* Corresponding author 1 Research applying machine learning to music modeling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and/or qualitative listening tests. Rarely does such work explicitly question and analyse its usefulness for and impact on real-world practitioners, and then build on those outcomes to inform the development and application of machine learning. This article attempts to do these things for machine learning applied to music creation. Together with practitioners, we develop and use several applications of machine learning for music creation, and present a public concert of the results. We reflect on the entire experience to arrive at several ways of advancing these and similar applications of machine learning to music creation.

The application of artificial intelligence (AI) to music stretches back many decades, and presents numerous unique opportunities for a variety of uses, such as the recommendation of recorded music from massive commercial archives, or the (semi-)automated creation of music. Due to unparalleled access to music data and effective learning algorithms running on high-powered computational hardware, AI is now producing surprising outcomes in a domain fully entrenched in human creativity—not to mention a revenue source around the globe. These developments call for a close inspection of what is occurring, and consideration of how it is changing and can change our relationship with music for better and for worse. This article looks at AI applied to music from two perspectives: copyright law and engineering praxis. It grounds its discussion in the development and use of a specific application of AI in music creation, which raises further and unanticipated questions. Most of the questions collected in this article are open as their answers are not yet clear at this time, but they are nonetheless important to consider as AI technologies develop and are applied more widely to music, not to mention other domains centred on human creativity.

Abstract. We extend our evaluation of generative models of music transcriptions that were first presented in Sturm, Santos, Ben-Tal, and Korshunova (2016). We evaluate the models in five different ways: 1) at the population level, comparing statistics of 30,000 generated transcriptions with those of over 23,000 training transcriptions; 2) at the practice level, examining the ways in which specific generated transcriptions are successful as music compositions; 3) as a "nefarious tester", seeking the music knowledge limits of the models; 4) in the context of assisted music composition, using the models to create music within the conventions of the training data; and finally, 5) taking the models to real-world music practitioners. Our work attempts to demonstrate new approaches to evaluating the application of machine learning methods to modelling and making music, and the importance of taking the results back to the realm of music practice to judge their usefulness. Our datasets and software are open and available at https://github.com/IraKorshunova/folk-rnn.

In order to identify a perceptually valid measure of rhythm complexity, we used five measures from information theory and algorithmic complexity to measure the complexity of 48 artificially generated rhythmic sequences. We compared these measurements to human implicit and explicit complexity judgments obtained from a listening experiment, in which 32 participants guessed the last beat of each sequence. We also investigated the modulating effects of musical expertise and general pattern identification ability. Entropy rate was correlated with implicit and explicit judgments, Kolmogorov complexity was highly correlated with explicit judgments, and scores on the implicit task were correlated with selfassessed musical perceptual abilities. A logistic regression showed main effects of entropy rate and musical training, and an interaction between entropy rate and musical training. These results indicate that information-theoretic concepts capture some salient features of human rhythm perception, and confirm the influence of musical expertise in the perception of rhythm complexity.

Réflexions sur la synesthésie, la perception et la cognition. Nous nous penchons dans cet article sur trois questions toujours actuelles : sur le rapport entre le monde physique et le monde perçu, sur la difficulté d’expliquer les différences individuelles dans la perception du monde environnant, et sur l’énigme de la compréhension de l’esprit d’autrui. En examinant la relation entre la synesthésie et les hallucinations, et entre les hallucinations et la perception normale, nous montrons que tous ces phénomènes ont bien plus en commun qu’on ne le supputait. Nous nous interrogeons ensuite sur la plausibilité d’une analyse fonctionnelle de la synesthésie, et examinons les mécanismes de différents types de perception ordinaire et extraordinaire. Nous soutenons que des mécanismes de même type que ceux impliqués dans les synesthésies pourraient intervenir tout un éventail de phénomènes perceptifs et cognitifs, et montrons l’utilité d’une telle approche eu égard à l’ubiquité dans la cognition humaine des processus qui formellement rappellent la synesthésie.