• A computational model for rule-based microtonal music theories and composition

      Anders, Torsten; Miranda, Eduardo Reck (New Music Inc., 2010)
    • Constraint application with higher-order programming for modeling music theories

      Anders, Torsten; Miranda, Eduardo Reck; University of Plymouth (MIT Press, 2010)
    • Constraint programming systems for modeling music theories and composition

      Anders, Torsten; Miranda, Eduardo Reck; University of Plymouth (Association for Computing Machinery (ACM), 2011-10)
    • Interfacing manual and machine composition

      Anders, Torsten; Miranda, Eduardo Reck (Routledge, 2009)
      Computer-aided composition (CAC) is situated somewhere in the middle between manual composition and automated composition that is performed autonomously by a computer program. Computers cannot make aesthetic decisions in their own right. They can only follow orders. Aesthetic decisions are made by composers, both via the design of computer programs and by manually controlling these programs. The latter plays an important part in CAC. The composition process typically involves much emending and revising: changing how a computer program is controlled is easier and allows for a more intuitive way of working than changing the program itself. This paper argues that constraint programming is a particularly suitable programming paradigm for flexibly interfacing manual and machine composition.
    • A model of musical motif

      Anders, Torsten (Springer, 2009)
    • Modelling durational accents for computer-aided composition

      Anders, Torsten; University of Bedfordshire (2014)
    • A novel music constraint programming system: the PWGL libraries cluster engine and cluster rules

      Anders, Torsten; Sandred, Örjan (2015-06)
      This workshop demonstrates a music constraint system that offers a user-friendly visual programming interface suitable for rapid development, and at the same time allows for a large range of constraint problems, including complex polyphonic problems. This system consists of the two PWGL libraries Cluster Engine and Cluster Rules.
    • Teaching rule-based algorithmic composition: the PWGL library cluster rules

      Anders, Torsten; University of Bedfordshire (2015-07)
      This session reports on an approach to research - informed learning (research - based learning, according to Jenkins et al. (2007)) in the field of Music Technology. In the unit Algorithmic Composition, students learn how to create computer programs that assist the music composition process (using an easy to learn visual programming system). They then use their programs to compose music with them. Our students typically want to compose in a mainstream musical idiom, e.g., virtually all students aim for tonal music, and most often they want a clear rhythmic structure. Constraint programming is a proven approach to successfully model complex mus ic theories like harmony. I recently developed a software library that greatly simplifies the constraint - based modelling of tonal and metric music. More specifically, this new library (called Cluster Rules) provides a collection of predefined musical rules (constraints) for the new music constraint system Cluster Engine by Örjan Sandred (University of Manitoba, Canada). The collection includes various rules on rhythm, melody, harmony and counterpoint. These predefined rules offer a low floor (students easil y get started), but also allow for a high ceiling (highly complex music theories can be modelled freely, by defining further rules for Cluster Engine from scratch). This session will demonstrate the new software, motivate its design, discuss how students u sed this software to generate musical material for their compositions, and it will report on challenges met in that process.
    • Teaching rule‐based algorithmic composition: the PWGL library cluster rules

      Anders, Torsten; University of Bedfordshire (University of Bedfordshire, 2016)
      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.