Music generation has generally been focused on either creating scores or interpreting them. We discuss differences between these two problems and propose that, in fact, it may be valuable to work in the space of direct performance generation: jointly predicting the notes and also their expressive timing and dynamics. We consider the significance and qualities of the dataset needed for this. Having identified both a problem domain and characteristics of an appropriate dataset, we show an LSTM-based recurrent network model that subjectively performs quite well on this task. Critically, we provide generated examples. We also include feedback from professional composers and musicians about some of these examples.
Perhaps it is precisely because music is so often perceived as a profoundly human endeavour that there has also been, in parallel, an ongoing fascination with automating its creation. This fascination long predates notions such as the Turing test (ostensibly for discriminating automation of the most human behaviour), and has spawned a range of efforts: from attempts at the formalization of unambiguously strict rules of composition to incorporation of complete random chance into scores and performances. The use of rules exemplifies the algorithmic (and largely deterministic) approach to music generation, one that is interesting and outside the scope of the current work; for background on this we refer the reader, for example, to the text by Nierhaus @cite_1 . Our present work, on the other hand, lies in a part of the spectrum that incorporates probability and sampling.
@cite_1
2952428868
Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (, 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.