The Potential of Unsupervised Induction of Harmonic Syntax for Jazz
References
- Baker, J. K. (1979). Trainable grammars for speech recognition. The Journal of the Acoustical Society of America, 65(
S1 ), S132–S132. - Broze, Y., and Shanahan, D. (2013). Diachronic changes in jazz harmony: A cognitive perspective. Music Perception: An Interdisciplinary Journal, 31(1), 32–45.
- de Berardinis, J., Meroño‑Peñuela, A., Poltronieri, A., and Presutti, V. (2023). Choco: A chord corpus and a data transformation workflow for musical harmony knowledge graphs. Scientific Data, 10(1), 641.
- Drozdov, A., Verga, P., Yadav, M., Iyyer, M., and McCallum, A. (2019). Unsupervised latent tree induction with deep inside‑outside recursive auto‑encoders. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL‑HLT, pp. 1129–1141.
- Eisner, J. (2016).
Inside‑outside and forward‑backward algorithms are just backprop (tutorial paper) . In Proceedings of the Workshop on Structured Prediction for NLP@EMNLP, pp. 1–17. ACL. - Eremenko, V., Demirel, E., Bozkurt, B., and Serra, X. (2018). Audio‑aligned jazz harmony dataset for automatic chord transcription and corpus‑based research. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), pp. 483–490.
- Foscarin, F., Harasim, D., and Widmer, G. (2023). Predicting music hierarchies with a graph‑based neural decoder. In Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR), pp. 425–432.
- Goodman, J. (1996). Parsing algorithms and metrics. In 34th Annual Meeting of the Association for Computational Linguistics, pp. 177–183. Morgan Kaufmann Publishers/ ACL.
- Granroth‑Wilding, M., and Steedman, M. (2012). Statistical parsing for harmonic analysis of jazz chord sequences. In Non‑Cochlear Sound: Proceedings of the 38th International Computer Music Conference (ICMC). Michigan Publishing.
- Granroth‑Wilding, M., and Steedman, M. (2014). A robust parser‑interpreter for jazz chord sequences. Journal of New Music Research, 43(4), 355–374.
- Haas, B. (2004). Die neue Tonalität von Schubert bis Webern. Hören und Analysieren nach Albert Simon. Noetzel.
- Hamanaka, M., Hirata, K., and Tojo, S. (2014). Musical structural analysis database based on GTTM. In Proceedings of the 15th Conference of the International Society for Music Information Retrieval (ISMIR), pp. 325–330. ISMIR.
- Harasim, D. (2020). The learnability of the grammar of jazz: Bayesian inference of hierarchical structures in harmony. Doctoral dissertation, EPFL.
- Harasim, D., Finkensiep, C., Ericson, P., O’Donnell, T. J., and Rohrmeier, M. (2020). The jazz harmony treebank. In Proceedings of the 21th International Society for Music Information Retrieval Conference (ISMIR), pp. 207–215.
- Harasim, D., Rohrmeier, M., and O’Donnell, T. J. (2018). A generalized parsing framework for generative models of harmonic syntax. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), pp. 152–159.
- Herff, S. A., Bonetti, L., Cecchetti, G., Vuust, P., Kringelbach, M. L., and Rohrmeier, M. A. (2024). Hierarchical syntax model of music predicts theta power during music listening. Neuropsychologia, 199, 108905.
- Herff, S. A., Harasim, D., Cecchetti, G., Finkensiep, C., and Rohrmeier, M. A. (2021). Hierarchical syntactic structure predicts listeners’ sequence completion in music. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, CogSci (Vol. 43, pp. 903–909). Cognitive Science Society.
- Jurafsky, D., and Martin, J. H. (2024). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition with language models (3rd ed.).
https://web.stanford.edu/~jurafsky/slp3 . Online manuscript released August 20, 2024. - Katz, J., and Pesetsky, D. (2011). The identity thesis for language and music.
- Kim, Y., Dyer, C., & Rush, A. M. (2019). Compound probabilistic context‑free grammars for grammar induction. In Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL), pp. 2369–2385. ACL.
- Kirlin, P. B., and Jensen, D. D. (2011). Probabilistic modeling of hierarchical music analysis. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR), pp. 393–398. University of Miami.
