Torsten Anders

Constraint programming systems for modeling music theories and composition

Constraint programming is well suited for the computational modeling of music theories and composition: its declarative and modular approach shares similarities with the way music theory is traditionally expressed, namely by a set of rules which describe the intended result. Various music theory disciplines have been modeled, including counterpoint, harmony, rhythm, form, and instrumentation. Because modeling music theories “from scratch” is a complex task, generic music constraint programming systems have been proposed that predefine the required building blocks for modeling a range of music theories. After introducing the field and its problems in general, this survey compares these generic systems according to a number of criteria such as the range of music theories these systems support.

Constraint application with higher-order programming for modeling music theories

Modeling music theories with computer programs has attracted composers and scholars for a long time. On the one hand, the resulting programs can serve as algorithmic composition tools. On the other hand, such an approach leads to a better understanding of existing as well as newly developed theories, which in turn can lead to a better understanding of music, as well as to better ways to retrieve music from databases. Constraint programming (Apt 2003) has often been used to create computational models of music theories and composition. Constraint-based harmonization systems were surveyed by Pachet and Roy (2001); other examples include purely rhythmic tasks (Sandred 2003), Fuxian counterpoint (Schottstaedt 1989), Ligeti-like textures (Chemillier and Truchet 2001; Laurson and Kuuskankare 2001), and instrument-specific writing (Laurson and Kuuskankare 2001). Many music constraint systems have been proposed in which users implement their own music theory models. Two seminal systems are PWConstraints (Laurson 1996) and Situation (Rueda et al. 1998). Carla (Courtot 1990) is one of the earliest systems. Other examples include the aggregation of the music representation MusES with the constraint system BackTalk (Pachet and Roy 1995), Arno (Anders 2000), OMClouds (Truchet and Codognet 2004), and Örjan Sandred’s PWMC defined on top of PWConstraints (Sandred 2010, in this issue). A survey of music-constraint programming in general and a detailed comparison of existing systems is provided by Anders and Miranda (in press). Each system provides the following components, which are essential for solving musical constraint satisfaction problems (CSP). It defines a music representation (score) where some aspects are

Towards Brain-Computer Music Interfaces: Progress and challenges

Brain-Computer Music Interface (BCMI) is a new research area that is emerging at the cross roads of neurobiology, engineering sciences and music. This research involves three major challenging problems: the extraction of meaningful control information from signals emanating directly from the brain, the design of generative music techniques that respond to such information, and the training of subjects to use the system. We have implemented a proof-of-concept BCMI system that is able to use electroencephalogram information to generate music online. Ongoing research informed by a better understanding of brain activity associated with music cognition, and the development of new tools and techniques for implementing brain-controlled generative music systems offer a bright future for the development of BCMI.

Interfacing manual and machine composition

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.

Strasheela: design and usage of a music composition environment based on the oz programming model

Strasheela provides a means for the composer to create a symbolic score by formally describing it in a rule-based way. The environment defines a rich music representation for complex polyphonic scores. Strasheela enables the user to define expressive compositional rules and then to apply them to the score. Compositional rules can restrict many aspects of the music – including the rhythmic structure, the melodic structure and the harmonic structure – by constraining the parameters (e.g. duration or pitch) of musical events according to some numerical or logical relation. Strasheela combines this expressivity with efficient search strategies. Strasheela is implemented in the Oz programming language. The Strasheela user writes an Oz program which applies the Strasheela music representation. The program searches for one or more solution scores which fulfil all compositional rules applied to the score.

A Model of Musical Motifs

This paper presents a model of musical motifs for composition. It defines the relation between a motif’s music representation, its distinctive features, and how these features may be varied. Motifs can also depend on non-motivic musical conditions (e.g., harmonic, melodic, or rhythmic rules). The model was implemented as a constraint satisfaction problem.