79 citations · 121 across the 3 of their papers we have counts for
4 papers · 1 filter
Improving Source Separation by Explicitly Modeling Dependencies Between Sources
Ethan Manilow, Curtis Hawthorne, Cheng-Zhi Anna Huang +2
We propose a new method for training a supervised source separation system that aims to learn the interdependent relationships between all combinations of sources in a mixture. Rat…
AI Song Contest: Human-AI Co-Creation in Songwriting
Cheng-Zhi Anna Huang, Hendrik Vincent Koops, Ed Newton-Rex +2
Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work…
The Bach Doodle: Approachable music composition with machine learning at scale
Cheng-Zhi Anna Huang, Curtis Hawthorne, Adam Roberts +4
To make music composition more approachable, we designed the first AI-powered Google Doodle, the Bach Doodle, where users can create their own melody and have it harmonized by a ma…
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…