Proper research, development and evaluation of AI-based generative systems of music that focus on
performance or composition require active user-system interactions. To include a diverse group of users that
can properly engage with a given system, researchers should provide easy access to their developed systems.
Given that many users (i.e. musicians) are non-technical to the field of AI and the development frameworks
involved, the researchers should aim to make their systems accessible within ...
Proper research, development and evaluation of AI-based generative systems of music that focus on
performance or composition require active user-system interactions. To include a diverse group of users that
can properly engage with a given system, researchers should provide easy access to their developed systems.
Given that many users (i.e. musicians) are non-technical to the field of AI and the development frameworks
involved, the researchers should aim to make their systems accessible within the environments commonly used
in production/composition workflows (e.g. in the form of plugins hosted in digital audio workstations).
Unfortunately, deploying generative systems in this manner is highly expensive. As such, researchers with
limited resources are often unable to provide easy access to their works, and subsequently, are not able to
properly evaluate and encourage active engagement with their systems. Facing these limitations, we have been
working on a solution that allows for easy, effective and accessible deployment of generative systems. To this
end, we propose a wrapper/template called NeuralMidiFx, which streamlines the deployment of neural
network based symbolic music generation systems as VST3 plugins. The proposed wrapper is intended to
allow researchers to develop plugins with ease while requiring minimal familiarity with plugin development.
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