Performing Structured Improvisations with pre-trained Deep Learning Models
This addresses a problem for musicians seeking to incorporate AI models in live improvisations, but it appears incremental as it builds on existing models without major methodological breakthroughs.
The paper tackles the challenge of integrating pre-trained deep generative models into live structured music performances by respecting beat and harmony constraints, proposing a system that leverages musician expertise to enable practical use.
The quality of outputs produced by deep generative models for music have seen a dramatic improvement in the last few years. However, most deep learning models perform in "offline" mode, with few restrictions on the processing time. Integrating these types of models into a live structured performance poses a challenge because of the necessity to respect the beat and harmony. Further, these deep models tend to be agnostic to the style of a performer, which often renders them impractical for live performance. In this paper we propose a system which enables the integration of out-of-the-box generative models by leveraging the musician's creativity and expertise.