We provide a software framework that lets deep learning practitioners easily integrate their own PyTorch models into the open-source Audacity DAW. This lets ML audio researchers put tools in the hands of sound artists without doing DAW-specific development work.
Invited talk at Bay Innovative Signal Hackers Meetup (2021)
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! :)
ISMIR 2021 Poster Video
PyTorch wrappers for using your deep model in Audacity, and sharing it with the community!
A PyTorch port of the openl3 audio embedding (ported from the marl implementation).
PyTorch dataset bindings for 14,000 sound samples of the Philharmonia Orchestra, retrieved from their website. [github]