Applications and Extensions of music21¶
Music21 has been used in numerous research tasks already, and will continue to offer researchers many tools with which to explore new domains.
Papers, Presentations, and Publications¶
The following papers and publications make extensive use of music21. Start here:
Cuthbert, Michael Scott Cuthbert and Christopher Ariza. 2010. “music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data.” In Proceedings of the International Society for Music Information Retrieval. https://www.academia.edu/243058/music21_A_Toolkit_for_Computer-Aided_Musicology_and_Symbolic_Music_Data
Then continue with:
Church, Maura and Michael Scott Cuthbert. 2014. “Improving Rhythmic Transcriptions via Probability Models Applied Post-OMR.” In Proceedings of the International Society for Music Information Retrieval. https://www.academia.edu/7709124/Improving_Rhythmic_Transcriptions_via_Probability_Models_Applied_Post-OMR
Cuthbert, Michael Scott, Beth Hadley, Lars Johnson, and Christopher Reyes. 2012. “Interoperable Digital Musicology Research via music21 Web Applications.” From Joint CLARIN-D/DARIAH Workshop at Digital Humanities Conference Hamburg. https://www.academia.edu/1787946/Interoperable_Digital_Musicology_Research_via_music21_Web_Applications
Cuthbert, Michael Scott, Chris Ariza, Jose Cabal-Ugaz, Beth Hadley, and Neena Parikh. 2011. “Hidden Beyond MIDI’s Reach:Feature Extraction and Machine Learning with Rich Symbolic Formats in music21” In Proceedings of the Neural Information Processing Systems Conference. https://www.academia.edu/1256513/Hidden_Beyond_MIDI_s_Reach_Feature_Extraction_and_Machine_Learning_with_Rich_Symbolic_Formats_in_music21
Cuthbert, Michael Scott, Chris Ariza, and Lisa D. Friedland. 2011. “Feature Extraction and Machine Learning on Symbolic Music using the music21 Toolkit” In Proceedings of the International Symposium on Music Information Retrieval https://www.academia.edu/1256514/Feature_Extraction_and_Machine_Learning_on_Symbolic_Music_using_the_music21_Toolkit
Jordi Barthomé Guillen and Michael Scott Cuthbert. 2011. “Score Following from Inaccurate Score and Audio Data using OMR and music21.” In Proceedings of the Neural Information Processing Systems Conference (Music and Machine Learning, Workshop 4. https://www.academia.edu/1256512/Score_Following_from_Inaccurate_Score_and_Audio_Data_using_OMR_and_music21
Ariza, C. and Michael Scott Cuthbert. 2011. “The music21 Stream: A New Object Model for Representing, Filtering, and Transforming Symbolic Musical Structures.” In Proceedings of the International Computer Music Conference. San Francisco: International Computer Music Association, pp. 61-68. Available online at http://www.flexatone.org/static/docs/music21Stream.pdf
Ariza, C. and Michael Scott Cuthbert. 2011. “Analytical and Compositional Applications of a Network-Based Scale Model in music21.” In Proceedings of the International Computer Music Conference. San Francisco: International Computer Music Association, pp. 701-708. Available online at http://www.flexatone.org/static/docs/scaleNetwork.pdf
Ariza, C. and Michael Scott Cuthbert. 2010. “Modeling Beats, Accents, Beams, and Time Signatures Hierarchically with music21 Meter Objects.” In Proceedings of the International Computer Music Conference. San Francisco: International Computer Music Association. 216-223. Available online at http://mit.edu/music21/papers/2010MeterObjects.pdf
Future Goals and Potential Applications¶
There are numerous applications for music21 that we anticipate, yet simply have not had time to implement. Consider taking on one of these projects, or write us with new and interesting suggestions. To contact the authors, visit Authors, Acknowledgments, Contributing, and Licensing.
Slonimsky scale types
Automatic clarinet fingering generation via ClarFinger font (link: www.trecento.com/fonts/)
Automatic string fingerings.
Indian Raga encoding: including ascending, descending, and typical presentations, microtonal inflections, common associations, historical context.
Multiple-simultaneous-tempi to a single tempo conversion (via tuplets).
Palestrina counterpoint generation (via algorithms of Mary Farbood and others.).
Beneventan chant similarity indices (thanks to the work of Thomas Forrest Kelly and the Exultet encodings made available in **kern by Elsa De Luca).
Identify potential clefs for fragmentary Renaissance and Medieval pieces that are missing their clefs. (Use their staff-lines and minimizing number of melodic and harmonic tritones).