AI Methods in Algorithmic Composition: A Comprehensive Survey

By Jose D. Fernández, Francisco J. Vico. Journal of Artificial Intelligence Research, 2013

344 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3839aae244763195f1437e7c00d2c6c8f5e3b549

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Citations in the corpus

Counterpoint by Convolution (2019, methodology): “ad ends. Statistical models of music, which is our current focus, is one of the many computational approaches that can help composers try out ideas more quickly, thus reducing the cost of exploration [8]. Whereas previous work in statistical music modeling has relied mainly on sequence models such a”
Deep Learning Techniques for Music Generation - A Survey (2017, background): “• Roads’ book about computer music [146]; • Cope’s [24], Dean and McLean’s [30] and/or Nierhaus’ books [132] about algorithmic composition; • a recent survey about AI methods in algorithmic composition [44]; and • Cope’s book about models of musical creativity [25].”
Style Imitation and Chord Invention in Polyphonic Music with Exponential Families (2016, background): “Many models of polyphonic music have been proposed since the 50s (see [11] for a comprehensive survey), starting with the famous Illiac Suite, which used Markov chains to produce 4-voice music, controlled by hand-made rules [15].”
A Functional Taxonomy of Music Generation Systems (2017, methodology): “Although several survey papers on automatic music generation [Papadopoulos and Wiggins 1999; Nierhaus 2009; Fernández and Vico 2013] exist, researchers still debate the kinds of musical tasks that can be performed by machines and the degree to which satisfactory outcomes can be achieved.”
Learning a Latent Space of Multitrack Measures (2018, methodology): “Prior to the recent neural network renaissance, most systems, such as that of Cope [9], used human-encoded rules, Markov models, or a few other method categories as described by Fernández and Vico [13] and Papadopoulos and Wiggins [29] in recent surveys.”
Human-AI Co-Creation in Songwriting (2020, methodology): “Within the music domain, there has been a long history of using AI techniques to model music composition [23, 31, 53, 54], by assisting in composing counter-point [22, 32], harmonizing melodies [13, 43, 51], more general inﬁlling [29, 35, 41, 61], exploring more adven-turous chord progressions [27,…”