# Research

## ISMIR 2022 Tutorial: Few-Shot and Zero-Shot Learning for Music Information Retrieval

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

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.

## 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! :)

# Software

## Audacity with Deep Learning

I am contributing a deep learning framework and a deep model manager that connects to HuggingFace to Audacity. This project was funded by a Google Summer of Code grant. Read the Work Product Summary.

## audacitorch

PyTorch wrappers for using your deep model in Audacity, and sharing it with the community!

## torchopenl3

A PyTorch port of the openl3 audio embedding (ported from the marl implementation).

## Philharmonia Dataset

PyTorch dataset bindings for 14,000 sound samples of the Philharmonia Orchestra, retrieved from their website.  [github]