149 citations · 378 across the 9 of their papers we have counts for
9 papers
Redefining Relationships in Music
Christian Detweiler, Beth Coleman, Fernando Diaz +13
AI tools increasingly shape how we discover, make and experience music. While these tools can have the potential to empower creativity, they may fundamentally redefine relationship…
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…
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Yusong Wu, Ethan Manilow, Yi Deng +6
Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost…
Expressive Communication: A Common Framework for Evaluating Developments in Generative Models and Steering Interfaces
Ryan Louie, Jesse Engel, Anna Huang
There is an increasing interest from ML and HCI communities in empowering creators with better generative models and more intuitive interfaces with which to control them. In music,…
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…