Headed by Prof. Bryan Pardo, the Interactive Audio Lab is in the Computer Science Department of Northwestern University. We develop new methods in Machine Learning, Signal Processing and Human Computer Interaction to make new tools for understanding and manipulating sound.
Ongoing research in the lab is applied to audio scene labeling, audio source separation, inclusive interfaces, new audio production tools and machine audition models that learn without supervision. For more see our projects page.
Latest News
Best Paper Nomination at ISMIR 2021
Oct 17, 2021
We host the Oct 27 Bay Innovative Signal Hackers Bash
Oct 16, 2021
New Audio Source Separation Tutorial
Nov 20, 2020
Alisa Liu wins student paper award in DCASE 2020
Nov 1, 2020
Fatemeh Pishdadian defends dissertation
Oct 27, 2020
Invited Talk at AES 2020
Sep 16, 2020
Pardo on Headroom podcast
Sep 16, 2020
Lab welcomes 2 new members
Aug 30, 2020
Projects
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Deep Learning Tools for Audacity
Hugo Flores Garcia, Aldo Aguilar, Ethan Manilow, Dmitry Vedenko and Bryan Pardo
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.
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Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition
Hugo Flores Garcia, Aldo Aguilar, Ethan Manilow, Bryan Pardo
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.
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Cerberus
Ethan Manilow, Prem Seetharaman, Bryan Pardo
Cerberus is a single deep learning architecture that can simultaneously separate sources in a musical mixture and transcribe those sources.