Single Module Digital Signal Processing and Machine Learning

Program Overview

This Level 7 Single Module Digital Signal Processing and Machine Learning program, offered through distance learning, provides advanced training in signal processing techniques for acoustic applications. Students will gain expertise in adaptive filtering, machine learning algorithms, and their use in acoustics, preparing them for careers in acoustics consultancies, research, and audio design.

Program Outline

Degree Overview:

Title:

Single Module Digital Signal Processing and Machine Learning, Level 7 (15 Credits)

School:

School of Science, Engineering and Environment

Mode:

Distance Learning

Duration:

One semester

Next Enrolment:

January 2025

Objectives:

• Gain an understanding of digital signal processing methods for acoustic signals, including benefits, limitations, and skills in design and analysis.
• Develop knowledge of advanced signal processing methods based on adaptive filtering and machine learning, including awareness of their basis, limitations, and application skills.
• Select, apply, and critique Machine Learning methods for acoustics applications.

Description:

This module provides an in-depth understanding of advanced signal processing techniques in the field of acoustics. Students will explore adaptive filtering, machine learning algorithms, and their application to acoustic problems. The program is designed for individuals seeking specialized training in this area, either for professional development or as part of the MSc/PgDip Acoustics program.

Outline:

Content:

• Signal decomposition in frequency and manipulation using digital filters.
• Design and analysis techniques for digital filters.
• Advanced signal processing using adaptive filtering and machine learning.
• Understanding of acoustic signals, their properties, and processing methods.

Structure:

• Part-time, distance learning program.
• Weekly program of directed reading supported by study guides and tutorial questions.
• Occasional pre-recorded lecture content following the 'flipped classroom' approach.
• Co-taught format for in-person and online cohorts, streamed live with real-time interaction with tutors for both groups.

Schedule:

• Classes are timetabled Monday to Friday between 9am and 6pm (UK time).
• Flexibility for learners outside these hours through session recordings and discussion forums.

Modules:

• Module Name: Digital Signal Processing and Machine Learning
• Credits: 15
• Description: Covers digital signal processing fundamentals, advanced techniques like adaptive filtering, and applications of machine learning in the context of acoustics.

Teaching:

• Face-to-face and online cohorts taught together through live streaming.
• Interactive learning through tutorial and seminar groups.
• Web-based learning support materials including databases, discussion boards, etc.
• Emphasis on guided self-learning.

Faculty:

• Taught by academics and technicians experienced in collaborative research and practical applications in acoustics.

Other:

• May be taken as a standalone module or as part of the MSc/PgDip Acoustics program.
• Industry collaborations andresearch opportunities available.
• Graduates often employed in acoustics consultancies, research, development, and audio design.

| Type of study | Year | Fees | |---|---|---| | Distance learning UK | 2024/25 | £1665 per 30 credits | | Part-time UK | 2024/25 | £850 per 15 credits | | Distance Learning International | 2024/25 | £2730 per 30 credits | | Part-time International | 2024/25 | £1,365 per 15 credits |