DAY 2Track 3

How AI Meets Art (Music) to Create Value: An Example of AI Applied to Machine Musicianship

With the recent advances in deep learning and machine learning in general, many AI-based algorithms and systems are delivering performances that are comparable to or better than those of human in applications such as object recognition, question answering, or speech recognition, to name a few. The domain of creative arts (writing, painting, music, etc.) is not an exception. This presentation will introduce you to some seminal examples of AI applied to creative arts, and then focus on what and how AI can be applied to music in particular, which we call “Machine Musicianship”.



Lee Kyo Gu Graduate School of Convergence Science and Technology Seoul National University, Professor

Kyogu Lee received the B.S. degree in Electrical Engineering from Seoul National University, Seoul, Korea, in 1996, the M.M. degree in Music Technology from New York University, New York, in 2002, and the M.S. degree in Electrical Engineering and the Ph.D. degree in Computer-based Music Theory and Acoustics from Stanford University, Stanford, CA, in 2007 and 2008, respectively.

He worked as a Senior Researcher in the Media Technology Lab at Gracenote from 2007 to 2009. He is now a professor at the Graduate School of Convergence Science and Technology at Seoul National University, Seoul, Korea and is leading the Music and Audio Research Group (MARG). His research interests can be summarized as Machine Listening (a.k.a. Computer Audition), where signal processing and machine learning are utilized as two main instruments to better understand human auditory perception and cognition.