2.1k citations · 4.1k across the 19 of their papers we have counts for
5 papers · 1 filter
Learning to Groove with Inverse Sequence Transformations
Jon Gillick, Adam Roberts, Jesse Engel +2
We explore models for translating abstract musical ideas (scores, rhythms) into expressive performances using Seq2Seq and recurrent Variational Information Bottleneck (VIB) models.…
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…
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…
Onsets and Frames: Dual-Objective Piano Transcription
Curtis Hawthorne, Erich Elsen, Jialin Song +6
We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and fr…
Learning and Evaluating Musical Features with Deep Autoencoders
Mason Bretan, Sageev Oore, Doug Eck +1
In this work we describe and evaluate methods to learn musical embeddings. Each embedding is a vector that represents four contiguous beats of music and is derived from a symbolic…