Video 29a: Feature Extraction and Machine Learning (III): Artificial Intelligence
MIT OpenCourseWare: Free Courses from the instituteSummary
Researchers explore the intersection of machine learning and music analysis, focusing on how to convert musical scores into numerical data that can be processed by artificial intelligence. The discussion emphasizes the importance of feature extraction, which transforms musical elements into quantifiable formats, enabling algorithms to analyze and classify music effectively.
The conversation delves into practical applications, such as predicting whether a piece of music is a waltz based on its time signature and other musical features. The hosts demonstrate the extraction of specific features, such as meter and pitch, using the Music21 toolkit. They highlight the challenges of ensuring accurate data input, underscoring the principle of "garbage in, garbage out" in machine learning.
As the discussion progresses, they introduce more advanced feature extraction techniques and the importance of ethical considerations in AI development. The episode concludes with a call to action for listeners to engage with their own datasets and explore the potential of computational musicology, while also hinting at future discussions on the limitations and possibilities of deep learning in music analysis.
Show Notes
MIT 21M.383 Computational Music Theory and Analysis Spring 2023Instructor: Michael Scott Asato CuthbertView the complete course: https://ocw.mit.edu/courses/21m-383-computational-music-theory-and-analysis-spring-2023/YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62vSB2sI0W8lQFKsmS2-A6RUploaded 15-10-25Tutorial on feature extraction and machine learning in general and in music21.License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlWe encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments. Learn more about your ad choices. Visit megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices
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