Chord Types and Figuration: A Bayesian Learning Model of Extended Chord Profiles
Making sense of a musical excerpt is an acquired skill that depends on previous musical experience. Having acquired familiarity with different types of chords, a listener can distinguish tones in a musical texture that outline these chords (i.e., chord tones) from ornamental tones such as neighbor o...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | Music & Science |
| Online Access: | https://doi.org/10.1177/20592043241291661 |
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| _version_ | 1832594069290745856 |
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| author | Christoph Finkensiep Petter Ericson Sebasian Klassmann Martin Rohrmeier |
| author_facet | Christoph Finkensiep Petter Ericson Sebasian Klassmann Martin Rohrmeier |
| author_sort | Christoph Finkensiep |
| collection | DOAJ |
| description | Making sense of a musical excerpt is an acquired skill that depends on previous musical experience. Having acquired familiarity with different types of chords, a listener can distinguish tones in a musical texture that outline these chords (i.e., chord tones) from ornamental tones such as neighbor or passing notes that elaborate the chord tones. However, music-theoretical definitions of chord types usually only mention chord tones, excluding typical figurations. The aim of this project is to investigate (i) how knowledge about (chord-specific) figurations can be incorporated into characterizations of chord types and (ii) how these characterizations can be acquired by the listener. To this end, we develop a computational model of chord types that distinguishes chord tones and “figuration tones” and can be learned using Bayesian inference following methods in computational cognitive science. This model is trained on two datasets using Bayesian variational inference, comprising scores of Western classical and popular music, respectively, and containing harmonic annotations as well as heuristically determined note-type labels. We find that the proposed characterization of chords is indeed learnable and the specific inferred profiles match previous music-theoretic accounts. In addition, we can observe patterns in the use of figuration, such as the distribution of figuration tones being related to the diatonic contexts in which chords appear and chord types differing in their predisposition to generate non-chord tones. Moreover, the differences in figuration distributions between the two corpora indicate style-specific peculiarities in the role and usage of figurations. The different patterns of typical figuration tones for specific chord types indicate that harmony and figuration are not independent. |
| format | Article |
| id | doaj-art-50747997c7234d6fa9c1af79086f0216 |
| institution | Kabale University |
| issn | 2059-2043 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Music & Science |
| spelling | doaj-art-50747997c7234d6fa9c1af79086f02162025-01-20T06:03:25ZengSAGE PublishingMusic & Science2059-20432025-01-01810.1177/20592043241291661Chord Types and Figuration: A Bayesian Learning Model of Extended Chord ProfilesChristoph Finkensiep0Petter Ericson1Sebasian Klassmann2Martin Rohrmeier3 Music Cognition Group, , The Netherlands Research Group for Responsible AI, , Sweden Institute of Musicology, , Germany Digital and Cognitive Musicology Lab, , SwitzerlandMaking sense of a musical excerpt is an acquired skill that depends on previous musical experience. Having acquired familiarity with different types of chords, a listener can distinguish tones in a musical texture that outline these chords (i.e., chord tones) from ornamental tones such as neighbor or passing notes that elaborate the chord tones. However, music-theoretical definitions of chord types usually only mention chord tones, excluding typical figurations. The aim of this project is to investigate (i) how knowledge about (chord-specific) figurations can be incorporated into characterizations of chord types and (ii) how these characterizations can be acquired by the listener. To this end, we develop a computational model of chord types that distinguishes chord tones and “figuration tones” and can be learned using Bayesian inference following methods in computational cognitive science. This model is trained on two datasets using Bayesian variational inference, comprising scores of Western classical and popular music, respectively, and containing harmonic annotations as well as heuristically determined note-type labels. We find that the proposed characterization of chords is indeed learnable and the specific inferred profiles match previous music-theoretic accounts. In addition, we can observe patterns in the use of figuration, such as the distribution of figuration tones being related to the diatonic contexts in which chords appear and chord types differing in their predisposition to generate non-chord tones. Moreover, the differences in figuration distributions between the two corpora indicate style-specific peculiarities in the role and usage of figurations. The different patterns of typical figuration tones for specific chord types indicate that harmony and figuration are not independent.https://doi.org/10.1177/20592043241291661 |
| spellingShingle | Christoph Finkensiep Petter Ericson Sebasian Klassmann Martin Rohrmeier Chord Types and Figuration: A Bayesian Learning Model of Extended Chord Profiles Music & Science |
| title | Chord Types and Figuration: A Bayesian Learning Model of Extended Chord Profiles |
| title_full | Chord Types and Figuration: A Bayesian Learning Model of Extended Chord Profiles |
| title_fullStr | Chord Types and Figuration: A Bayesian Learning Model of Extended Chord Profiles |
| title_full_unstemmed | Chord Types and Figuration: A Bayesian Learning Model of Extended Chord Profiles |
| title_short | Chord Types and Figuration: A Bayesian Learning Model of Extended Chord Profiles |
| title_sort | chord types and figuration a bayesian learning model of extended chord profiles |
| url | https://doi.org/10.1177/20592043241291661 |
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