- Use mxl files to extract notes in each measure
- The extracted notes are stored in xml format
- Based on the concept of duration (the length of time a pitch / tone is sounded), that the duration in each measure is fixed, we create matrix stored in csv file.
Extensive studies have been conducted on both musical scores and audio tracks of western classical music with the finality of learning and detecting the key in which a particular piece of music was played. Both the Bayesian Approach and modern unsupervised learning via latent Dirichlet allocation have been used for such learning tasks. In this research work, we venture out of the western classical genre and embrace and explore jazz music. We consider the musical score sheets and audio tracks of some of the giants of jazz like Duke Ellington, Miles Davis, John Coltrane, Dizzie Gillespie, Wes Montgomery, Charlie Parker, Sonny Rollins, Louis Armstrong (Instrumental), Bill Evans, Dave Brubeck, Thelonious Monk (Pianist). We specifically employ Bayesian techniques and modern topic modelling methods (and even occasionally a combination of both) to explore tasks such as: automatic improvisation detection, genre identification, key learning (how many keys do the giants of jazz tended to play in, and what are those keys) and even elements of the mood of the piece.
- 2018 UP-STAT (Apr 21, 2018): Music Mining In Topic Modeling Approach For Improvisational Learning (Qiuyi Wu)
- 2018 JSM (Jul 30, 2018): Bayesian and Unsupervised Machine Learning Machines for Jazz Music Analysis (Qiuyi Wu)
This code is the result of work by Qiuyi Wu.
(C) Copyright 2018, Qiuyi Wu