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COURSE CONTENTS
Topic 1: Background Material
Adaptive filtering: Concepts and applications
Discrete-time signal processing
Stationary processes and models
Spectrum analysis
Linear algebra: Eigenanalysis and matrix decompositions
Topic 2: Wiener Filtering
Minimum mean square error (MMSE) and the orthogonality principle
Digital Wiener filter and Wiener-Hopf equations
Constrained linear MMSE estimation
Applications: Minimum variance beamforming
Topic 3: Linear Prediction
Forward and backward prediction
Levinson-Durbin algorithm
Lattice filters
Applications: DPCM speech coding
Topic 4: Stochastic Methods
Steepest Descent algorithm
Least-Mean-Square (LMS) algorithm
Properties of the LMS
Normalized and frequency-domain LMS
Gradient adaptive lattice methods
Recursive LMS (RLMS) for adaptive IIR filtering
Applications: Active noise control and echo-cancellation
Topic 5: Least Squares Methods
Least squares and orthogonality
Recursive least squares adaptive algorithms
Properties of RLS
Applications: ADPCM speech encoding
Topic 6: Non-Linear Methods
Bussgang algorithms for blind deconvolution
Adaptive filters based on higher-order statistics
Applications: Blind channel equalization