An experiment in musical composition

Brooks, Frank P.; Hopkins, A. L.; Neumann, Philipp; Wright, William V.

doi:10.1109/tec.1957.5222016

Cited by 69 publications

(43 citation statements)

References 3 publications

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“…However, there is at least one earlier, unpublished work by Caplin and Prinz in 1955 (Ariza, 2011), which used two approaches: an implementation of Mozart's dice dame and a generator of melodic lines using stochastic transitional probabilities for various aspects of the composition. Another commonly cited example by Brooks et al (1957) explored the potential of the Markoff 8 chain method.…”

Section: The Early Yearsmentioning

confidence: 99%

AI Methods in Algorithmic Composition: A Comprehensive Survey

Fernández

Vico

2013

jair

207

113

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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.

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Section: The Early Yearsmentioning

confidence: 99%

AI Methods in Algorithmic Composition: A Comprehensive Survey

Fernández

Vico

2013

jair

207

113

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“…Since Shannon's seminal works on information theory, the Markovian aspects of musical sequences has long been acknowledged, see e.g. [8]. Many attempts to model musical style have therefore exploited Markov chains in various ways [21].…”

Section: Motivationmentioning

confidence: 99%

“…8). 8 An application to interactive doodling. First, a shape is drawn by the user (left), and the system builds a Markov model from this shape.…”

Section: Other Applicationsmentioning

confidence: 99%

Markov constraints: steerable generation of Markov sequences

Pachet¹,

Roy²

2010

Constraints

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Markov chains are a well known tool to model temporal properties of many phenomena, from text structure to fluctuations in economics. Because they are easy to generate, Markovian sequences, i.e. temporal sequences having the Markov property, are also used for content generation applications such as text or music generation that imitate a given style. However, Markov sequences are traditionally generated using greedy, left-to-right algorithms. While this approach is computationally cheap, it is fundamentally unsuited for interactive control. This paper addresses the issue of generating steerable Markovian sequences. We target interactive applications such as games, in which users want to control, through simple input devices, the way the system generates a Markovian sequence, such as a text, a musical sequence or a drawing. To this aim, we propose to revisit Markov sequence generation as a branch and bound constraint satisfaction problem (CSP). We propose a CSP formulation of the basic Markovian hypothesis as elementary Markov Constraints (EMC). We propose algorithms that achieve domain-consistency for the propagators of EMCs, in an event-based implementation of CSP. We show how EMCs can be combined to estimate the global Markovian probability of a whole sequence, and accommodate for different species of Markov generation such as fixed order, variable-order, or smoothing. Such a formulation, although more costly than traditional greedy generation algorithms, yields the immense advantage of being naturally steerable, since control specifications can be represented by arbitrary additional constraints, without any modification of the generation algorithm. We illustrate our approach on simple yet combinatorial chord sequence and melody generation problems and give some performance results.

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“…The idea to use Markov processes to analyze and generate music is not new, and dates from the very beginning of computer science and information theory (Brooks et al, 1957). Many refinements to this idea were brought to these early models, culminating with the composition systems of David Cope (1996).…”

Section: Continuator: From Frustration To Flowmentioning

confidence: 99%

The future of content is in ourselves

Pachet

2008

Comput. Entertain.

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An experiment in musical composition

Cited by 69 publications

References 3 publications

AI Methods in Algorithmic Composition: A Comprehensive Survey

AI Methods in Algorithmic Composition: A Comprehensive Survey

Markov constraints: steerable generation of Markov sequences

The future of content is in ourselves

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