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.
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Acknowledgments
This research includes data from the STELIB and MILES library service developed by the Spanish Virtual Observatory in the framework of the IAU Commission G5 Working Group : Spectral Stellar Libraries.
All MIDI files used in the experiments have been downloaded from music21 and Musescore open libraries.
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Riber, A.G., Serradilla, F. (2023). AI-rmonies of the Spheres. In: Johnson, C., Rodríguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2023. Lecture Notes in Computer Science, vol 13988. Springer, Cham. https://doi.org/10.1007/978-3-031-29956-8_9
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