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Not All Roads Lead to Rome: Pitch Representation and Model Architecture for Automatic Harmonic Analysis Cover

Not All Roads Lead to Rome: Pitch Representation and Model Architecture for Automatic Harmonic Analysis

Transactions of the International Society for Music Information Retrieval
Volume 3 (2020): Issue 1
By: Gianluca Micchi,  Mark Gotham and  Mathieu Giraud  
Open Access
|May 2020

References

  1. 1Briot, J.-P., Hadjeres, G., & Pachet, F.-D. (2020). Deep Learning Techniques for Music Generation. Computational Synthesis and Creative Systems. Springer. DOI: 10.1007/978-3-319-70163-9
    Back to article
  2. 2Chen, T.-P., & Su, L. (2018). Functional harmony recognition of symbolic music data with multi-task recurrent neural networks. In International Society for Music Information Retrieval Conference (ISMIR 2018).
    Back to article
  3. 3Chen, T.-P., & Su, L. (2019). Harmony Transformer: Incorporating chord segmentation into harmony recognition. In International Society for Music Information Retrieval Conference (ISMIR 2019).
    Back to article
  4. 4Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078. DOI: 10.3115/v1/D14-1179
    Back to article
  5. 5Clendinning, J. P., & Marvin, E. W. (2016). The Musician’s Guide to Theory and Analysis. W.W. Norton, 3rd edition.
    Back to article
  6. 6Cohn, R. (1999). As wonderful as star clusters: Instruments for gazing at tonality in Schubert. 19th-Century Music, 22(3), 213–232. DOI: 10.1525/ncm.1999.22.3.02a00020
    Back to article
  7. 7Cohn, R. (2012). Audacious Euphony: Chromatic Harmony and the Triad’s Second Nature. Oxford Studies in Music Theory. Oxford University Press, USA.
    Back to article
  8. 8Cross, I. (1997). Pitch schemata. In I. Deliège & J. Sloboda (Eds.), Perception and Cognition of Music, pages 357–390. Psychology Press, Hove.
    Back to article
  9. 9Cuthbert, M. S., & Ariza, C. (2010). music21: A toolkit for computer-aided musicology and symbolic music data. In International Society for Music Information Retrieval Conference (ISMIR 2010), pages 637–642.
    Back to article
  10. 10de Clercq, T., & Temperley, D. (2011). A corpus analysis of rock harmony. Popular Music, 30(1), 47–70. DOI: 10.1017/S026114301000067X
    Back to article
  11. 11De Haas, W. B., Rohrmeier, M., Veltkamp, R. C., & Wiering, F. (2009). Modeling harmonic similarity using a generative grammar of tonal harmony. In International Society for Music Information Retrieval Conference (ISMIR 2009), pages 549–554.
    Back to article
  12. 12Devaney, J., Arthur, C., Condit-Schultz, N., & Nisula, K. (2015). Theme and variation encodings with Roman numerals (TAVERN): A new data set for symbolic music analysis. In International Society for Music Information Retrieval Conference (ISMIR 2015).
    Back to article
  13. 13Duinker, B. (2019). Plateau loops and hybrid tonics in recent pop music. Music Theory Online, 25(4). DOI: 10.30535/mto.25.4.3
    Back to article
  14. 14Euler, L. (1739). Tentamen Novae Theoriae Musicae ex Certissimis Harmoniae Principiis Dilucide Expositae. Saint Petersburg Academy.
    Back to article
  15. 15Giraud, M., Groult, R., & Leguy, E. (2018). Dezrann, a web framework to share music analysis. In International Conference on Technologies for Music Notation and Representation (TENOR 2018), pages 104–110.
    Back to article
  16. 16Hadjeres, G., Pachet, F., & Nielsen, F. (2017). Deep-Bach: A steerable model for Bach chorales generation. In Proceedings of the 34th International Conference on Machine Learning – Volume 70, ICML’17, page 1362–1371. JMLR.org.
    Back to article
  17. 17Harasim, D., Rohrmeier, M., & O’Donnell, T. J. (2018). A generalized parsing framework for generative models of harmonic syntax. In International Society for Music Information Retrieval Conference (ISMIR 2018), pages 152–159.
    Back to article
  18. 18Heinichen, J. D. (1711). Neu erfundene und gründliche Anweisung – zu vollkommener Erlernung des General-Basses. Schiller, Hamburg.
    Back to article
  19. 19Holtzman, S. R. (1977). A program for key determination. Interface, 6, 29–56. DOI: 10.1080/09298217708570231
    Back to article
  20. 20Huang, C.-Z. A., Vaswani, A., Uszkoreit, J., Shazeer, N., Simon, I., Hawthorne, C., Dai, A. M., Hoffman, M. D., Dinculescu, M., & Eck, D. (2018). Music Transformer. arXiv:1809.0428.
    Back to article
  21. 21Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016). Densely connected convolutional networks. arXiv:1608.06993. DOI: 10.1109/CVPR.2017.243
    Back to article
  22. 22Illescas, P. R., Rizo, D., & Iñesta, J. M. (2007). Harmonic, melodic, and functional automatic analysis. In International Computer Music Conference (ICMC 2007), pages 165–168.
    Back to article
  23. 23Ju, Y., Condit-Schultz, N., Arthur, C., & Fujinaga, I. (2017). Non-chord tone identification using deep neural networks. In International Workshop on Digital Libraries for Musicology (DLfM’17), pages 13–16. DOI: 10.1145/3144749.3144753
    Back to article
  24. 24Ju, Y., Howes, S., McKay, C., Condit-Schultz, N., Calvo-Zaragoza, J., & Fujinaga, I. (2019). An interactive workflow for generating chord labels for homorhythmic music in symbolic formats. In International Society for Music Information Retrieval Conference (ISMIR 2019).
