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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.


  • Audacity logo

    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.

  • Hierarchical Prototypical Networks

    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.

  • System diagram of 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.

Full List of Projects