short-paper

Key-Finding Based on a Hidden Markov Model and Key Profiles

Authors:

Néstor Nápoles López,

Ichiro FujinagaAuthors Info & Claims

DLfM '19: Proceedings of the 6th International Conference on Digital Libraries for Musicology

Pages 33 - 37

https://doi.org/10.1145/3358664.3358675

Published: 09 November 2019 Publication History

Abstract

Musicologists and musicians often would like to search by keys in a digital music library. In this paper, we introduce a new key-finding algorithm that can be applied to music in both symbolic and audio formats. The algorithm, which is based on a Hidden Markov Model (HMM), provides two stages of key-finding output; the first one referring to local keys and the second one to the global key.

We describe the input, the two output stages, and the parameters of the model. In particular, we describe two configurable parameters, the transition probability distributions, which are based on a matrix of neighbouring keys, and the emission probability distributions, which make use of established key profiles.

We discuss the local key-finding capabilities of the algorithm, presenting an example analysis of the Prelude Op. 28 No. 20 in C minor by Chopin, showing the local key regions obtained using different key profiles. We evaluate the global key-finding capabilities of the model, using an existing dataset and six well-known key profiles as different model parameters.

Since different key profiles will tend to err or misclassify in different ways and across different pieces, we train an ensemble method with the predictions from all the key profiles (6) through our model. We show that the ensemble method achieves state-of-the-art performance for major and overall keys, however, it still underperforms the state-of-the-art for minor keys.

References

[1]

Bret J. Aarden. 2003. Dynamic Melodic Expectancy. Ph.D. Dissertation. The Ohio State University.

[2]

Joshua Albrecht and Daniel Shanahan. 2013. The Use of Large Corpora to Train a New Type of Key-Finding Algorithm: An Improved Treatment of the Minor Mode. Music Perception: An Interdisciplinary Journal 31, 1 (2013), 59–67.

[3]

Héctor Bellmann. 2006. About the Determination of Key of a Musical Excerpt. In Computer Music Modeling and Retrieval, Richard Kronland-Martinet, Thierry Voinier, and Solvi Ystad(Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 76–91.

[4]

Tom Collins, Sebastian Böck, Florian Krebs, and Gerhard Widmer. 2014. Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio. In Audio Engineering Society Conference: 53rd International Conference: Semantic Audio. Audio Engineering Society. http://www.aes.org/e-lib/browse.cfm?elib=17096

[5]

David Forney. 1973. The Viterbi Algorithm. Proc. IEEE 61, 3 (1973), 268–278.

[6]

David Huron. 2002. Music Information Processing Using the Humdrum Toolkit: Concepts, Examples, and Lessons. Computer Music Journal 26, 2 (2002), 11–26.

Digital Library

[7]

Filip Korzeniowski and Gerhard Widmer. 2017. End-to-End Musical Key Estimation Using a Convolutional Neural Network. In 2017 25th European Signal Processing Conference (EUSIPCO). 966–970. https://doi.org/10.23919/EUSIPCO.2017.8081351

[8]

Carol L. Krumhansl and Edward J. Kessler. 1982. Tracing the Dynamic Changes in Perceived Tonal Organization in a Spatial Representation of Musical Keys.Psychological Review 89, 4 (1982), 334–368.

[9]

Matthias Mauch and Simon Dixon. 2010. Approximate Note Transcription for the Improved Identification of Difficult Chords. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010).

[10]

Hélène Papadopoulos and Geoffroy Peeters. 2009. Local Key Estimation Based on Harmonic and Metric Structures. In 12th International Conference on Digital Audio Effects (DAFx-09), Fabio Antonacci (Ed.). Politecnico de Milano, Como, Italy, 408–415. https://hal.archives-ouvertes.fr/hal-00511452

[11]

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.

