AI-Rmonies of the Spheres

EasyChair Preprint 9890

Abstract

Thanks to the efforts and cooperation of the international community, nowadays it is possible to analyze astronomical data captured by the observatories and telescopes of major space agencies around the world from a personal computer. The development of virtual observatory technology (VO), and the standardization of the formats it uses, allow professional and amateur astronomers to access astronomical data and images through internet with relative ease. Immersed in this environment of global accessibility, this article presents an astronomical data-driven unsupervised music composition system based on Deep Learning, aimed at offering an automatic and objective review on the classical topic of the Harmonies of the Spheres. The system explores the MILES stellar library from the Spanish Virtual Observatory (SVO) using a variational autoencoder architecture to cross-match its stellar spectra via Pitch-Class Set Theory with a music score generated by a LSTM with attention neural network in the style of late-renaissance music.

Keyphrases: Astronomy, LSTM networks, astrophysics, deep learning, music, sonification

@booklet{EasyChair:9890,
  author    = {Adrian Garcia Riber and Francisco Serradilla Garcia},
  title     = {AI-Rmonies of the Spheres},
  howpublished = {EasyChair Preprint 9890},
  year      = {EasyChair, 2023}}