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Explore the intersection of computer science and musicology through this comprehensive MIT course that teaches computational approaches to music theory and analysis in the symbolic domain. Learn to use algorithms for music theory analysis, encoding systems, corpus studies, musical search and similarity algorithms, feature extraction techniques, and machine learning applications in music. Master the music21 Python toolkit for computational music analysis while studying pitch and duration representation, hierarchical music structures, and MusicXML encoding standards. Investigate corpus-based statistical analysis of musical works, voice leading principles, and music cognition research methodologies. Develop skills in algorithmic composition techniques, feature extraction for machine learning applications, and music visualization methods including optical music recognition. Engage with guest lectures on music cognition, interviews with industry experts like MusicXML creator Michael Good, and practical tutorials covering everything from basic note representation to advanced artificial intelligence applications in music. Work through hands-on problem sets that combine theoretical concepts with practical coding exercises, learning to analyze musical corpora, generate algorithmic compositions, and apply statistical methods to musicological research questions.
