The use of machine learning in artistic music generation leads to controversial discussions of the quality of art, for which objective quantification is nonsensical. We therefore consider a music-generating algorithm as a counterpart to a human musician, in a setting where reciprocal interplay is to lead to new experiences, both for the musician and the audience. To obtain this behaviour, we resort to the framework of recurrent Variational Auto-Encoders (VAE) and learn to generate music, seeded by a human musician. In the learned model, we generate novel musical sequences by interpolation in latent space. Standard VAEs however do not guarantee any form of smoothness in their latent representation. This translates into abrupt changes in the generated music sequences. To overcome these limitations, we regularise the decoder and endow the latent space with a flat Riemannian manifold, i.e., a manifold that is isometric to the Euclidean space. As a result, linearly interpolating in the latent space yields realistic and smooth musical 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 music datasets and we deploy our model for an interactive jam session with a professional drummer. The live performance provides qualitative evidence that the latent representation can be intuitively interpreted and exploited by the drummer to drive the interplay. Beyond the musical application, our approach showcases an instance of human-centred design of machine-learning models, driven by interpretability and the interaction with the end user.
翻译:艺术音乐创作中机器学习的使用导致对艺术质量的争议性讨论,这种讨论的客观量化是非明智的。 因此,我们认为一种音乐生成算法与人类音乐家相对应,在相互互动导致音乐家和观众产生新经验的环境中,相互影响使音乐家和观众产生新经验。 为了获得这一行为,我们采用由人类音乐家播种的反复变式自动进化器(VAE)框架,并学习产生音乐,由人类音乐家来播种。在学习的模型中,我们通过暗中空间的互调产生新的音乐序列。标准VAE并不保证其潜质代表形式的任何顺利性。这转化为所制作音乐序列的突然变化。为了克服这些局限性,我们调整了解调器,并用平坦的流体多姿势多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多姿多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多语多。