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The University of Southampton

ISVR6130 Signal Processing

Module Overview

Signals such as audio, music, sonar, image and video convey information about physical quantities that vary over time and space. Signals can, for example, describe acoustic vibrations or radio waves, and thus play an important role throughout engineering. To help engineers to record, process, transmit and understand this information, computational and mathematical tools are used. In this module, you will study different principles used to analyse signals and will learn how signals are affected by certain systems. To do this, you will learn about fundamental concepts such as frequency analysis, spectral analysis and digital systems theory. In frequency analysis, a signal is decomposed into different frequencies. As many systems affect different frequencies independently, such a description allows us to study a system by analysing how it affects different frequencies. For example, a loudspeaker can be described as a system and an engineer might be interested in designing this system so that it does not unduly boost or attenuate different frequencies, which would colour the sound of the speaker. Spectral analysis also reveals the frequency content in a signal, but also takes account of the fact that most signals show significant random variation. These techniques are used, for example, to study physical systems and can be used to compare and relate different signals, such as, for example, the electrical signal driving a speaker and the acoustic signal reaching you ears. Nowadays, when we analyse or process signals, we tend to use digital computers. Analogue signals thus need to be converted first to a digital representation. To understand when this is possible and to appreciate the errors that can occur if the correct requirements are not met, a more detailed understanding of this conversion process is required. This also leads to the related problem of analysing digital systems, using tools similar to those used for analogue, continuous systems.

Aims and Objectives

Learning Outcomes

Disciplinary Specific Learning Outcomes

Having successfully completed this module you will be able to:

  • Explain the main steps in the acquisition, analysis and interpretation of time histories.
  • Appreciate the generic nature of signal processing and the relevance and applicability of the methodology to engineering and many other fields.
  • Demonstrate sufficient mathematical expertise to underpin the study of signal processing.
  • Distinguish between conceptual ideas and principles and the mathematical language used.
  • Demonstrate confidence in being able to tackle data analysis problems systematically and to apply analytical and computational methods correctly.
  • Relate the analysis to applications and interpretation of the results.
  • Read, interpret, relate and use current literature on advanced signal processing.


Frequency analysis: Fourier series, the Fourier integral, the discrete Fourier transform and the fast Fourier transform (FFT). The effects of windowing, convolution and methods for sampling and signal reconstruction. Spectral analysis: Definitions and properties of the auto- and cross- spectra and correlation functions. Estimation methods for spectra, correlation functions and transfer functions. Digital systems theory. Classes of digital systems (AR, MA and ARMA), the z-transform, digital filter design.

Learning and Teaching

Teaching and learning methods

Lectures will be augmented with assessed laboratory sessions. These lab sessions will be computer based tasks which will be written up for assessment. There will be three lab based assessments, covering: frequency analysis, spectral analysis and digital systems theory.

Completion of assessment task27
Wider reading or practice27
Practical classes and workshops6
Follow-up work27
Preparation for scheduled sessions9
Total study time150

Resources & Reading list

Computer Requirement. This course requires access to a suite of computers on which MATLAB can be run. The computers should have microphones and headsets.



MethodPercentage contribution
Continuous Assessment 20%
Final Assessment  80%


MethodPercentage contribution
Set Task 100%


MethodPercentage contribution
Set Task 100%

Repeat Information

Repeat type: Internal & External

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