Abstract
Natural language processing methods have been applied in a variety of music studies, drawing the connection between music and language. In this paper, we expand those approaches by investigating chord embeddings, which we apply in two case studies to address two key questions: (1) what musical information do chord embeddings capture?; and (2) how might musical applications benefit from them? In our analysis, we show that they capture similarities between chords that adhere to important relationships described in music theory. In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians. In the second case study, we show the potential benefits of using the representations in tasks related to musical stylometrics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Sometimes multiple users submit chord charts for a song.
- 3.
Qualities refers to sound properties that are consistent across chords with different roots but equidistant constituent pitches. The interaction of intervals between pitches determines the quality.
- 4.
Relative refers to the relation between the chords’ roots, in which the scale beginning on the minor chord’s root shares the same notes as the scale beginning on the major chord’s root, but the ordering of the notes give different qualities to the scales.
- 5.
Special cases include: the “*” marking on a chord, which is a special marker specific to the ultimate-guitar.com site; “UNK” which we use to replace chords that do not meet the 0.1% document frequency threshold; and “H” and “Hm” which indicates “hammer-ons” in the notation on ultimate-guitar.com.
- 6.
We use an open-source repository of neural language models https://github.com/pytorch/examples/blob/master/word_language_model/model.py.
- 7.
We did not limit our next chord prediction models to these 48 chords.
- 8.
- 9.
CNN model is built on https://github.com/Shawn1993/cnn-text-classification-pytorch.
- 10.
By a paired t-test with p \(<.05\).
References
Absolu, B., Li, T., Ogihara, M.: Analysis of chord progression data. In: Raś, Z.W., Wieczorkowska, A.A. (eds.) Advances in Music Information Retrieval. Studies in Computational Intelligence, vol. 274, pp. 165–184. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-11674-2_8
Brinkman, A., Shanahan, D., Sapp, C.: Musical stylometry, machine learning and attribution studies: a semi-supervised approach to the works of Josquin. In: Proceedings of the Biennial International Conference on Music Perception and Cognition, pp. 91–97 (2016)
Brunner, G., Wang, Y., Wattenhofer, R., Wiesendanger, J.: JamBot: music theory aware chord based generation of polyphonic music with LSTMs. In: Proceedings of ICTAI (2017)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL (2019)
Feld, S., Fox, A.A.: Music and language. Annu. Rev. Anthropol. 23(1), 25–53 (1994)
Fell, M., Sporleder, C.: Lyrics-based analysis and classification of music. In: Proceedings of COLING (2014)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of KDD (2016)
Hillewaere, R., Manderick, B., Conklin, D.: Melodic models for polyphonic music classification. In: Second International Workshop on Machine Learning and Music (2009)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hontanilla, M., Pérez-Sancho, C., Iñesta, J.M.: Modeling musical style with language models for composer recognition. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds.) IbPRIA 2013. LNCS, vol. 7887, pp. 740–748. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38628-2_88
Jäncke, L.: The relationship between music and language. Front. Psychol. 3, 123 (2012)
Kaliakatsos-Papakostas, M.A., Epitropakis, M.G., Vrahatis, M.N.: Musical composer identification through probabilistic and feedforward neural networks. In: Di Chio, C., et al. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 411–420. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12242-2_42
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR 2015 (2014)
Madjiheurem, S., Qu, L., Walder, C.: Chord2vec: learning musical chord embeddings. In: Proceedings of the Constructive Machine Learning Workshop (2016)
Mayer, R., Rauber, A.: Musical genre classification by ensembles of audio and lyrics features. In: Proceedings of ISMIR (2011)
McCarthy, D., Navigli, R.: The English lexical substitution task. Lang. Resour. Eval. 43(2), 139–159 (2009). https://doi.org/10.1007/s10579-009-9084-1
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)
Ogihara, M., Li, T.: N-gram chord profiles for composer style representation. In: ISMIR, pp. 671–676 (2008)
Owen, H.: Music Theory Resource Book. Oxford University Press, USA (2000)
Patel, A.D.: Language, music, syntax and the brain. Nat. Neurosci. 6(7), 674 (2003)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of EMNLP (2014)
Peters, M., et al.: Deep contextualized word representations. In: Proceedings of NAACL (2018)
Phon-Amnuaisuk, S.: Exploring Music21 and Gensim for music data analysis and visualization. In: Tan, Y., Shi, Y. (eds.) DMBD 2019. CCIS, vol. 1071, pp. 3–12. Springer, Singapore (2019). https://doi.org/10.1007/978-981-32-9563-6_1
Randel, D.M.: The Harvard Concise Dictionary of Music and Musicians. Harvard University Press, Cambridge (1999)
Saker, M.N.: A theory of circle of fifths progressions and their application in the four Ballades by Frederic Chopin. Ph.D. thesis, University of Wisconsin-Madison (1992)
Sergeant, D.C., Himonides, E.: Gender and music composition: a study of music, and the gendering of meanings. Front. Psychol. 7, 411 (2016). https://doi.org/10.3389/fpsyg.2016.00411, https://www.frontiersin.org/article/10.3389/fpsyg.2016.00411
Shepherd, J.: A theoretical model for the sociomusicological analysis of popular musics. Popular Music 2, 145–177 (1982)
Shuyo, N.: Language detection library for java (2010). http://code.google.com/p/language-detection/
Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60(3), 538–556 (2009). https://doi.org/10.1002/asi.21001, https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.21001
Wołkowicz, J., Kulka, Z., Kešelj, V.: N-gram-based approach to composer recognition. Arch. Acoust. 33(1), 43–55 (2008)
Acknowledgements
We would like to thank the anonymous reviewers and the members of the Language and Information Technologies lab at Michigan for their helpful suggestions. We are grateful to MeiXing Dong and Charles Welch for helping with the design and interface of the next-chord annotation task. This material is based in part upon work supported by the Michigan Institute for Data Science, and by Girls Encoded and Google for sponsoring Jiajun Peng through the Explore Computer Science Research program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Michigan Institute for Data Science, Girls Encoded or Google.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lahnala, A. et al. (2021). Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021. Lecture Notes in Computer Science(), vol 12693. Springer, Cham. https://doi.org/10.1007/978-3-030-72914-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-72914-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72913-4
Online ISBN: 978-3-030-72914-1
eBook Packages: Computer ScienceComputer Science (R0)