TY - GEN
T1 - Automatic Estimation of Harmonic Tension by Distributed Representation of Chords
AU - Nikrang, Ali
AU - Sears, David R.W.
AU - Widmer, Gerhard
N1 - 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.
KW - Cadence
KW - Harmonic progression
KW - Musical expectations
KW - Musical tension
KW - word2vec
UR - http://www.scopus.com/inward/record.url?scp=85057430746&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01692-0_2
DO - 10.1007/978-3-030-01692-0_2
M3 - Conference contribution
AN - SCOPUS:85057430746
SN - 9783030016913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 23
EP - 34
BT - Music Technology with Swing - 13th International Symposium, CMMR 2017, Revised Selected Papers
A2 - Davies, Matthew E.P.
A2 - Aramaki, Mitsuko
A2 - Kronland-Martinet, Richard
A2 - Ystad, Sølvi
PB - Springer-Verlag
T2 - 13th international Symposium on Computer Music Multidisciplinary Research, CMMR 2017
Y2 - 25 September 2017 through 28 September 2017
ER -