Python Hidden Markov Model - Michael Hamilton, Colorado State University
The McGill Billboard Project - a large data set of harmonic annotations of songs from several decades of the Billboard Top 100 list.
“A Corpus Study of Rock Music” - David Temperley and Trevor de Clercq’s data and programs for analyzing the harmonic progressions in a corpus of 200 songs from Rolling Stone magazine’s “Greatest [Rock] Songs of All Time.”
The Million Song Dataset - “a freely-available collection of audio features and metadata for a million contemporary popular music tracks” (Columbia Univ.).
Open Music Theory – an online “textbook” for music theory and aural skills.
Spotipy – a lightweight Python library for the Spotify Web API.
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Burgoyne, John Ashley. 2011. “Stochastic Processes & Database-Driven Musicology.” Ph.D. diss., McGill University.
De Clercq, Trevor and David Temperley. 2011. “A corpus analysis of rock harmony.” In Popular Music 30/1, pp. 47–70.
Everett, Walter. 2004. “Making Sense of Rock’s Tonal Systems.” In Music Theory Online 10/4.
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Gjerdingen, Robert O., and David Perrott. “Scanning the dial: The rapid recognition of music genres.” Journal of New Music Research 37, no. 2 (2008): 93-100.
Huron, David. 2006. Sweet Anticipation: Music and the Psychology of Expectation. MIT Press.
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Shaffer, Kris. 2012. “Averages, clusters, and hidden patterns.” July 27, 2012.
Shaffer, Kris. 2013. “Harmonic syntax in corpus studies.” January 12, 2013.
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Summach, Jason. 2012. “Form in Top-20 Rock Music, 1955–89.” Ph.D. diss., Yale University.
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White, Christopher and Ian Quinn. 2014. Draft of primer on hidden Markov modeling in music.
Yim, Gary. 2012. “Affordant Harmony in Popular Music: Do Physical Attributes of the Guitar Influence Chord Sequences?” In Proceedings of the 12th International Conference on Music Perception and Cognition and the 8th Triennial Conference of the European Society for the Cognitive Sciences of Music., July 23–28, 2012.