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Photograph of William Brent

William Brent Associate Professor Performing Arts

University of California, San Diego, Department of Music. Ph.D. in Music with a concentration in Computer Music, Spring 2010. Dissertation title: Physical and Perceptual Aspects of Percussive Timbre.

Mills College; Oakland, California, Department of Music. Master of Arts in Composition, Spring 2003. Master’s thesis title: Technology and the Art of Transformation.

Wilfrid Laurier University; Waterloo, Ontario, Canada, Department of Music. Bachelor of Music in Composition, Spring 2001.

William Brent is a computer musician and Associate Professor of Audio Technology at American University in Washington DC. In collaboration with internationally recognized composers and performers, he develops original software for intermedia performance works, such as James Dillon’s Nine Rivers, Steve Antosca's HABITAT, and OFFSHORE, by Laure Hiendl and Monica Duncan. In this capacity, he has presented work at venues such as SESC (São Paulo), Glasgow Concert Halls (Scotland), Miller Theatre (New York City), The Kitchen (New York City), and the National Gallery of Art (Washington, DC). He is a founding member of the INTERFERENCE New Music Collective, which performs in-residence at the American University Museum at the Katzen Arts Center.

His open source software libraries for the Pure Data (Pd) programming environment are used by an international community of artists and researchers. These include timbreID (a suite of tools for audio analysis and identification), DILib (which is geared toward facilitating the creation of novel digital musical instruments), and DRFX (a library of original signal processing effects and utility classes). This work has been presented at major venues in the field of computer music, including the International Computer Music Conference, the International Conference on New Interfaces for Musical Expression, and the International Conference on Auditory Display.
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Spring 2024

  • ATEC-407 Sound Synthesis

Fall 2024

  • ATEC-482 Adv Stds in Audio Technology: Machine Learning for Audio