Apolo: a toolkit for generative music production and benchmarking

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The Apolo project is a direct response to recent developments in Artificial Intelligence, and Machine Learning when applied to the partial or complete automation of creative tasks, a field known as computational creativity, Creative AI, or Metacreation. In particular, recent advances in corpus-based generative music algorithms are leading to a revolution in how music is being created by professionals and amateurs alike. These increasingly powerful algorithms have not yet been applied much, mostly due to the complexities of machine learning, and the lack of an open source library, data management software, and graphical user interfaces for accessing these algorithms. The Apolo project aims to fill this gap providing user-friendly technologies that enable composers, sound designers, game developers, VR studios, amateur musicians as well as researchers and students to understand, apply, refine, benchmark, extend these new computational techniques in their own applications or research. The main outcomes of the Apolo project will be: - Generative algorithms that go beyond pastiche and style imitation using novelty and surprise search in combination with Deep Learning generative algorithms - High-level variables and cross-modal mapping strategies that can be used to steer the musical generation in interactive contexts be it for computer-assisted composition in digital audio workstations, or adaptive online generation in online applications (such as video games). - The ApoloC corpus manager will allow users (researchers, students, musicians, composer, and enthusiasts) to load and manipulate music compositions (in a variety of formats) to manage training sets for generative style imitation algorithms. - The ApoloG generation engine, will provide a graphic interface to select training sets, training algorithms, and specify the generative task and parameters. - Both ApoloC and ApoloG will rest on the Apolo library: an open source library implementing a collection of existing and new music generation algorithms. - The ApoloQ quantitative evaluation toolbox for style imitation will be integrated into the framework to help benchmark algorithms. With the shift from linear to non-linear media, users are now exposed to rich immersive and interactive experiences. Be it for gaming, education, or entertainment, interactive mediums are quickly outgrowing linear media (books, film, tv, ...). As nonlinear (interactive) mediums develop, the number of assets needed is growing exponentially. Besides its contribution to artificial intelligence, machine learning, computational creativity, computer music, and music information retrieval, the Apolo project will lay the ground for computer-assisted or completely procedural music generation approaches that are now required in a variety of software products (interactive systems, video games, ...), central to the digital economy and Canadian industry in these sectors.