Digital Library

[12]

Jeremy Pickens, Juan Pablo Bello, Giuliano Monti, Mark Sandler, Tim Crawford, Matthew Dovey, and Don Byrd. 2003. Polyphonic Score Retrieval Using Polyphonic Audio Queries: A Harmonic Modeling Approach. Journal of New Music Research 32, 2 (2003), 223–236. https://doi.org/10.1076/jnmr.32.2.223.16742

[13]

Ian Quinn and Christopher Wm. White. 2017. Corpus-Derived Key Profiles Are Not Transpositionally Equivalent. Music Perception: An Interdisciplinary Journal 34, 5 (2017), 531–540. https://doi.org/10.1525/mp.2017.34.5.531 arXiv:https://mp.ucpress.edu/content/34/5/531.full.pdf

[14]

Lawrence R. Rabiner and Biing-Hwang Juang. 1986. An Introduction to Hidden Markov Models. IEEE ASSP Magazine 3, 1 (1986), 4–16.

[15]

Craig Stuart Sapp. 2011. Computational Methods for the Analysis of Musical Structure. Ph.D. Dissertation. Stanford University.

[16]

David Temperley. 2002. A Bayesian Approach to Key-Finding. In Music and Artificial Intelligence, Christina Anagnostopoulou, Miguel Ferrand, and Alan Smaill(Eds.). Springer, Berlin, Heidelberg, 195–206.

[17]

Petri Toiviainen. 2007. Visualization of Tonal Content in the Symbolic and Audio Domains. Computing in Musicology 15 (2007), 187–199.

[18]

George Tzanetakis, Andrey Ermolinskiy, and Perry R. Cook. 2002. Pitch Histograms in Audio and Symbolic Music Information Retrieval. In Proceedings of the 3rd International Society for Music Information Retrieval Conference. https://doi.org/10.1076/jnmr.32.2.143.16743

[19]

Gottfried Weber, James Franklin Warner, and John Bishop. 1851. Theory of Musical Composition, Treated With a View to a Naturally Consecutive Arrangement of Topics. R. Cocks, London.

Cited By

Tymoczko D(2024)Tonality10.1093/oso/9780197577103.001.0001Online publication date: 18-Jul-2024
https://doi.org/10.1093/oso/9780197577103.001.0001
Ventura M(2021)Automatic Recognition of Key Modulations in Symbolic Musical Pieces Using Information TheoryIntelligent Systems and Applications10.1007/978-3-030-82193-7_56(823-836)Online publication date: 4-Aug-2021
https://doi.org/10.1007/978-3-030-82193-7_56
Nápoles López NFeisthauer LLevé FFujinaga I(2020)On Local Keys, Modulations, and TonicizationsProceedings of the 7th International Conference on Digital Libraries for Musicology10.1145/3424911.3425515(18-26)Online publication date: 16-Oct-2020
https://dl.acm.org/doi/10.1145/3424911.3425515

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cover image ACM Other conferences

DLfM '19: Proceedings of the 6th International Conference on Digital Libraries for Musicology

November 2019

88 pages

ISBN:9781450372398

DOI:10.1145/3358664

Conference Chair:
David Rizo
Universidad de Alicante, Instituto Superior de Enseñanzas Artísticas de la Comunidad Valenciana (ISEA.CV)

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 09 November 2019

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Fonds de Recherche du Québec-Société et Culture

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DLfM '19

DLfM '19: 6th International Conference on Digital Libraries for Musicology

November 9, 2019

The Hague, Netherlands

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Overall Acceptance Rate 27 of 48 submissions, 56%

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Cited By

Tymoczko D(2024)Tonality10.1093/oso/9780197577103.001.0001Online publication date: 18-Jul-2024
https://doi.org/10.1093/oso/9780197577103.001.0001
Ventura M(2021)Automatic Recognition of Key Modulations in Symbolic Musical Pieces Using Information TheoryIntelligent Systems and Applications10.1007/978-3-030-82193-7_56(823-836)Online publication date: 4-Aug-2021
https://doi.org/10.1007/978-3-030-82193-7_56
Nápoles López NFeisthauer LLevé FFujinaga I(2020)On Local Keys, Modulations, and TonicizationsProceedings of the 7th International Conference on Digital Libraries for Musicology10.1145/3424911.3425515(18-26)Online publication date: 16-Oct-2020
https://dl.acm.org/doi/10.1145/3424911.3425515

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