Adaptive Accompaniment with ReaLchords
Yusong Wu, Tim Cooijmans, Kyle Kastner, Adam Roberts, Ian Simon, Alexander Scarlatos, Chris Donahue, Cassie Tarakajian, Shayegan Omidshafiei, Aaron Courville, Pablo Samuel Castro, Natasha Jaques, Cheng-Zhi Anna Huang

TL;DR
ReaLchords is an online generative model for real-time chord accompaniment that uses reinforcement learning and a novel reward system to adapt to live melodies, enabling collaborative musical improvisation.
Contribution
The paper introduces ReaLchords, a novel online music generation model that combines reinforcement learning and a new distillation method for real-time accompaniment.
Findings
ReaLchords adapts effectively to unfamiliar melodies.
The model produces harmonically and temporally coherent accompaniments.
Listening tests confirm the model's suitability for live jamming.
Abstract
Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expressive output but are not able to generate in an \emph{online} manner, meaning simultaneously with other musicians (human or otherwise). We propose ReaLchords, an online generative model for improvising chord accompaniment to user melody. We start with an online model pretrained by maximum likelihood, and use reinforcement learning to finetune the model for online use. The finetuning objective leverages both a novel reward model that provides feedback on both harmonic and temporal coherency between melody and chord, and a divergence term that implements a novel type of distillation from a teacher model that can see the future melody. Through quantitative experiments and listening tests, we demonstrate that the resulting model adapts well to…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Reinforcement Learning in Robotics
