Date:
Thu, 05/19/2022 - 5:30pm - 6:30pm
Location:
CCRMA Classroom [Knoll 217]
Abstract: Transformers have touched many fields of research and music/audio is no different. This talk will present 3 of my papers as case studies on how we can leverage the power of Transformers in representation learning, signal processing, and clustering. First, we discuss how we're able to beat the wildly popular WaveNet architecture, proposed by Google-DeepMind for raw audio synthesis. We also show how we overcame the quadratic constraint of the Transformers by conditioning on context. Secondly, a version of Audio Transformers for large-scale audio understanding, inspired by viT, operating on raw waveforms, is presented.