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.
J.S. Bach chorales has been the main corpus in computer music that serves as a starting point to tackle full-fledged counterpoint. A wide range of approaches have been used to generate music in the style of Bach chorales, for example rule-based and instance-based approaches such as Cope's recombinancy method @cite_1 . This method involves first segmenting existing Bach chorales into smaller chunks based on music theory, analyzing their function and stylistic signatures and then re-concatenating the chunks into new coherent works. Other approaches range from constraint-based @cite_14 to statistical methods @cite_36 . In addition, @cite_39 gives a comprehensive survey of AI methods used not just for generating Bach chorales, but also algorithmic composition in general.
@cite_36 @cite_14 @cite_1 @cite_39
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We survey works on the musical problem of automatic harmonization. This problem, which consists in creating musical scores which satisfy given rules of harmony, has been the object of numerous studies, most of them using constraint techniques in one way or another. We outline the main results obtained and the current status of this category of problems. A brief background of automated music Musical style and linguistics LISP programming and object orientation Style replication Musical examples Cybernetic composition. Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.