Audio and Signal Processing

Module overview

Acoustical engineers are required to demonstrate knowledge of the basic methods for acquisition, analysis, processing and reproduction of audio data and audio material. They must be familiar with the theoretical fundamentals of these techniques and be capable of applying them to practical engineering problems. The first part of this module covers a range of basic signal processing techniques for the analysis and processing of audio signals. The theory is reviewed of a number of topics that students have encountered in other modules with emphasis on their relevance to audio technologies, and elements of digital signal processing are introduced to students. Students are trained, especially through computing tutorials, to apply these theoretical tools to practical engineering problems. The second part of the module covers the fundamentals of systems and technologies for audio capturing and reproduction. The concept of channel quality and its objective descriptors (noise, distortion, etc.) are explained and put into relation to human sound perception, some aspects of which are introduced. The principles of operation of stereo and surround sound systems are introduced and demonstrated. It is emphasized that the theory of some of the topics is covered in this module at a basic level. An advanced theoretical analysis of these topics will be provided during the third- and fourth-year modules.

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:

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.
Students develop the ability to refer to information on Python programming that are available online during the module.
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.
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 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 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.
Students must organise their work and resources when working on the summative Python computing assignment.

Disciplinary Specific Learning Outcomes

Having successfully completed this module you will be able to:

to demonstrate a working knowledge of Fourier analysis and to apply it the analysis of audio signals and systems
To demonstrate a knowledge of the basics of digital signals and digital signal processing.
to describe and explain the basic operating principles of a range of systems for audio data acquisition and reproduction
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 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 understand the metrics and specifications associated with audio channel quality

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
Practical classes and workshops	9
Preparation for scheduled sessions	40
Completion of assessment task	30
Lecture	30
Tutorial	6
Revision	35
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