Vol. 6 No. 3 (2013): Vol 6 (2013) Special Issue – Ontologie musicali
Articles

Sistemi e autopoiesi nella musica elettronica su nastro magnetico

Published 2014-02-26

Keywords

  • electronic music,
  • self-organizing systems,
  • autopoiesis

How to Cite

Cossettini, L. (2014). Sistemi e autopoiesi nella musica elettronica su nastro magnetico. Aisthesis. Pratiche, Linguaggi E Saperi dell’estetico, 6(3), 192–213. https://doi.org/10.13128/Aisthesis-14102

Abstract

With the development of sound production and processing technologies, composers' private rooms stopped delimiting the boundaries of musical creativity, which started inhabiting recording studios instead. Here memory, traditionally entrusted to paper and to the silence of the musical text, met a technology that enabled to record sounds directly, thus achieving the acoustic fulfilment of the composer's thought. The crystalline abstraction of musical notation gave way to a world of sounds submerged in noise as well as in human and technological indeterminacy: it is a world that requires specific control strategies and direct confrontation with performance practices and listening within a complex system. Composers were tossed into this system, becoming performers and active observers of their own work. Thus, the linearity of Shannon’s model of communication was altered. In his works on musical semiology, Jean-Jacques Nattiez had already modified the model from Sender -> Message -> Receiver to Poietic dimension -> Trace <- Esthesic dimension. The rise of the composer as an active observer requires a new transformation of the model of communication, creating a continuous feedback between the different elements. This feedback will often involve the listener as well. The electronic music work can therefore be seen as a self-organizing system that includes in itself the electronic devices as well as the human factor that operates them. In this system, compositional models are the invariants that vouch for the identity and recognisability of the work.

Metrics

Metrics Loading ...