An abstract approach to music.
In this article we have outlined a formal framework for an abstract approach to music and music composition. The model is formulated in terms of objects that have attributes, obey relationships, and are subject to certain well-defined operations. The motivation for this approach uses traditional terms and concepts of music theory, but the approach itself is formal and uses the language of mathematics. The universal object is an audio wave; partials, sounds, and compositions are special objects, which are placed in a hierarchical order based on time scales. The objects have both static and dynamic attributes. When we realize a composition, we assign values to each of its attributes: a (scalar) value to a static attribute, an envelope and a size to a dynamic attribute. A composition is then a trajectory in the space of aural events, and the complex audio wave is its formal representation. Sounds are fibers in the space of aural events, from which the composer weaves the trajectory of a composition. Each sound object in turn is made up of partials, which are the elementary building blocks of any music composition. The partials evolve on the fastest time scale in the hierarchy of partials, sounds, and compositions. The ideas outlined in this article are being implemented in a digital instrument for additive sound synthesis and in software for music composition. A demonstration of some preliminary results has been submitted by the authors for presentation at the conference.
- Research Organization:
- Argonne National Lab., IL (US)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- W-31109-ENG-38
- OSTI ID:
- 11763
- Report Number(s):
- ANL/MCS/CP-98805; TRN: AH200118%%179
- Resource Relation:
- Conference: JIM '99 (Journees d'Informatique Musicale), Paris (FR), 05/17/1999--05/19/1999; Other Information: PBD: 19 Apr 1999
- Country of Publication:
- United States
- Language:
- English
Similar Records
DISCO: An object-oriented system for music composition and sound design
Monaural Music Source Separation using Convolutional Sparse Coding