Professor: Marianopolis College Regular member: CIRMMT Principle designer: jMIR E-mail: cory.mckay@mail.mcgill.ca

Cory McKay is a professor of music and humanities at Marianopolis College, a regular member of the Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT) and a private research consultant specializing in machine learning and music. His multidisciplinary background in computer science, physics, jazz and sound recording has helped him produce many academic publications in a diverse range of fields linked to music. He received his Ph.D., M.A. and B.Sc. from McGill University, completed his B.A. at the University of Guelph and did a postdoc at the University of Waikato.

Cory’s current research as a co-investigator in the LinkedMusic, SIMSSA and MIRAI projects focuses on using machine learning and statistical analysis to find and understand patterns in early music, particularly with respect to composer attribution, genre, regional traits and the general delineation of musical style. This work also involves developing and integrating repositories and frameworks for sharing music research corpora in digital forms. His industry work, in contrast, focuses on researching and refining automatic music production algorithms. He is also the primary designer of the jMIR software framework for performing multimodal music information retrieval (MIR) research, which includes the jSymbolic tool for extracting musical features from digital scores.

Cory is also deeply involved in student life at Marianopolis, where he is director of the college’s laptop computer orchestra (MLOrk) and coaches the college’s R4TT trivia team, which has won multiple Québec championships. He has also been active at the administrative and committee levels, where he has held a wide variety of both elected and appointed positions over the years, both at Marianopolis and CIRMMT. He is currently on the CIRMMT executive committee and is co-leader of Research Axis 2 (music information research). His teaching focuses on sound recording, audio production, performing live music with computers, psychoacoustics and video games. He has also supervised many independent student course projects and recitals.

He also apparently has a penchant for anachronistic personal web sites authored in old-school HTML.

• Computational Musicology: applying machine learning and statistical analysis to Renaissance (and other) music; composer attribution; empirical delineation of musical style by genre, region, time period, etc.
• Music Information Retrieval (MIR): automatic music production; multimodal feature extraction (from audio, symbolic music, lyrics, metadata, images, video, etc.); automatic chord labeling; responsibly addressing intrinsic ambiguity in musical machine learning; general automatic music classification.
• Digital Music Libraries: open music research databases; music-related data models; automatic musical metadata annotation; standardized open music-related data formats.
• Music Performance: live performance with computers and electronics; automatic accompaniment systems; analysis of performers’ expressive gestures.
• Education: AI-based music tutors; automatic error detection systems; music visualization.