COMP47700 Speech and Audio

Academic Year 2022/2023

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

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Curricular information is subject to change

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

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; peer and group work; lectures; lab/tutorial work; enquiry & problem-based learning; student presentations. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Continuous Assessment: project work/assignments Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

100


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
• Peer review activities

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 Helard Becerra Lecturer / Co-Lecturer
Dr Alessandro Ragano 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, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 12:00 - 12:50
Practical Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 15:00 - 16:50
Spring