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COMP47700

Academic Year 2026/2027

Speech and Audio (COMP47700)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Andrew Hines
Trimester:
Spring
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module provides an introduction to speech and hearing and how digital representations of speech and audio can be processed. The area of speech and audio processing has evolved rapidly with applications like Voice over IP (e.g. Skype), media streaming (e.g. Spotify, YouTube) using encoding to optimise network bandwidth resources while maintaining user's the quality of experience. Advances in automatic speech recognition and speech synthesis have allowed speech controlled applications (e.g. Siri, Alexa) possible.

Data understanding is an important step in preprocessing and feature selection for predictive modelling. Machine learning models for applications such as automatic speech recognition, speech analysis and quality of experience prediction rely on understanding the salient properties of discrete time series signals.

This module provides theoretical grounding, practical knowledge, and hands on experience using high level programming languages (e.g. Python) to allow students to apply domain knowledge from speech and audio to data driven analytics. Fundamental analysis and processing methods for speech and audio applications will be explored. This module takes a hands-on approach making use of third-party libraries and APIs.

The module covers the following core topics:
• Introduction to speech and audio processing
• Basic audio processing
• Speech
• The human auditory system
• Psychoacoustics
• Speech communications
• Audio analysis
• It also introduces advanced topics and current research within the field of speech and audio processing. For example:
– Psychoacoustic modelling
– Speech and Audio Quality
– Objective models of intelligibility and quality
– Speaker recognition
– Spatial audio

About this Module

Learning Outcomes:

1. Analyse speech and audio signals and features
2. Articulate the characteristics of speech, speech production and speech understanding
3. Describe the signal characteristics of speech and audio signals using appropriate terminology
4. Recognise and reflect on the relationship between the underlying theories and start-of-the-art research
5. Apply signal processing algorithms to speech and audio signals
6. Create programmes to conduct experiments on speech and audio samples building on third software libraries

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Critical Thinking

 The ability to question norms, practices and opinions; to reflect on one’s own values, perceptions and actions. Competent

Systems Thinking

 The ability to recognize and understand relationships; to analyse complex systems; to think of how systems are embedded within different domains and different scales; and to deal with uncertainty. Competent

Integrated Problem Solving

 The overarching ability to apply different problem-solving frameworks to complex sustainability problems and develop viable, inclusive and equitable solution options that promote sustainable development, integrating the competencies in this list. Competent

Digital Literacy and Judgement

 The ability to access, evaluate, create and communicate information in digital environments; to engage critically, ethically and responsibly with digital technologies and digital information; to understand their opportunities, limitations, risks and impact on individual’s digital identities; and to exercise informed judgement in digital participation and decision-making. Competent

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Competent

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

25
Autonomous Student Learning

66
Lectures

18
Seminar (or Webinar)

6
Total

115

Approaches to Teaching and Learning:
active/task-based learning; lectures; lab/tutorial work; enquiry & problem-based learning; student presentations.
If students are permitted to use generative AI tools in assignments, that will be indicated in the assignment specification.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Assignment(Including Essay): 6 lab assignments Week 2, Week 3, Week 4, Week 5, Week 6, Week 8, Week 9, Week 10 Graded No
20
 No
Exam (In-person): Closed book exam. short definition, essay and problem based questions End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
80
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Ian Vince McLoughlin (2016), Speech and Audio Processing: A MATLAB-based Approach, 2nd edition, Cambridge University Press (EBook available through UCD library)
Ben Gold, Nelson Morgan, Dan Ellis, (2011), Speech and Audio Signal Processing: Processing and Perception of Speech and Music, 2nd edition, Wiley

Name Role
Dr Carl Tolentino Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 23, 24, 25, 26, 29, 31, 32, 33 Fri 14:00 - 15:50
Spring Practical Offering 1 Week(s) - 20, 21, 23, 24, 25, 26, 29, 31, 32, 33 Mon 13:00 - 14:50
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