Automatic Estimation of Harmonic Tension by Distributed Representation of Chords

Ali Nikrang; David R.W. Sears; Gerhard Widmer

doi:10.1007/978-3-030-01692-0_2

Automatic Estimation of Harmonic Tension by Distributed Representation of Chords

Ali Nikrang, David R.W. Sears, Gerhard Widmer

Visual and Performing Arts

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications [27]. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of well-defined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model’s predictions conform very well with empirical evidence obtained from human listeners.

Original language	English
Title of host publication	Music Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers
Editors	Matthew E.P. Davies, Mitsuko Aramaki, Richard Kronland-Martinet, Sølvi Ystad
Publisher	Springer-Verlag
Pages	23-34
Number of pages	12
ISBN (Print)	9783030016913
DOIs	https://doi.org/10.1007/978-3-030-01692-0_2
State	Published - 2018
Event	13th international Symposium on Computer Music Multidisciplinary Research, CMMR 2017 - Matosinhos, Portugal Duration: Sep 25 2017 → Sep 28 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11265 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th international Symposium on Computer Music Multidisciplinary Research, CMMR 2017
Country/Territory	Portugal
City	Matosinhos
Period	09/25/17 → 09/28/17

Keywords

Cadence
Harmonic progression
Musical expectations
Musical tension
word2vec

Access to Document

10.1007/978-3-030-01692-0_2

Cite this

Nikrang, A., Sears, D. R. W., & Widmer, G. (2018). Automatic Estimation of Harmonic Tension by Distributed Representation of Chords. In M. E. P. Davies, M. Aramaki, R. Kronland-Martinet, & S. Ystad (Eds.), Music Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers (pp. 23-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11265 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-030-01692-0_2

Nikrang, Ali ; Sears, David R.W. ; Widmer, Gerhard. / Automatic Estimation of Harmonic Tension by Distributed Representation of Chords. Music Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers. editor / Matthew E.P. Davies ; Mitsuko Aramaki ; Richard Kronland-Martinet ; Sølvi Ystad. Springer-Verlag, 2018. pp. 23-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications [27]. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of well-defined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model{\textquoteright}s predictions conform very well with empirical evidence obtained from human listeners.",

keywords = "Cadence, Harmonic progression, Musical expectations, Musical tension, word2vec",

author = "Ali Nikrang and Sears, {David R.W.} and Gerhard Widmer",

note = "Funding Information: Acknowledgements. This research is supported by the European Research Council (ERC) under the EUs Horizon 2020 Framework Programme (ERC Grant Agreement number 670035, project “Con Espressione”). Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; null ; Conference date: 25-09-2017 Through 28-09-2017",

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doi = "10.1007/978-3-030-01692-0_2",

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Nikrang, A, Sears, DRW & Widmer, G 2018, Automatic Estimation of Harmonic Tension by Distributed Representation of Chords. in MEP Davies, M Aramaki, R Kronland-Martinet & S Ystad (eds), Music Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11265 LNCS, Springer-Verlag, pp. 23-34, 13th international Symposium on Computer Music Multidisciplinary Research, CMMR 2017, Matosinhos, Portugal, 09/25/17. https://doi.org/10.1007/978-3-030-01692-0_2

Automatic Estimation of Harmonic Tension by Distributed Representation of Chords. / Nikrang, Ali; Sears, David R.W.; Widmer, Gerhard.

Music Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers. ed. / Matthew E.P. Davies; Mitsuko Aramaki; Richard Kronland-Martinet; Sølvi Ystad. Springer-Verlag, 2018. p. 23-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11265 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Nikrang, Ali

AU - Sears, David R.W.

AU - Widmer, Gerhard

N1 - Funding Information: Acknowledgements. This research is supported by the European Research Council (ERC) under the EUs Horizon 2020 Framework Programme (ERC Grant Agreement number 670035, project “Con Espressione”). Publisher Copyright: © 2018, Springer Nature Switzerland AG.

PY - 2018

Y1 - 2018

N2 - The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications [27]. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of well-defined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model’s predictions conform very well with empirical evidence obtained from human listeners.

AB - The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications [27]. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of well-defined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model’s predictions conform very well with empirical evidence obtained from human listeners.

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Nikrang A, Sears DRW, Widmer G. Automatic Estimation of Harmonic Tension by Distributed Representation of Chords. In Davies MEP, Aramaki M, Kronland-Martinet R, Ystad S, editors, Music Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers. Springer-Verlag. 2018. p. 23-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01692-0_2

Automatic Estimation of Harmonic Tension by Distributed Representation of Chords

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