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Genetic Music System with Synthetic Biology.

Eduardo Reck Miranda1

  • 1University of Plymouth, Interdisciplinary Centre for Computer Music Research (ICCMR). eduardo.miranda@plymouth.ac.uk.

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|August 11, 2020
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Summary
This summary is machine-generated.

This study presents GeMS, a novel system merging synthetic biology and music composition. GeMS uses genetic process simulations to generate unique rhythmic and pitch sequences for musical pieces.

Keywords:
Artificial life musicDNA musicMiranda machinegenerative musicgenetic music

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Algorithmic Music Composition

Background:

  • Synthetic biology principles offer novel frameworks for computational systems.
  • Simulating biological processes can inspire new methods for creative content generation.
  • Existing music composition systems lack biologically-inspired generative mechanisms.

Purpose of the Study:

  • Introduce GeMS, a system for music composition utilizing synthetic biology concepts.
  • Demonstrate the generation of musical elements (rhythm and pitch) from simulated genetic processes.
  • Showcase a novel approach to algorithmic music creation through biological system analogies.

Main Methods:

  • The Miranda machine simulates DNA manipulation and generates DNA strands.
  • The rhythmator translates DNA sequences into rhythmic patterns using 'rhythmic codons'.
  • The pitch processor generates musical pitches inspired by protein folding, using DNA instruction orientation.

Main Results:

  • GeMS successfully generates rhythmic and pitch sequences from simulated genetic processes.
  • The system components (Miranda machine, rhythmator, pitch processor) function as designed.
  • A musical composition, "Artibiotics," was created using GeMS to demonstrate its capabilities.

Conclusions:

  • GeMS provides a viable framework for music composition informed by synthetic biology.
  • The system demonstrates the potential of applying biological simulation to creative domains.
  • This interdisciplinary approach opens new avenues for algorithmic art and music generation.