Unsupervised Music Emotion Tagging (Affective Computing)

Primary supervisor

Wai Peng Wong

Research area

Data Science and Artificial Intelligence

We are seeking a motivated PhD candidate to work on unsupervised music emotion tagging within the broader field of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model implementation, and rigorous validation.

Required knowledge

Candidate expectations:

  • Strong background in AI/ML, data science, or signal processing

  • Interest in music informatics, emotion modelling, or multimodal AI

  • Ability to implement and evaluate machine learning models independently

  • Commitment to reproducible research, critical analysis, and publication

Experience with deep learning, audio analysis, or affective computing is advantageous but not mandatory.

Learn more about minimum entry requirements.

Primary supervisor