Teaching rule‐based algorithmic composition: the PWGL library cluster rules

This paper presents software suitable for undergraduate students to implement computer programs that compose music. The software offers a low floor (students easily get started) but also a high ceiling (complex compositional theories can be modelled). Our students are particularly interested in tonal music: such aesthetic preferences are supported, without stylistically restricting users of the software. We use a rule‐based approach (constraint programming) to allow for great flexibility. Our software Cluster Rules implements a collection of compositional rules on rhythm, harmony, melody, and counterpoint for the new music constraint system Cluster Engine by Örjan Sandred. The software offers a low floor by observing several guidelines. The programming environment uses visual programming (Cluster Rules and Cluster Engine extend the algorithmic composition system PWGL). Further, music theory definitions follow a template, so students can learn from examples how to create their own definitions. Finally, students are offered a collection of predefined rules, which they can freely combine in their own definitions. Music Technology students, including students without any prior computer programming experience, have successfully used the software. Students used the musical results of their computer programs to create original compositions. The software is also interesting for postgraduate students, composers and researchers. Complex polyphonic constraint problems are supported (high ceiling). Users can freely define their own rules and combine them with predefined rules. Also, Cluster Engine’s efficient search algorithm makes advanced problems solvable in practice

A constraint-based framework to model harmony for algorithmic composition

Music constraint systems provide a rule-based approach to composition. Existing systems allow users to constrain the harmony, but the constrainable harmonic information is restricted to pitches and intervals between pitches. More abstract analytical information such as chord or scale types, their root, scale degrees, enharmonic note representations, whether a note is the third or fifth of a chord and so forth are not supported. However, such information is important for modelling various music theories. This research proposes a framework for modelling harmony at a high level of abstraction. It explicitly represents various analytical information to allow for complex theories of harmony. It is designed for efficient propagation-based constraint solvers. The framework supports the common 12-tone equal temperament, and arbitrary other equal temperaments. Users develop harmony models by applying user-defined constraints to its music representation. Three examples demonstrate the expressive power of the framework: (1) an automatic melody harmonisation with a simple harmony model; (2) a more complex model implementing large parts of Schoenberg’s tonal theory of harmony; and (3) a composition in extended tonality. Schoenberg’s comprehensive theory of harmony has not been computationally modelled before, neither with constraints programming nor in any other way.

Compositions created with constraint programming

This chapter surveys music constraint programming systems, and how composers have used them. The chapter motivates and explains how users of such systems describe intended musical results with constraints. This approach to algorithmic composition is similar to the way declarative and modular compositional rules have successfully been used in music theory for centuries as a device to describe composition techniques. In a systematic overview, this survey highlights the respective strengths of different approaches and systems from a composer's point of view, complementing other more technical surveys of this field. This text describes the music constraint systems PMC, Score-PMC, PWMC (and its successor Cluster Engine), Strasheela and Orchidée -- most are libraries of the composition systems PWGL or OpenMusic. These systems are shown in action by discussing the composition process of specific works by Jacopo Baboni-Schilingi, Magnus Lindberg, Örjan Sandred, Torsten Anders, Johannes Kretz and Jonathan Harvey

Compositions created with constraint programming

This chapter surveys music constraint programming systems, and how composers have used them. The chapter motivates and explains how users of such systems describe intended musical results with constraints. This approach to algorithmic composition is similar to the way declarative and modular compositional rules have successfully been used in music theory for centuries as a device to describe composition techniques. In a systematic overview, this survey highlights the respective strengths of different approaches and systems from a composer's point of view, complementing other more technical surveys of this field. This text describes the music constraint systems PMC, Score-PMC, PWMC (and its successor Cluster Engine), Strasheela and Orchidée -- most are libraries of the composition systems PWGL or OpenMusic. These systems are shown in action by discussing the composition process of specific works by Jacopo Baboni-Schilingi, Magnus Lindberg, Örjan Sandred, Torsten Anders, Johannes Kretz and Jonathan Harvey

Harmony and Technology Enhanced Learning

New technologies offer rich opportunities to support education in harmony. In this chapter we consider theoretical perspectives and underlying principles behind technologies for learning and teaching harmony. Such perspectives help in matching existing and future technologies to educational purposes, and to inspire the creative re-appropriation of technologies

AI Methods in Algorithmic Composition: A Comprehensive Survey

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.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía