1 citations · 1 across the 5 of their papers we have counts for
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Ethics Statements in AI Music Papers: The Effective and the Ineffective
Julia Barnett, Patrick O'Reilly, Jason Brent Smith +2
While research in AI methods for music generation and analysis has grown in scope and impact, AI researchers' engagement with the ethical consequences of this work has not kept pac…
The Rhythm In Anything: Audio-Prompted Drums Generation with Masked Language Modeling
Patrick O'Reilly, Julia Barnett, Hugo Flores García +4
Musicians and nonmusicians alike use rhythmic sound gestures, such as tapping and beatboxing, to express drum patterns. While these gestures effectively communicate musical ideas,…
Deep Audio Watermarks are Shallow: Limitations of Post-Hoc Watermarking Techniques for Speech
Patrick O'Reilly, Zeyu Jin, Jiaqi Su +1
In the audio modality, state-of-the-art watermarking methods leverage deep neural networks to allow the embedding of human-imperceptible signatures in generated audio. The ideal is…
HARP 2.0: Expanding Hosted, Asynchronous, Remote Processing for Deep Learning in the DAW
Christodoulos Benetatos, Frank Cwitkowitz, Nathan Pruyne +4
HARP 2.0 brings deep learning models to digital audio workstation (DAW) software through hosted, asynchronous, remote processing, allowing users to route audio from a plug-in inter…