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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 Generative Modeling, Signal Processing and Human Computer Interaction to make new tools for understanding, creating, and manipulating sound.

Ongoing research in the lab is applied to generation of music and speech, 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.

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Projects

  • MaskMark

    MaskMark - Robust Neural Watermarking for Real and Synthetic Speech

    Patrick O'Reilly, Zeyu Jin, Jiaqi Su, Bryan Pardo

    High-quality speech synthesis models may be used to spread misinformation or impersonate voices. Audio watermarking can combat misuse by embedding a traceable signature in generated audio. However, existing audio watermarks typically demonstrate robustness to only a small set of transformations of the watermarked audio. To address this, we propose MaskMark, a neural network-based digital audio watermarking technique optimized for speech.

  • System description

    VampNet - Music Generation via Masked Acoustic Token Modeling

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

    We introduce VampNet, a masked acoustic token modeling approach to music audio generation. VampNet 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.

  • VoiceBlock

    Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models

    Patrick O'Reilly, Andreas Bugler, Keshav Bhandari, Max Morrison, Bryan Pardo

    As governments and corporations adopt deep learning systems to apply voice ID at scale, concerns about security and privacy naturally emerge. We propose a neural network model capable of inperceptibly modifying a user’s voice in real-time to prevent speaker recognition from identifying their voce.

Full List of Projects