AI

Learning Latent Representations of Music to Generate Interactive Musical Palettes

Abstract

Advances in machine learning have the potential to radically reshape interactions between humans and computers. Deep learning makes it possible to discover powerful representations that are capable of capturing the latent structure of highdimensional data such as music. By creating interactive latent space “palettes” of musical sequences and timbres, we demonstrate interfaces for musical creation made possible by machine learning. We introduce an interface to the intuitive, low-dimensional control spaces for high-dimensional note sequences, allowing users to explore a compositional space of melodies or drum beats in a simple 2-D grid. Furthermore, users can define 1-D trajectories in the 2-D space for autonomous, continuous morphing during improvisation. Similarly for timbre, our interface to a learned latent space of audio provides an intuitive and smooth search space for morphing between the timbres of different instruments. We remove technical and computational barriers by embedding pre-trained networks into a browser-based GPU-accelerated framework, making the systems accessible to a wide range of users while maintaining potential for creative flexibility and personalization.