Constraint programming systems for modeling music theories and composition

Anders, Torsten; Miranda, Eduardo Reck

doi:10.1145/1978802.1978809

Cited by 31 publications

(21 citation statements)

References 39 publications

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“…Since knowledge about musical composition has traditionally been structured as sets of more or less formalized rules for manipulating musical symbols (Anders & Miranda, 2011), knowledge-based and rule systems come as a natural way to implement algorithmic composition. In fact, it is extremely common for algorithmic composition systems to include some kind of composition rules at some point of the workflow.…”

Section: Symbolic Knowledge-based Systems and Related Methodsmentioning

confidence: 99%

“…Some review both analysis and composition by computer with AI methods (Roads, 1985), while others discuss algorithmic composition from a point of view related to music theory and artistic considerations (Collins, 2009), or from the personal perspective of a composer (Langston, 1989;Dobrian, 1993;Pope, 1995;Maurer, 1999). Some of them provide an in depth and comprehensive view of a specific technique for algorithmic composition, as Anders and Miranda (2011) do for constraint programming, as Ames (1989) does for Markov chains, or as Santos et al (2000) do for evolutionary techniques, while some others are specialized in the comparison between paradigms for computational research on music, as Toiviainen (2000). Others offer a wide-angle (but relatively shallow) panoramic of the field (Papadopoulos & Wiggins, 1999), review the early history of the field (Loy & Abbott, 1985;Ames, 1987;Burns, 1994), or analyze methodologies and motivations for algorithmic composition (Pearce et al, 2002).…”

Section: Motivationmentioning

confidence: 99%

“…Previously referenced work, as Steels's (1979), Levitt's (1981), Schottstaedt's (1989) and Löthe's (1999) can be seen as part of a gradual trend towards the formulation of musical problems as CSPs 17 , although constraint logic programming (CLP) came to be the tool of choice to solve CSPs. Good surveys on CLP for algorithmic composition have been written by Pachet and Roy (2001) and Anders and Miranda (2011). Ebcioğlu worked for many years in this area, achieving notable results.…”

Section: Constraint Satisfactionmentioning

confidence: 99%

See 2 more Smart Citations

AI Methods in Algorithmic Composition: A Comprehensive Survey

Fernández

Vico

2013

jair

213

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: Symbolic Knowledge-based Systems and Related Methodsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Constraint Satisfactionmentioning

confidence: 99%

See 1 more Smart Citation

AI Methods in Algorithmic Composition: A Comprehensive Survey

Fernández

Vico

2013

jair

213

113

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“…Constraint programming was extensively used for melody harmonization problems, see [4]. Constraint programming was so far less considered for interactive, real time applications.…”

Section: Sequencing Constraintsmentioning

confidence: 99%

“…• Melody 3 is a short scale consisting of 5th intervals, still in E minor, and is represented by the following sequence: 4,11,16,23,28,23,16,11,4. This melody is added to the corpus to allow intervallic variety.…”

Section: Melody Generationmentioning

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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Brain–Computer Music Interfacing: Interdisciplinary Research at the Crossroads of Music, Science and Biomedical Engineering

Miranda

2014

Guide to Brain-Computer Music Interfacing

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Research into Brain-Computer Music Interfacing (BCMI) involves three major challenges: the extraction of meaningful control information from signals emanating from the brain, the design of generative music techniques that respond to such information and the definition of ways in which such technology can effectively improve the lives of people with special needs and address therapeutic needs. This chapter discussed the first two challenges, in particularly the music technology side of BCMI research, which has been largely overlooked by colleagues working in this field. After a brief historical account of the field, the author reviews the pioneering research into BCMI that has been developed at Plymouth University's Interdisciplinary Centre for Computer Music Research (ICCMR) within the last decade or so. The chapter introduces examples illustrating ICCMR's developments and glances at current work informed by cognitive experiments.

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Constraint programming systems for modeling music theories and composition

Cited by 31 publications

References 39 publications

AI Methods in Algorithmic Composition: A Comprehensive Survey

AI Methods in Algorithmic Composition: A Comprehensive Survey

Markov constraints: steerable generation of Markov sequences

Brain–Computer Music Interfacing: Interdisciplinary Research at the Crossroads of Music, Science and Biomedical Engineering

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