Exploring melodic motif to support an affect-based music compositional intelligence

Although the design of our constructive adaptive user interface (CAUI) for an affect-based music compositional artificial intelligence has been modified on several fronts since the time it was introduced, what has become a persisting limitation of our research is the extent by which it should effici...

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Main Authors: Legaspi, Roberto S., Ueda, Akinobu, Cabredo, Rafael A., Nishikawa, Takayuki, Fukui, Ken Ichi, Moriyama, Koichi, Kurihara, Satoshi, Numao, Masayuki
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Published: Animo Repository 2011
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/5431
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-62912022-08-15T01:29:29Z Exploring melodic motif to support an affect-based music compositional intelligence Legaspi, Roberto S. Ueda, Akinobu Cabredo, Rafael A. Nishikawa, Takayuki Fukui, Ken Ichi Moriyama, Koichi Kurihara, Satoshi Numao, Masayuki Although the design of our constructive adaptive user interface (CAUI) for an affect-based music compositional artificial intelligence has been modified on several fronts since the time it was introduced, what has become a persisting limitation of our research is the extent by which it should efficiently cover music theory effectively. This paper reports our initial investigation on the possible significant contribution of melodic motif in creating compositions that are more fluent and cohesive. From an initial collection of 10 melodic motifs from different musical pieces, we provided heuristic-based renditions to these melodic motifs, four for each one, and obtained a total of 50 melodic motifs. We asked 10 subjects to provide self-annotations of the affective flavor of these motifs. We then represented these motifs as first-order logic predicates and employed inductive logic programming for the CAUI to learn relations of user affect perceptions and music features. To obtain new compositions, we first used a genetic algorithm with a fitness functions that is based on the induced relations for the CAUI to generate chordal tone variants. We then used probabilistic modifications for the CAUI to alter these chordal tones to become non-harmonic tones. The CAUI composed 60 new user-specific affect-based musical pieces for each subject. Our results indicate that the compositions differ significantly for only one pair of affect type when the subject evaluations of the CAUI compositions were compared using paired t-test. However, when we compared the subject evaluations of the quality of the melodies and of the musical pieces from when melodic motif variants were not considered, the improvement is significant with t-values of 5.86 and 6.33, respectively, for a significance level of 0.01. 2011-10-14T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/5431 Faculty Research Work Animo Repository Music, Influence of Music—Physiological effect Information storage and retrieval systems—Music Emotion recognition Human-computer interaction Computer Sciences Music
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Music, Influence of
Music—Physiological effect
Information storage and retrieval systems—Music
Emotion recognition
Human-computer interaction
Computer Sciences
Music
spellingShingle Music, Influence of
Music—Physiological effect
Information storage and retrieval systems—Music
Emotion recognition
Human-computer interaction
Computer Sciences
Music
Legaspi, Roberto S.
Ueda, Akinobu
Cabredo, Rafael A.
Nishikawa, Takayuki
Fukui, Ken Ichi
Moriyama, Koichi
Kurihara, Satoshi
Numao, Masayuki
Exploring melodic motif to support an affect-based music compositional intelligence
description Although the design of our constructive adaptive user interface (CAUI) for an affect-based music compositional artificial intelligence has been modified on several fronts since the time it was introduced, what has become a persisting limitation of our research is the extent by which it should efficiently cover music theory effectively. This paper reports our initial investigation on the possible significant contribution of melodic motif in creating compositions that are more fluent and cohesive. From an initial collection of 10 melodic motifs from different musical pieces, we provided heuristic-based renditions to these melodic motifs, four for each one, and obtained a total of 50 melodic motifs. We asked 10 subjects to provide self-annotations of the affective flavor of these motifs. We then represented these motifs as first-order logic predicates and employed inductive logic programming for the CAUI to learn relations of user affect perceptions and music features. To obtain new compositions, we first used a genetic algorithm with a fitness functions that is based on the induced relations for the CAUI to generate chordal tone variants. We then used probabilistic modifications for the CAUI to alter these chordal tones to become non-harmonic tones. The CAUI composed 60 new user-specific affect-based musical pieces for each subject. Our results indicate that the compositions differ significantly for only one pair of affect type when the subject evaluations of the CAUI compositions were compared using paired t-test. However, when we compared the subject evaluations of the quality of the melodies and of the musical pieces from when melodic motif variants were not considered, the improvement is significant with t-values of 5.86 and 6.33, respectively, for a significance level of 0.01.
format text
author Legaspi, Roberto S.
Ueda, Akinobu
Cabredo, Rafael A.
Nishikawa, Takayuki
Fukui, Ken Ichi
Moriyama, Koichi
Kurihara, Satoshi
Numao, Masayuki
author_facet Legaspi, Roberto S.
Ueda, Akinobu
Cabredo, Rafael A.
Nishikawa, Takayuki
Fukui, Ken Ichi
Moriyama, Koichi
Kurihara, Satoshi
Numao, Masayuki
author_sort Legaspi, Roberto S.
title Exploring melodic motif to support an affect-based music compositional intelligence
title_short Exploring melodic motif to support an affect-based music compositional intelligence
title_full Exploring melodic motif to support an affect-based music compositional intelligence
title_fullStr Exploring melodic motif to support an affect-based music compositional intelligence
title_full_unstemmed Exploring melodic motif to support an affect-based music compositional intelligence
title_sort exploring melodic motif to support an affect-based music compositional intelligence
publisher Animo Repository
publishDate 2011
url https://animorepository.dlsu.edu.ph/faculty_research/5431
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