This document outlines the contents and structure of a course on digital signal processing and digital filters taught by Professor A G Constantinides. The course covers topics such as multirate techniques and wavelets, classical and modern spectrum estimation methods, adaptive signal processing, and applications of DSP. It includes 6 chapters that cover these topics in depth along with background material and references key textbooks. The document provides an overview of what students will learn in the course.

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Digital Signal Processing &
Digital Filters
An Introductory Course
By
Professor A G Constantinides
MSc, EE4, ISE4, PhD

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Digital Signal Processing &
Digital Filters
Contents
1-Introduction
1) Introduction to Digital Signal Processing
 Review of background DSP
 Review of mathematical methods
 Review of discrete-time random
processes and linear systems

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Digital Signal Processing &
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2) Multirate techniques and wavelets
 Introduction to short-time Fourier analysis
 Filter-banks and overlap-add methods of analysis
and synthesis
 Introduction to generalised time-frequency
representation
 Wavelet analysis
 Multirate signal processing
 Interpolation and decimation
 Efficient filter structures for interpolation and
decimation

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3) Classical spectrum estimation methods
 Power spectrum, power spectral density functions,
random processes and linear systems
 Introduction to statistical estimation and estimators
 Biased and unbiased estimators
 Einstein/Wiener Khintchine Theorem
 Estimation of autocorrelations
 Means and variances of periodograms
 Smoothed spectral estimates, leakage

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4) Modern spectrum estimation methods
 Introduction to modern spectral estimation:
Principles and approaches
 Cramer-Rao Lower Bound (CRLB) and Efficient
estimators
 The Maximum Entropy Method (MEM) or
Autoregressive Power Spectrum Estimation:
Principles.
 The MEM equations and Levinson/Durbin
algorithm

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4) Modern spectrum estimation methods
(continued)
 Introduction to Linear Prediction
 Linear Predictive Coding using covariances
and correlations
 Cholesky decomposition
 Lattice Filters
 Linear Prediction of Speech Signals

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5) Adaptive signal processing
 Introduction to adaptive signal processing
 Objective measures of goodness
 Least squares and consequences
 Steepest descent
 The LMS and RLS algorithms
 Kalman Filters

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6) Applications
 Communications
 Biomedical
 Seismic
 Audio/Music

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DIGITAL FILTERS
Digital Filters
 In this course you will learn:
 How to choose an appropriate filter
response.
 Why Butterworth responses are maximally
flat.
 Why Chebyshev and Elliptic responses are
equiripple.
 When to choose an IIR and when an FIR
filter

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 How do you design FIR and IIR filters from
specifications on amplitude performance?
 What are multirate systems and their
properties? What is interpolation /
Upsampling and Decimation / Downsampling?
 How do you design efficient Decimation and
Interpolation systems?
 What are frequency transformations and how
do you design these?
 How accurate is the DFT as a spectrum
estimator?

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 What are short FFT algorithms?
 How do you choose the required
wordlength?
 What are Fast Convolutions and how
are they realised?
 How do you deal with a DSP problem in
practice?

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Course content
Assumed DSP background
DSP Background folder
 1-Introduction
 2-z transform
 3-transfer functions
 4-Signal Flow Graphs
 5-digital filters intro

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Course content
2-Digital Filter Design
 1-Digital Filters (FIR)
 2-Digital Filters (IIR)
3-Multirate
1-Interpolation_Decimation

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Course content
4-Tranforms
 1-DFT
 2-DFT_one2two
 3-general transforms
 4-Wavelets
5-Finite Wordlength
 1-Finite Wordlength

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Course content
6-Spectrum Estimation (Assumed
background in Mathematical
Background folder)
 1-Fourier transform & DFT
 2-FFT-based Power Spectrum Estimation
 3-Modern Spectrum Estimation
 4-Intro-Estimation
 5-Eigen-based methods
 6-A Prediction Problem

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Course content
7-Adaptive Signal Processing
 1-Adaptive Signal Processing
8-Applications
 1-Applications
 2-Applications

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Digital Signal Processing &
Digital Filters
BOOKS
 Main Course text books: Digital Signal
Processing: A computer Based Approach, S K
Mitra, McGraw Hill
 Mathematical Methods and Algorithms for
Signal Processing, Todd Moon, Addison
Wesley
 Other books:
 Digital Signal Processing, Roberts & Mullis,
Addison Wesley
 Digital Filters, Antoniou, McGraw Hill

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Analogue Vs Digital Signal Processing
Reliability:
Analogue system performance degrades due to:
 Long term drift (ageing)
 Short term drift (temperature?)
 Sensitivity to voltage instability.
 Batch-to-Batch component variation.
 High discrete component count
Interconnection failures

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Digital Systems:
 No short or long term drifts.
 Relative immunity to minor power supply
variations.
 Virtually identical components.
 IC’s have > 15 year lifetime
 Development costs
 System changes at design/development
stage only software changes.
 Digital system simulation is realistic.

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Power aspects
 Size
 Dissipation
 DSP chips available as well as ASIC/FPGA
realisations

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Applications
Radar systems & Sonar systems
 Doppler filters.
 Clutter Suppression.
 Matched filters.
 Target tracking.
 Identification

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DIGITAL FILTERS
Image Processing
 Image data compression.
 Image filtering.
 Image enhancement.
 Spectral Analysis.
 Scene Analysis / Pattern recognition.

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Biomedical Signal Analysis
 Spatial image enhancement. (X-rays)
 Spectral Analysis.
 3-D reconstruction from projections.
 Digital filtering and Data compression.

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Music
 Music recording.
 Multi-track “mixing”.
 CD and DAT.
 Filtering / Synthesis / Special effects.

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Seismic Signal Analysis
 Bandpass Filtering for S/N
improvement.
 Predictive deconvolution to extract
reverberation characteristics.
 Optimal filtering. (Wiener and Kalman.)

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Telecommunications and Consumer
Products
These are the largest and most
pervasive applications of DSP and
Digital Filtering
 Mobile Communications
 Digital Recording
 Digital Cameras
 Blue Tooth or similar