2 papers
cs.LG2025
Conditional Diffusion as Latent Constraints for Controllable Symbolic Music Generation
Matteo Pettenó, Alessandro Ilic Mezza, Alberto Bernardini
Recent advances in latent diffusion models have demonstrated state-of-the-art performance in high-dimensional time-series data synthesis while providing flexible control through co…
cs.LG2025
On the Joint Minimization of Regularization Loss Functions in Deep Variational Bayesian Methods for Attribute-Controlled Symbolic Music Generation
Matteo Pettenó, Alessandro Ilic Mezza, Alberto Bernardini
Explicit latent variable models provide a flexible yet powerful framework for data synthesis, enabling controlled manipulation of generative factors. With latent variables drawn fr…