糖心视频

This website stores cookies on your computer. These cookies are used to collect information about how you interact with our website and allow us to remember your browser. We use this information to improve and customize your browsing experience, for analytics and metrics about our visitors both on this website and other media, and for marketing purposes. By using this website, you accept and agree to be bound by 糖心视频鈥檚 Terms of Use 蹿辞谤听web and social media privacy.聽聽If you do not agree to the above, you can configure your browser鈥檚 setting to 鈥渄o not track.鈥�

Skip to main content
Portrait of George Tzanetakis

Professor; music/CSC advisor

Computer Science

Contact:
Office: ECS 606 250-472-5711
Area(s) of expertise:
Computer audition, audio signal processing, machine learning, music information retrieval, human computer interaction, computer assisted music education, music robotics and sensors

About George

George Tzanetakis is a professor in the Department of Computer Science with cross-listed appointments in ECE and Music at the 糖心视频, Canada. He is the Canada Research Chair (Tier II) in the computer analysis of audio and music and received the Craigdarroch research award in artistic expression at the 糖心视频 in 2012.

George received his PhD 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 George 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.