(Apologies for cross-posting)
Title: A hierarchical representation learning approach for source separation, transcription, and music generation
Date/Time: July 14, Thursday, 11:00 AM – 12:00 PM EST.
Abstract: With interpretable music representation learning, music source separation problems are well connected with transcription problems, and transcription problems can be transformed into music arrangement problems. In particular, Gus will discuss two recently developed models. The first one used a pitch-timbre disentanglement to achieve source separation, transcription, and synthesis. The second one used cross-modal chord-texture disentanglement to solve audio-to-symbolic piano arrangement. In the end, Gus will show his vision of a unified hierarchical representation-learning framework that bridges music understanding and generation.
Bio of the speaker:
Gus is an Assistant Professor in Computer Science at NYU Shanghai and Global Network Assistant Professor at NYU. He also holds affiliations at Tandon, CILVR at the Center for Data Science, and MARL. He received his Ph.D. in the Machine Learning Department at Carnegie Mellon University in 2016, and he was a Neukom Fellow at Dartmouth from 2016 to 2017. Gus is also a professional Di and Xiao (Chinese flute and vertical flute) player. He plays as a soloist in the NYU Shanghai Jazz ensemble, Pitt Carpathian Ensemble, and Chinese Music Institute of Peking University.
Gus's research is very interdisciplinary. He is broadly interested in the design of interactive intelligent systems to extend human musical creation and expression. This research lies in the intersection of Machine Learning, HCI, Robotics, and Computer Music. Some representative works include interactive composition via style transfer, human-computer interactive performances, autonomous dancing robots, large-scale content-based music retrieval, haptic guidance for flute tutoring, and bio-music computing using slime mold.
This webinar will be offered online via Zoom. Please register to receive the Zoom link prior to the webinar.
Registration link: https://libcal.dartmouth.edu/calendar/itc/2022DSAIW3. Click or tap if you trust this link." data-linkindex="7">libcal.dartmouth.edu/calendar/itc/2022DSAIW3
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Jianjun Hua
Statistical Consultant
Dartmouth College
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