- Lazzari, N., Poltronieri, A., and Presutti, V. (2022). Pitchclass2vec: Symbolic music structure segmentation with chord embeddings. In Proceedings of the 1st Workshop on Artificial Intelligence and Creativity, volume 3278 of CEUR Workshop Proceedings, pp. 14–30. CEUR‑WS.org
- Lazzari, N., Presutti, V., and Poltronieri, D. A. (2023). Knowledge‑based chord embeddings. Doctoral dissertation, Università di Bologna.
- Lerdahl, F., and Jackendoff, R. S. (1983). A generative theory of tonal music. MIT Press.
- Liu, W., Yang, S., Kim, Y., and Tu, K. (2023).
Simple hardware‑efficient PCFGs with independent left and right productions . In Findings of the Association for Computational Linguistics: EMNLP, pp. 1662–1669. ACL. - Manning, C. D., and Schütze, H. (2001). Foundations of statistical natural language processing. MIT Press.
- Mauch, M., Dixon, S., Harte, C., Casey, M. A., and Fields, B. (2007). Discovering chord idioms through Beatles and Real Book songs. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR), pp. 255–258. Austrian Computer Society.
- Melkonian, O. (2019).
Music as language: Putting probabilistic temporal graph grammars to good use . In Proceedings of the 7th ACM SIGPLAN International Workshop on Functional Art, Music, Modeling, and Design, FARM@ICFP, pp. 1–10. ACM. - Ogura, Y., Ohmura, H., Uehara, Y., Tojo, S., and Katsurada, K. (2020). Expectation‑based parsing for jazz chord sequences. In 17th Sound and Music Computing Conference, SMC 2020, pp. 350–356. CERN.
- Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L. … Chintala, S. (2019). PyTorch: An imperative style, high‑performance deep learning library. In Advances in Neural Information Processing Systems 32, NeurIPS (pp. 8024–8035).
- Patel, A. D. (2003). Language, music, syntax and the brain. Nature Neuroscience, 6(7), 674–681.
- Rohrmeier, M. (2011). Towards a generative syntax of tonal harmony. Journal of Mathematics and Music, 5(1), 35–53.
- Rohrmeier, M. (2020). The syntax of jazz harmony: Diatonic tonality, phrase structure, and form. Music Theory and Analysis (MTA), 7(1), 1–63.
- Rohrmeier, M., and Moss, F. C. (2021). A formal model of extended tonal harmony. In Proceedings of the 22nd International Society for Music Information Retrieval Conference (ISMIR), pp. 569–578.
- Rohrmeier, M., and Neuwirth, M. (2015).
Towards a syntax of the classical cadence . In What is a Cadence? (pp. 287–338). Leuven University Press. - Sakai, I. (1961). Syntax in universal translation. In Proceedings of the International Conference on Machine Translation and Applied Language Analysis, Teddington, UK. National Physical Laboratory.
- Schenker, H. (1979). Free composition (E. Oster, Trans.). Longman. (Original work published 1935)
- Shanahan, D., Broze, Y., and Rodgers, R. (2012). A diachronic analysis of harmonic schemata in jazz. 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, pp. 909–917.
- Simon, A. (1983). Béla Bartók: Secondes mineures–septièmes majeures (Mikrokosmos, VI/144). Schweizerische Musikzeitung/Revue Musicale Suisse, 123, 82–86.
- Steedman, M. J. (1984). A generative grammar for jazz chord sequences. Music Perception, 2(1), 52–77.
- Terefenko, D. (2014). Jazz theory: From basic to advanced study. Routledge.
- Winograd, T. (1968). Linguistics and the computer analysis of tonal harmony. Journal of Music Theory, 12(1), 2–49.
- Yang, S., Liu, W., and Tu, K. (2022). Dynamic programming in rank space: Scaling structured inference with low‑rank HMMs and PCFGs. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), pp. 4797–4809. ACL.
- Yang, S., Zhao, Y., and Tu, K. (2021a). Neural bi‑lexicalized PCFG induction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL/IJCNLP), pp. 2688–2699. ACL.
- Yang, S., Zhao, Y., and Tu, K. (2021b). PCFGs can do better: Inducing probabilistic context‑free grammars with many symbols. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL‑HLT), pp. 1487–1498. ACL.
DOI: https://doi.org/10.5334/tismir.217 | Journal eISSN: 2514-3298
Language: English
Submitted on: Sep 2, 2024
Accepted on: May 12, 2025
Published on: Jun 20, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2025 Ruben Cartuyvels, John Koslovsky, Marie-Francine Moens, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.