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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Alexander Lerch
alexander.lerch@gatech.edu
Summary Sentence: Guest lecture by University of Victoria CS Associate Professor George Tzanetakis. Topics will focus on using music information retrieval to solve problems such as automatic genre classification, mood prediction and music recommendation.
Full Summary: Guest lecture by University of Victoria CS Associate Professor George Tzanetakis. Topics will focus on using music information retrieval to solve problems such as automatic genre classification, mood prediction and music recommendation.
George Tzanetakis
George Tzanetakis is an associate professor in the Department of Computer Science with cross-listed appointments in ECE and Music at the University of Victoria, Canada. He is Canada Research Chair (Tier II) in the Computer Analysis and Audio and Music and received the Craigdaroch research award in artistic expression at the University of Victoria in 2012. In 2011 he was visiting faculty at Google Research. He received his Ph.D. in computer science at Princeton University in 2002 and was a post-doctoral fellow at Carnegie Mellon University in 2002-2003. His research spans all stages of audio content analysis such as feature extraction, segmentation, classification with specific emphasis on music information retrieval. He is also the primary designer and developer of Marsyas an open source framework for audio processing with specific emphasis on music information retrieval applications. His pioneering work on musical genre classification received a IEEE signal processing society young author award and is frequently cited. More recently he has been exploring new interfaces for musical expression, music robotics, computational ethnomusicology, and computer-assisted music instrument tutoring. These interdisciplinary activities combine ideas from signal processing, perception, machine learning, sensors, actuators and human-computer interaction with the
connecting theme of making computers better understand music to create more effective interactions with musicians and listeners.
