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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.

  • Speech conversation icon

    Controllable Speech Generation

    Max Morrison and Bryan Pardo

    Nuances in speech prosody (i.e., the pitch, timing, and loudness of speech) are a vital part of how we communicate. We utilize generative machine learning models to generate prosody with user control over these nuances and generate speech reflecting user-specified prosody.

  • System description

    Music Audio Generation

    Hugo Flores Garcia, Prem Seetharaman, Rithesh Kumar, Bryan Pardo

    We introduce VampNet, a masked acoustic token modeling approach to music audio generation. VampNet, made in collaboration with Descript, lets us sample coherent music from the model by applying a variety of masking approaches (called prompts) during inference. Prompting VampNet appropriately, enables music compression, inpainting, outpainting, continuation, and looping with variation (vamping). This makes VampNet a powerful music co-creation tool.

Full List of Projects