Thesis Info

Thesis Title
Score Following: An Artificially Intelligent Musical Accompanist
Anna Jordanous
2nd Author
3rd Author
MSc Artificial Intelligence
Number of Pages
University of Edinburgh
Thesis Supervisor
Dr Alan Smaill
Supervisor e-mail
smaill AT
Other Supervisor(s)
Language(s) of Thesis
Department / Discipline
Artificial Intelligence
Languages Familiar to Author
URL where full thesis can be found
score following, artificially intelligent musician, automatic accompaniment, hidden markov models, Max/MSP
Abstract: 200-500 words
Score Following is the process by which a musician can be tracked through their performance of a piece, for the purpose of accompanying the musician with the appropriate notes. This tracking is done by following the progress of the musician through the score (written music) of the piece, using observations of the notes they are playing. Artificially intelligent musical accompaniment is where a human musician is accompanied by a computer musician. The computer musician is able to produce musical accompaniment that relates musically to the human performance. Hidden Markov Models (HMMs) are a stochastic modelling tool that can be used to represent real-world systems in a variety of domains. This project discusses how HMMs can be used in the domain of Score Following and describes the construction and evaluation of a score following system that uses HMMs to implement score following. It explores the hypothesis that using an HMM to represent a musical score is an efficient and practical way to implement score following, and that in particular this method is suitable for providing real-time accompaniment to a human performer. The score followers developed during this project are tested and compared against other score following systems and against human musicians. The resulting performances support the project hypothesis to a large extent.