Computer Assisted Composition in Continuous Time
This addresses a challenge in computer-assisted music composition by enabling more flexible constraint handling, though it is incremental as it builds on prior discrete-time methods.
The paper tackles the problem of integrating sequence models of symbolic music with user-defined constraints in continuous time and arbitrary rhythm, introducing a particle filter scheme that outperforms a beam search baseline in statistical properties and human listening tests.
We address the problem of combining sequence models of symbolic music with user defined constraints. For typical models this is non-trivial as only the conditional distribution of each symbol given the earlier symbols is available, while the constraints correspond to arbitrary times. Previously this has been addressed by assuming a discrete time model of fixed rhythm. We generalise to continuous time and arbitrary rhythm by introducing a simple, novel, and efficient particle filter scheme, applicable to general continuous time point processes. Extensive experimental evaluations demonstrate that in comparison with a more traditional beam search baseline, the particle filter exhibits superior statistical properties and yields more agreeable results in an extensive human listening test experiment.