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The use of machine learning in artistic music generation leads tocontroversial discussions of the quality of art, for which objectivequantification is nonsensical. We therefore consider a music-generatingalgorithm as a counterpart to a human musician, in a setting where reciprocalinterplay is to lead to new experiences, both for the musician and theaudience. To obtain this behaviour, we resort to the framework of recurrentVariational Auto-Encoders (VAE) and learn to generate music, seeded by a humanmusician. In the learned model, we generate novel musical sequences byinterpolation in latent space. Standard VAEs however do not guarantee any formof smoothness in their latent representation. This translates into abruptchanges in the generated music sequences. To overcome these limitations, weregularise the decoder and endow the latent space with a flat Riemannianmanifold, i.e., a manifold that is isometric to the Euclidean space. As aresult, linearly interpolating in the latent space yields realistic and smoothmusical changes that fit the type of machine--musician interactions we aim for.We provide empirical evidence for our method via a set of experiments on musicdatasets and we deploy our model for an interactive jam session with aprofessional drummer. The live performance provides qualitative evidence thatthe latent representation can be intuitively interpreted and exploited by thedrummer to drive the interplay. Beyond the musical application, our approachshowcases an instance of human-centred design of machine-learning models,driven by interpretability and the interaction with the end user.

Flat Latent Manifolds for Human-machine Co-creation of Music