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

Program Overview

---

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

Overview

This single module is drawn from our MSc/PgDip Acoustics programme. It is offered standalone to cater for the needs of those who only require training in one specialist area, e.g., for CPD purposes in relevant industries.

Introduction

Digital Signal Processing and Machine Learning aims to give you an understanding of how acoustic signals may be handled and processed digitally, considering the benefits and limitations. You will study the decomposition of signals in frequency and their manipulation using digital filters, including design and analysis techniques.

Course Details

• Attendance: Part-time
• Distance Learning: Available
• Course Duration: One semester
• Next Enrolment: January 2025

What You Will Learn

• Gain an understanding of how acoustic signals may be handled and processed digitally, including the benefits and limitations, and skills in digital filter design and analysis
• Develop knowledge of advanced signal processing methods based on adaptive filtering and machine learning, including an awareness of their basis and limitations and the skills to apply them
• Select, apply, and critique Machine Learning methods for acoustics

Teaching

• Learning is mainly delivered using tutorial and seminar groups
• Interaction is face-to-face wherever practical, but we also use web-based learning support packages (databases of materials, discussion boards etc.)
• There is a strong focus on guided self-learning

Assessment

• Each module features one substantial written assignment to test the learning outcomes and cover the syllabus
• The assignments are based around ‘open-ended’ design exercises and case studies based on realistic problems

School of Science, Engineering and Environment

• Rising to the challenge of a changing world, our postgraduate courses are designed to shape the next generation of urbanists, scientists, engineers, consultants and leaders
• Driven by industry and delivered by supportive programme teams, you can develop the knowledge and skills to take your career potential further

Facilities

• Campus-based students will experience a modern learning environment, featuring accessible lecture theatres, AV-equipped classrooms, computing suites and multimedia libraries
• Distance learners will experience our Collaborate Ultra platform, with real-time and on-demand materials
• As an acoustics student, you will interact with world-class facilities, including full and semi-anechoic chambers, a transmission suite, large and small reverberation chambers, an ITU standard listening room, audiometric test facilities and a range of advanced instrumentation and equipment

Industry Collaboration & Research

• When you study acoustics at Salford, you are also joining a community making a difference in industry, our local region and in our wider society
• Many of our academics and technicians who support your course also deliver collaborative, interdisciplinary, high-impact work in a range of local and global issues and challenges with noise, sound and vibration

Employment and Stats

• Acousticians with engineering, science and mathematical skills are in short supply, which puts you in a strong position to build a career
• Our Acoustics postgraduate single modules can help you specialise your knowledge in the areas of acoustics that you're most interested in to help your career development, e.g. for CPD in a specific area that you are currently employed in or are seeking a job in

Requirements

• Applicant Profile: Our Acoustics postgraduate modules are recommended for numerate science graduates and professionals who require education in specific areas of Acoustics
• English Language Requirements: All of our courses are taught and assessed in English. If English is not your first language, you must meet our minimum English language entry requirements
• International Students - Academic Technology Approval Scheme (ATAS): International students are required by the Home Office and/or the Foreign and Commonwealth Office (FCO) to apply for an Academic Technology Approval Scheme (ATAS) Certificate before they begin their studies

Entry Requirements

• Undergraduate Degree: An undergraduate award at 2:2 or above (or equivalent) in science, engineering or mathematics
• Alternative Entry Requirements: Accreditation of Prior Learning (APL)

Fees

• Type of Study: Distance learning UK, Part-time UK, Distance learning International, Part-time International
• Year: 2025/26
• Fees: £1,730 per 30 credits (Distance learning UK), £885 per 15 credits (Part-time UK), £2,840 per 30 credits (Distance learning International), £1,420 per 15 credits (Part-time International)

Additional Costs

• You should also consider further costs which may include books, stationery, printing, binding and general subsistence on trips and visits.

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 |