Audio and Signal Processing

Module overview

Every piece of recorded music, every voice call and every immersive soundtrack depends on engineers who understand how audio is captured, analysed, processed and reproduced. Behind the microphones, loudspeakers and digital tools lies a body of theory that turns sound into signals and signals back into sound. This module introduces that theory and, just as importantly, shows you how to put it to work on practical audio engineering problems. In the first part you will develop a set of signal processing techniques for analysing and processing audio, revisiting ideas from your other modules through the lens of audio technology and taking your first steps into digital signal processing. Computing tutorials will train you to apply these tools to real problems rather than treating them as abstract theory. In the second part you will turn to the systems that capture and reproduce sound, learning how channel quality is described objectively through measures such as noise and distortion, how these relate to the way we actually perceive sound, and how stereo and surround sound systems work in practice. By the end of the module, you will be able to analyse and process audio signals with confidence and explain how audio capture and reproduction systems perform against both objective and perceptual criteria. The topics here are treated at a foundational level by design, giving you the grounding for the more advanced analysis you will meet in the third and fourth years, and equipping you with skills valued across the audio, broadcast and communications industries.

Linked modules

Pre-requisites: FEEG1201 and MATH2048

Aims and Objectives

Learning Outcomes

Partial CEng Programme Level Learning Outcomes

Having successfully completed this module you will be able to:

Students must organise their work and resources when working on the summative Python computing assignment.
Students will be introduced to some aspects of communication/software security. Students will be asked to demonstrate their understanding of these aspects in the final exam.
Application of signal processing principles to the solution of practical problems of signal and system analysis, often by means of Python programming.
Study of various signal processing principles (Fourier analysis, convolution, correlation, etc.) and their application to analyse signals and systems. Study of fundamental of some audio technologies. Final exam on signal processing and audio technology theory, where students are asked to demonstrate their understanding of these topics. Python-based assignment on signal processing applications.
Students develop the ability to refer to information on Python programming that are available online during the module.
Students are required to explain their process and results, using appropriate technical language, when answering the summative assignment questions.
Students are encouraged to work in groups on their Python computing formative assignments
Application of signal processing principles and audio principles to the solution of theoretical problems. Final exam on signal processing and audio technology theory, where students are also asked to apply these principles to the solution of theoretical problems.
Student learn to combine different signal processing tools and to implement them numerically to solve practical problems. For example, student can combine spectral analysis, filter design, and convolution to remove noise from an audio signal. Python-based assignment, where students are asked to combine signal processing techniques to solve practical problems.

Disciplinary Specific Learning Outcomes

Having successfully completed this module you will be able to:

to apply the basic signal processing techniques to estimate the impulse response and frequency response function of a linear, time-invariant system and to interpret the results
to demonstrate a working knowledge of the concept of convolution, of the convolution theorem, of digital filters, and of some of their applications
to demonstrate a knowledge of the operating principles of sound reproduction and of its limitations
to understand the metrics and specifications associated with audio channel quality
to demonstrate a working knowledge of Fourier analysis and to apply it the analysis of audio signals and systems
to describe and explain the basic operating principles of a range of systems for audio data acquisition and reproduction
To demonstrate a knowledge of the basics of digital signals and digital signal processing.

Learning and Teaching

Teaching and learning methods

Module delivered over semester. This will include - Weekly lectures - 7 tutorial sessions focusing on Python implementation of DSP algorithms. - 3 computer-based laboratories on applications of digital signal processing

Study time
Type	Hours
Lecture	30
Tutorial	6
Practical classes and workshops	9
Completion of assessment task	40
Preparation for scheduled sessions	65
Total study time	150

Assessment

Assessment strategy

The assignment will require the application and critical understanding of the DSP algorithms that the students will have implemented during the tutorial sessions.

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Continuous Assessment	50%
Final Assessment	50%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Set Task	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Set Task	100%

Repeat Information

Repeat type: Internal & External