    Back to article
  25. 25Krumhansl, C. L., & Kessler, E. J. (1982). Tracing the dynamic changes in perceived tonal organisation in a spatial representation of musical keys. Psychological Review, 89(2), 334–368. DOI: 10.1037/0033-295X.89.4.334
    Back to article
  26. 26Kröger, P., Passos, A., & Sampaio, M. (2008). Rameau: a system for automatic harmonic analysis. In International Computer Music Conference (ICMC 2008).
    Back to article
  27. 27Laitz, S. G. (2016). The Complete Musician: An Integrated Approach to Theory, Analysis, and Listening. Oxford University Press, 4th edition.
    Back to article
  28. 28Lerdahl, F., & Jackendoff, R. (1983). A Generative Theory of Tonal Music. MIT Press.
    Back to article
  29. 29Lewin, D. (1987). Generalized Musical Intervals and Transformations. Yale University Press.
    Back to article
  30. 30Liang, F. T., Gotham, M., Johnson, M., & Shotton, J. (2017). Automatic stylistic composition of Bach chorales with deep LSTM. In International Society for Music Information Retrieval Conference (ISMIR 2017), pages 449–456.
    Back to article
  31. 31Madsen, S. T., & Widmer, G. (2007). Key-finding with interval profiles. In International Computer Music Conference (ICMC 2007).
    Back to article
  32. 32McFee, B., & Bello, J. P. (2017). Structured training for large-vocabulary chord recognition. In International Society for Music Information Retrieval Conference (ISMIR 2017).
    Back to article
  33. 33Nápoles López, N., Arthur, C., & Fujinaga, I. (2019). Key-finding based on a hidden Markov model and key profiles. In International Workshop on Digital Libraries for Musicology (DLfM’19). DOI: 10.1145/3358664.3358675
    Back to article
  34. 34Neuwirth, M., Harasim, D., Moss, F. C., & Rohrmeier, M. (2018). The annotated Beethoven corpus (ABC): A dataset of harmonic analyses of all Beethoven string quartets. Frontiers in Digital Humanities, 5. DOI: 10.3389/fdigh.2018.00016
    Back to article
  35. 35Oettingen, A. (1866). Harmoniesystem in dualer Entwicklung. Leipzig.
    Back to article
  36. 36Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: A generative model for raw audio. arXiv:1609.03499.
    Back to article
  37. 37Oore, S., Simon, I., Dieleman, S., Eck, D., & Simonyan, K. (2018). This time with feeling: Learning expressive musical performance. Neural Computing and Applications. DOI: 10.1007/s00521-018-3758-9
    Back to article
  38. 38Paiement, J.-F., Eck, D., & Bengio, S. (2005). A probabilistic model for chord progressions. In International Conference on Music Information Retrieval (ISMIR 2005).
    Back to article
  39. 39Pardo, B., & Birmingham, W. P. (2002). Algorithms for chordal analysis. Computer Music Journal, 26(2), 27–49. DOI: 10.1162/014892602760137167
    Back to article
  40. 40Robine, M., Rocher, T., & Hanna, P. (2008). Improvements of key-finding methods. In International Computer Music Conference (ICMC 2008).
    Back to article
  41. 41Rocher, T., Robine, M., Hanna, P., & Strandh, R. (2009). Dynamic chord analysis for symbolic music. In International Computer Music Conference (ICMC 2009).
    Back to article
  42. 42Rohrmeier, M. (2011). Towards a generative syntax of tonal harmony. Journal of Mathematics and Music, 5(1), 35–53. DOI: 10.1080/17459737.2011.573676
    Back to article
  43. 43Sapp, C. S. (2005). Visual hierarchical key analysis. Computers in Entertainment, 3(4), 3. DOI: 10.1145/1095534.1095544
    Back to article
  44. 44Schenker, H. (1935). Der freie Satz. Universal Edition.
    Back to article
  45. 45Schoenberg, A. (1954 – op.posth, completed 1948). Structural Functions of Harmony. Williams and Norgate, London.
    Back to article
  46. 46Steedman, M., & Longuet-Higgins, H. C. (1971). On interpreting Bach. Machine Intelligence, 6.
    Back to article
  47. 47Temperley, D. (1997). An algorithm for harmonic analysis. Music Perception: An Interdisciplinary Journal, 15(1), 31–68. DOI: 10.2307/40285738
    Back to article
  48. 48Temperley, D. (1999). What’s key for key? The Krumhansl-Schmuckler key-finding algorithm reconsidered. Music Perception, 17(1). DOI: 10.2307/40285812
    Back to article
  49. 49Tymoczko, D. (2011). A Geometry of Music: Harmony and Counterpoint in the Extended Common Practice. Oxford University Press.
    Back to article
  50. 50Tymoczko, D., Gotham, M., Cuthbert, M. S., & Ariza, C. (2019). The Romantext Format: A flexible and standard method for representing Roman numeral analyses. In International Society for Music Information Retrieval Conference (ISMIR 2019).
    Back to article
  51. 51Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122.
    Back to article
DOI: https://doi.org/10.5334/tismir.45 | Journal eISSN: 2514-3298
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Language: English
Submitted on: Dec 4, 2019
Accepted on: Mar 23, 2020
Published on: May 12, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Roman numeral analysis,
functional harmony,
machine learning,
pitch encoding,
corpus

© 2020 Gianluca Micchi, Mark Gotham, Mathieu Giraud, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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