Raw Music from Free Movements: Early Experiments in Using Machine Learning to Create Raw Audio from Dance Movements

Paper i proceeding, 2021

Raw Music from Free Movements is a deep learning architecture that translates pose sequences into audio waveforms. The architecture combines a sequence-to-sequence model generating audio encodings and an adversarial autoencoder that generates raw audio from audio encodings. Experiments have been conducted with two datasets: a dancer improvising freely to a given music, and music created through simple movement sonification. The paper presents preliminary results. These will hopefully lead closer towards a model which can learn from the creative decisions a dancer makes when translating music into movement and then follow these decisions reversely for the purpose of generating
music from movement.