A compositional methodology based on data extracted from natural phenomena

This paper illustrates, through practical examples from the composer’s own work, methods to base compositional structure upon natural phenomena by extracting data from real-world processes, or from the pattern a process leads to. Obtaining control data from observational techniques has been chosen in contrast to the more popular use of mathematical models, or aural analysis of acoustic sound recordings. Examples show how data have been extracted from two contrasting sources: a rain-forest acoustic environment, and a system displaying self-organised criticality, and how these data have been used to control the spatial, temporal and spectral organisation of musical structure. INTRODUCTION As for a great number of composers, aspects of the natural world inspire my composition. I apply a number of compositional techniques with the ideal goal of musically evoking my perceptual and emotional reaction to these aspects. In earlier works, these techniques consisted of the aural compositional methods used traditionally in acousmatic composition, including recording environmental and other acoustic sound sources, transforming these materials with various degrees of surrogacy, and mixing them in a manner to evoke a musical or extra-musical concept based on aural judgement. I continue to use such methods, to various degrees. The search for effective methods to express concepts in sound, such as ideas ranging from simple kinetic activity to complex landscape description, in a way that is balanced on both musical and extra-musical levels, seems infinite. Through searching for a method to more clearly and accurately articulate natural phenomena, it was clear that the use of numerical data would prove useful. Nature’s formations are created by, or are part of, complex processes about which the sciences are only approaching an understanding. In many cases, understanding is aided through numerical modelling techniques, or by accurate observational methods. Numerical models create data in an attempt to simulate some aspects of a process or pattern. This is in contrast to observation, where data are obtained directly from the subject. Historically, common methods of numerical control over musical structure have involved data borrowed from scientific models. Cellular automata have been used to model growth processes (Brown & Rothery 1993), Markov chains have been used to describe geological strata (Davis 1986), stochastic processes used to model genetics (Brown & Rothery 1993) and the behaviour of gases, and all have been appropriated for musical use in some shape or form. It is not within the scope of this paper to investigate why some composers have chosen to use such control methods, nor to analyse or judge their degree of success. Nevertheless, rarely have data been applied with the aim of evoking specific physical phenomena, and often a lack of appropriate musical mapping has been clear in the result. If numerical data from either modelling or observational methods are to be used in a music composition, there are several questions that need asking: Which aspects of the subject do I want to capture in the music? Which aspects of macro and micro musical structure should be controlled, and what aspects of the data are musically most appropriate? Do I need to re-scale data sets such that the listener can perceive a musical `mapping’? Such questions are difficult to answer, and are different in every context. To investigate these simple questions it is necessary to have at hand reasonably complete sets of data describing the pattern or process. Often, data that describes a strong visual trend is not the most appropriate for musical mapping. For example, a numerical description of the shape of a landscape (commonly achieved by manipulating a fractal algorithm) may be less musically appropriate than a description of the process of erosion. In this instance, the description of the pattern has less importance than the description of the process. When one begins to look closer at how a natural phenomenon occurs, often the complete picture is much more involved than one expects, and an algorithm will rarely provide sufficient numerical data to begin answering such questions. For example, although Xenakis used algorithmic descriptions of gas particles to define some aspects of the musical material, significant editing was necessary to approach a coherence in musical structure (Xenakis 1971), and one could argue that the source implication weakened in the musical result. There are, however, advantages to using algorithmic descriptions, in that one can `tweak’ parameters such that the most contextually pleasing musical control data are obtained. Through wanting to avoid appropriating an algorithmic process to create musical materials and structures, I decided to obtain control data from real-world observation. In the following I give examples from two different projects: one where I collected and analysed my own material (through lack of sufficient existing data), and one where data and a subsequent numerical model were supplied by a third party. REAL-WORLD OBSERVATION: THE SPATIOTEMPORAL DISTRIBUTION OF ANIMAL VOCALISATIONS IN A TROPICAL WET FOREST. Though there have been many studies of the acoustical behaviour of individual animal species in tropical forests, the complete, complex acoustical ecology of this environment is a little researched area. By sampling the whole community, the distribution of animal signalling in time, space and frequency could be assessed. The project was carried out with the help of Oyvind Hammer at the University of Oslo. Two locations were tested, only one of which was used for musical application. This location was the biological field station of La Suerte, located in the lowlands of northeastern Costa Rica. The forest of La Suerte has a very high density of vocalising frogs, mantled howler monkeys, oropendolas, white-collared manakins, tinamous, and insects.