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"Found in Translation": An Evolutionary Framework for Auditory-Visual Relationships.

Ana Rodrigues1,2, Bruna Sousa1, Amílcar Cardoso1,2

  • 1Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, 3004-531 Coimbra, Portugal.

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Summary
This summary is machine-generated.

This study introduces a computational method to link music and images using evolutionary programming, grounded in cognitive science. User tests confirm the system

Keywords:
abstract artauditory–visual associationscomputational creativityemotionsgenetic programmingimage generationmusic generationperception

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Area of Science:

  • Computational creativity
  • Cognitive science
  • Human-computer interaction

Background:

  • Computational artifacts are increasingly used to study cross-modal associations.
  • Understanding the interplay between music, abstract imagery, and human emotion is a complex challenge.
  • Existing methods may not fully capture the nuanced relationships between auditory and visual stimuli.

Purpose of the Study:

  • To propose a novel computational approach for exploring music-image relationships.
  • To integrate cognitive science principles of emotion and perception into an audio-visual system.
  • To utilize evolutionary programming for generating and evaluating cross-modal associations.

Main Methods:

  • Developed a four-module framework: vocabulary set, music generator, image generator, and evolutionary engine.
  • Employed evolutionary programming techniques, specifically genetic programming, to evolve music-image pairs.
  • Tested the system by mapping music to images based on four expressed emotions (angry, calm, happy, sad).

Main Results:

  • Preliminary user tests indicated agreement between human emotional perception and the system's generated music-image associations.
  • The integration of cognitive science findings positively influenced the system's outcomes.
  • Evolutionary strategies proved beneficial for this multi-modal creative problem.

Conclusions:

  • The proposed computational framework effectively models auditory-visual associations informed by emotion and perception.
  • Findings support the value of incorporating cognitive science knowledge into AI-driven creative systems.
  • Evolutionary programming offers a powerful approach for tackling complex, multi-modal creative tasks.