NDLI: Music compositional intelligence with an affective flavor

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Music compositional intelligence with an affective flavor

Content Provider	ACM Digital Library
Author	Legaspi, Roberto Numao, Masayuki Moriyama, Koichi Kurihara, Satoshi Hashimoto, Yuya
Abstract	The consideration of human feelings in automated music generation by intelligent music systems, albeit a compelling theme, has received very little attention. This work aims to computationally specify a system's music compositional intelligence that tightly couples with the listener's affective perceptions. First, the system induces a model that describes the relationship between feelings and musical structures. The model is learned by applying the inductive logic programming paradigm of FOIL coupled with the Diverse Density weighting metric over a dataset that was constructed using musical score fragments that were hand-labeled by the listener according to a semantic differential scale that uses bipolar affective descriptor pairs. A genetic algorithm, whose fitness function is based on the acquired model and follows basic music theory, is then used to generate variants of the original musical structures. Lastly, the system creates chordal and non-chordal tones out of the GA-obtained variants. Empirical results show that the system is 80.6% accurate at the average in classifying the affective labels of the musical structures and that it is able to automatically generate musical pieces that stimulate four kinds of impressions, namely, favorable-unfavorable, bright-dark, happy-sad, and heartrending-not heartrending.
Starting Page	216
Ending Page	224
Page Count	9
File Format	PDF
ISBN	1595934812
DOI	10.1145/1216295.1216335
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-01-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Affective computing Automated reasoning User modeling Adaptive user interface
Content Type	Text
Resource Type	Article

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