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course information
course information
semester: spring, 2024
department: Department of Applied Music
course name: AI-assisted Music Composition
instructor: Hsin-Ming Lin
e-mail: hmlin (university mailbox)
teaching assistant: Peter
type: elective
level: sophomore
credits: 2
students: 7
time: Thursday 16:10–18:00
short URL: https://bit.ly/tnnua-uam-aiamc
course description
course description
This course is designed to equip students with the knowledge, skills, and creative mindset necessary to navigate the dynamic landscape of AI assisted music composition with confidence and proficiency. It combines the realms of artificial intelligence and musical creativity, providing students with a unique opportunity to harness cutting edge technology in the pursuit of musical expression. Through a blend of theoretical discussions, practical applications, and hands on projects, students will delve into the fascinating intersection of data science, artificial intelligence, and the art of music composition. From exploring generative models to navigating ethical considerations surrounding AI in music, this course aims to empower student s to become adept composers in the era of AI driven musical innovation.
objectives
objectives
to articulate the fundamental concepts and principles of generative music AI
to proficiently operate a variety of generative music AI tools and techniques
to apply generative music AI to compose original musical pieces, including songs, music, and incidental music
to critically asses s the creative possibilities and limitations of generative music AI in music composition
to demonstrate an understanding of the ethical considerations and implications of using AI in music composition
grading criteria
grading criteria
participation = 20%: activities during classes
quiz = 20%: oral and paper examinations
assignment = 20%: preparations and projects
midterm presentation = 20%: oral presentation and file(s) submission
final presentation = 20%: oral presentation and file(s) submission
weekly schedule
weekly schedule
AI Cup info session (Thursday 12:00)
musical creativity (synchronous online)
Tomb Sweeping Day
workshop (Thursday 14:00)
references
references
books:
A. Croll, Music Science: How Data and Digital Content Are Changing Music. Sebastopol, CA: O’Reilly Media, 2015.
G. Mazzola, J. Park, and F. Thalmann, Musical Creativity: Strategies and Tools in Composition and Improvisation. Berlin: Springer-Verlag, 2011.
B. D. Man, R. Stables, and J. D. Reiss, Intelligent Music Production. Abingdon: Routledge, 2019.
J. McCormack and M. d’Inverno, Eds., Computers and Creativity. Berlin: Springer-Verlag, 2012.
G. Nierhaus, Algorithmic Composition: Paradigms of Automated Music Generation. Berlin: Springer-Verlag, 2009.
Robert Rowe, Machine Musicianship. Cambridge, MA: The MIT Press, 2001.
Stephan M. Schwanauer and David A. Levitt, Machine Models Music. Cambridge, MA: The MIT Press, 1993.
A. DuBreuil, Hands-On Music Generation with Magenta: Explore the Role of Deep Learning in Music Generation and Assisted Music Composition. Birmingham: Packt Publishing, 2020.
J.-P. Briot, G. Hadjeres, and F.-D. Pachet, Deep Learning Techniques for Music Generation. Cham: Springer, 2019.
courses:
AI For Everyone, Coursera.
和AI做朋友，教育部AI人才培育計畫：中小學推廣教育計畫。
feedback
feedback
office hour
office hour
one hour every time by appointment
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