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I am an Assistant Professor in the Computer Science Department at Carnegie Mellon University, and also a Research Scientist at Google DeepMind on the Magenta team (part-time).
Publications. Shih-Lun Wu, Chris Donahue, Shinji Watanabe, Nicholas J. Bryan (2023). Music ControlNet: Multiple Time-varying Controls for Music Generation. arXiv PDF BibTeX 🔊 Examples Video. Michael Feffer, Zachary C. Lipton, Chris Donahue (2023).
This page contains info and FAQs for prospective PhD students of Chris Donahue ’s yet-to-be-named lab in the Computer Science Department at CMU.
Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation.
2022 Chris Donahue, John Thickstun, and Percy Liang. “Melody transcription via generative pre-training”, inISMIR 2022. 2022 Karan Goel, Albert Gu, Chris Donahue, and Christopher R´e. “It’s raw! Audio generation with state-space models”, inICML 2022(Oral presentation). 2021 Rodrigo Castellon
John Thickstun, David Hall, Chris Donahue, Percy Liang. June, 2023. arXiv PDF BibTeX 🔊 Examples Code. Abstract. We introduce anticipation: a method for constructing a controllable generative model of a temporal point process (the event process) conditioned asynchronously on realizations of a second, correlated process (the control process).
Text-to-music generation models are now capable of generating high-quality music audio in broad styles. However, text control is primarily suitable for the manipulation of global musical attributes like genre, mood, and tempo, and is less suitable for precise control over time-varying attributes such as the positions of beats in time or the changing dynamics of the music. We propose Music ...
Chris Donahue, Mina Lee, Percy Liang. July, 2020. arXiv PDF BibTeX Code Demo. Abstract. We present a simple approach for text infilling, the task of predicting missing spans of text at any position in a document.
Chris Donahue, Julian McAuley, Miller Puckette. We present sound examples from our WaveGAN and SpecGAN models (paper, code). Each sound file represents fifty examples of one second in length concatenated together, with a half second of silence after each example.
Chris Donahue, John Thickstun, Percy Liang. Note, this page is not optimized for viewing on mobile displays. Section I. Performance of our melody transcription methods and baselines on RWC-MDB. This section serves as the primary holistic comparison of our approach to melody transcription against existing baselines.
