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ELEC60002 Statistical Signal Processing and Inference

Lecturer(s): Prof Danilo Mandic

Aims

This module will introduce you to the fundamentals of statistical signal processing, with particular emphasis upon classical and modern estimation theory, parametric and nonparametric modelling, time series analysis, least squares methods, and basics of adaptive signal processing. You will gain practical experience of utilising statistical signal processing on real world multimedia signals, such as your own speech and video recordings through the provision of structured coursework assignments based upon using MATLAB.

Learning Outcomes

Upon successful completion of this module, you will be able to:

1. model and analyse real world random processes using linear stochastic models
2. identify time varying parameters of non-stationary signals using estimation theory
3. derive theoretical and practical performance bounds for the above algorithms and practical settings
4. to estimate clinically relevant physiological parameters from real life student acquired recordings and critically analyse their signal quality

Syllabus

Discrete random signals; statistical stationarity, strict sense and wide sense; Averages; mean, correlations and covariances. Bias-Variance dilemma; Curse of dimensionality; Linear stochastic models; ARMA modelling; Stability of linear stochastic models; Introduction to statistical estimation theory; Properties of estimators; bias and variance; Role of Cramer Rao lower bound; Minimum variance unbiased estimator; Best linear unbiased estimator (BLUE) and maximum likelihood estimation; Maximum likelihood estimator; Bayesian estimation; Least square estimation: orthogonality principle, block and sequential forms; Wiener filtering, adaptive filtering and signal modelling; Concept of an artificial neuron; Applications: time series modelling (financial, biomedical), acoustic echo cancellation and signal enhancement, inverse system modelling and denoising.
Assessment
Exam Duration: N/A
Exam contribution: 0%
Coursework contribution: 100%

Term: Spring

Closed or Open Book (end of year exam): N/A

Coursework Requirement:
To be announced

Oral Exam Required (as final assessment): no

Prerequisite module(s): None required

Course Homepage: unavailable

Book List: