StemGen: A music generation model that listens
Authors: name: Julian D. Parker, name: Janne Spijkervet, name: Katerina Kosta, name: Furkan Yesiler, name: Boris Kuznetsov, name: Ju-Chiang Wang, name: Matt Avent, name: Jitong Chen, name: Duc Le

End-to-end generation of musical audio using deep learning techniques has
seen an explosion of activity recently. However, most models concentrate on
generating fully mixed music in response to abstract conditioning information.
In this work, we present an alternative paradigm for producing music generation
models that can listen and respond to musical context. We describe how such a
model can be constructed using a non-autoregressive, transformer-based model
architecture and present a number of novel architectural and sampling
improvements. We train the described architecture on both an open-source and a
proprietary dataset. We evaluate the produced models using standard quality
metrics and a new approach based on music information retrieval descriptors.
The resulting model reaches the audio quality of state-of-the-art
text-conditioned models, as well as exhibiting strong musical coherence with
its context.