research

VampNet - Music Generation Via Masked Transfomers

VampNet is a generative model for music that uses a masked token modeling technique to perform music generation and compression. We proposed new ways to prompt a generative model with music by masking out parts of some input with a meaningful mask structure, and have VampNet fill in the missing parts with new musical content. Check out the paper and listen to audio samples!



ISMIR 2022 Tutorial on Few-Shot and Zero-Shot Learning for MIR

Yu Wang, Jeong Choi and I gave a tutorial during ISMIR 2022 on few-shot and zero-shot learning centered around music information retrieval tasks. In this tutorial, we cover the foundations of few-shot//zero-shot learning, build standalone coding examples, and discuss the state-of-the-art in the field, as well as future directions.

The tutorial is available as a jupyter book online.



Deep Learning Tools for Audacity

Aldo Aguilar, Ethan Manilow and I made a software framework that lets deep learning practitioners easily integrate their own PyTorch models into Audacity. This lets ML audio researchers put tools in the hands of sound artists without doing DAW-specific development work, which is often a long and arduous process in itself.

Learn more about it in our project page :).



Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition

In this work, we exploit hierarchical relationships between instruments in a few-shot learning setup to enable classification of a wider set of musical instruments, given a few examples at inference. See the supplementary code on github.

update: this work won the Best Paper Award at ISMIR 2021! :)

watch the poster video!