Tom Collins


My research interests lie at the intersection of music, computing, and psychology. I devise experiments and develop computational models to address:

1. Automatic generation of stylistic compositions, incorporation in software, and the technology's effect on student education and work;

2. Musical expectancy and listening choices (for symbolic/audio input and different listener backgrounds/contexts);

3. Discovery of repeated patterns in music, visual, and other domains;

4. Question answering on music scores (given a query like 'perfect cadence followed by homophonic texture', retrieve the relevant events from a digital score);

5. Use of music interfaces implemented with the Web Audio Framework and related JavaScript packages.

See my lab website for more details and full publication list:

If you are interested in joining the team, you are welcome to get in touch to discuss opportunities. Over time we will be pushing the boundaries of music/cognitive psychology, music informatics research, and music composition.

Representative Publication per Each of the Above Categories

1. Peter Harrison, Tom Collins, and Daniel Müllensiefen. Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation. Nature Scientific Reports, 7:3618, 2017.

2. Tom Collins, Barbara Tillmann, Frederick S. Barrett, Charles Delbé, and Petr Janata. A combined model of sensory and cognitive representations underlying tonal expectation: from audio signals to behaviorPsychological Review, 121(1):33-65, 2104. 

3. Tom Collins, Andreas Arzt, Harald Frostel, and Gerhard Widmer. Using geometric symbolic fingerprinting to discover distinctive patterns in polyphonic music corpora. In David Meredith (Ed), Computational Music Analysis, pp. 445-474, Berlin, 2016.

4. Tom Collins. Stravinsqi/De Montfort University at the MediaEval 2014 C@merata task. In Proceedings of the MediaEval 2014 Workshop, Barcelona, 2014.

5. Tom Collins and Christian Coulon. Using empirical analysis of music corpora to optimize Web Audio playback. In Proceedings of the Web Audio Conference, 4 pages, Atlanta, GA, 2016.

Visiting Assistant Professor
Chandler Ullmann 223
Ph.D., Computing, The Open University, Milton Keynes, UK 2011
BA, Mathematics and Statistics, Keble College, Oxford 2008
BA, Music, Robinson College, Cambridge 2005