collaborators

6 papers

cs.SD2019

Encoding Musical Style with Transformer Autoencoders

Kristy Choi, Curtis Hawthorne, Ian Simon +2

We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex lang…

cs.SD2018

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset

Curtis Hawthorne, Andriy Stasyuk, Adam Roberts +6

Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most mu…

cs.LG2018

Piano Genie

Chris Donahue, Ian Simon, Sander Dieleman

We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with eight buttons,…

cs.LG2018

Music Transformer

Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit +7

Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such…

cs.SD2018

This Time with Feeling: Learning Expressive Musical Performance

Sageev Oore, Ian Simon, Sander Dieleman +2

Music generation has generally been focused on either creating scores or interpreting them. We discuss differences between these two problems and propose that, in fact, it may be v…

stat.ML2018

Learning a Latent Space of Multitrack Measures

Ian Simon, Adam Roberts, Colin Raffel +3

Discovering and exploring the underlying structure of multi-instrumental music using learning-based approaches remains an open problem. We extend the recent MusicVAE model to repre…