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Ensemble music synchronization

Principal Supervisor: Dr Massimiliano Di Luca

Secondary Supervisor(s): Dr Mark Elliott 

University of Registration: University of Birmingham

BBSRC Research Themes: Understanding the Rules of Life (Neuroscience and Behaviour)

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Deadline: 4 January, 2024

Project Outline

The project is linked with the EPSRC-funded which aim is to create a virtual music ensemble.

In musical ensembles (such as piano duets, jazz trios, string quartets, rock groups, samba bands, and drum circles), there is no stable reference for performance timing, and musicians must time and synchronise their performance with each other. This project will expand the neuroscientific understanding of how humans synchronise with each other during ensemble music production and according to the underlying type of musical rhythm (e.g., Jacoby, McDermott, 2017). It will involve both an experimental component and a machine learning component to generate an agent-based computational synchronisation model. Taking inspiration from the discrete timing models of Wing and Kristofferson (1973) and the linear correction model of Vorberg and Schultz (2002) developed in finger-tapping performance, the student will build a synchronisation model (e.g., using continuous probability distributions and statistically optimal estimators). The student will have access to a database of hundreds of recorded performances of quartets, trios, and duets provided by a commercial partner in the project. The student will further be able to collect data by running a small number of performance recordings to tackle specific theoretical questions.


Wing, Alan M., and Alfred B. Kristofferson (1973). Response delays and the timing of discrete motor responses. Perception & Psychophysics 14.1 (1973): 5-12

Vorberg, D., & Schulze, H.-H. (2002). Linear phase-correction in synchronization: Predictions, parameter estimation, and simulations. Journal of Mathematical Psychology, 46(1), 56–87

Jacoby, Nori, and Josh H. McDermott (2017). Integer ratio priors on musical rhythm revealed cross-culturally by iterated reproduction. Current Biology 27.3 (2017): 359-370


  • Experimental psychology (Behavioural testing with humans, statistical analysis of behavioural data)
  • Music analysis (Digital signal processing)
  • Computational modelling
  • Machine learning / Artificial intelligence