Bound on the Minimum Estimator Variance -- 3.5 Bayesian Classification -- 3.6 Modelling the Space of a Random Signal -- Summary -- 4 Hidden Markov Models -- 4.1 Statistical Models for Nonstationary Processes -- 4.2 Hidden Markov Models -- 4.3 Training Hidden Markov Models --
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|a 8 Power Spectrum Estimation -- 8.1 Fourier Transform, Power Spectrum and Correlation -- 8.2 Non-parametric Power Spectrum Estimation -- 8.3 Model-based Power Spectrum Estimation -- 8.4 High Resolution Spectral Estimation Based on Subspace Eigen Analysis -- Summary -- 9 Spectral Subtraction -- 9.1 Spectral Subtraction -- 9.2 Processing Distortions -- 9.3 Non-linear Spectral Subtraction -- 9.4 Implementation of Spectral Subtraction -- Summary -- 10 Interpolation -- 10.1 Introduction -- 10.2 Polynomial Interpolation -- 10.3 Statistical Interpolation -- Summary -- 11 Impulsive Noise -- 11.1 Impulsive Noise -- 11.2 Stochastic Models for Impulsive Noise -- 11.3 Median Filters -- 11.4 Impulsive Noise Removal Using Linear Prediction Models -- 11.5 Robust Parameter Estimation -- 11.6 Restoration of Archived Gramophone Records -- Summary -- 12 Transient Noise -- 12.1 Transient Noise Waveforms -- 12.2 Transient Noise Pulse Models -- 12.3 Detection of Noise Pulses --
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|a 4.4 Decoding of Signals Using Hidden Markov Models -- 4.5 HMM-based Estimation of Signals in Noise -- Summary -- 5 Wiener Filters -- 5.1 Wiener Filters: Least Squared Error Estimation -- 5.2 Block-data Formulation of the Wiener Filter -- 5.3 Vector Space Interpretation of Wiener Filters -- 5.4 Analysis of the Least Mean Squared Error Signal -- 5.5 Formulation of Wiener Filter in Frequency Domain -- 5.6 Some Applications of Wiener Filters -- Summary -- 6 Kalman and Adaptive Least Squared Error Filters -- 6.1 State-space Kalman Filters -- 6.2 Sample Adaptive Filters -- 6.3 Recursive Least Squares (RLS) Adaptive Filters -- 6.4 The Steepest Descent Method -- 6.5 The LMS Adaptation Method -- Summary -- 7 Linear Prediction Models -- 7.1 Linear Prediction Coding -- 7.2 Forward, Backward and Lattice Predictors -- 7.3 Short-term and Long-term Predictors -- 7.4 MAP Estimation of Predictor Coefficients -- 7.5 Signal Restoration Using Linear Prediction Models -- Summary --
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| 653 |
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|a Engineering
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| 653 |
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|a Technology and Engineering
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| 041 |
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|a ger
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 028 |
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|a 10.1007/978-3-322-92773-6
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| 856 |
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|u https://doi.org/10.1007/978-3-322-92773-6?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 620
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