Interactive music composition system -composition of 16-bars musical work with a melody part and backing parts

Unehara, Muneyuki; Onisawa, Takehisa

doi:10.1109/icsmc.2004.1401109

Cited by 7 publications

(3 citation statements)

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“…The system of Unehara and Onisawa [20,21], for example, accepts user ratings when generating a "cheerful" music using chromosomes that represent melody, tones and patterns of backing parts. Although Kim and Andre [5] worked with more affective descriptors that are measured based on user's self-report and results of physiological sensing, they only dealt with representing and generating rhythms.…”

Section: Related Workmentioning

confidence: 99%

“…Although evolutionary methods have been instrumental in automatic music generation (reviewed in [23]; e.g., [1,4,21]), it is to our knowledge that a GA using for its fitness function the model of user affect, which this paper has been describing in length, appears only in the CAUI.…”

Section: Tune Generationmentioning

confidence: 99%

“…It can be hypothesized that the predictive knowledge of the models can be used to analyze and control the music generation task. It may also be the case that the presence of the model permits the absence of human intervention during composition (in contrast to being user-interactive in this stage, e.g., [4,5,21]) thereby alleviating user's burden and achieving full automation. Lastly, the model can contain re-usable information that need not be restricted to the current problem domain.…”

Section: Introductionmentioning

confidence: 97%

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Music compositional intelligence with an affective flavor

Legaspi

Hashimoto

Moriyama

et al. 2007

Proceedings of the 12th International Conference on Intelligent User Interfaces

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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 handlabeled 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 affective impressions, namely, favorableunfavorable, bright-dark, happy-sad, and heartrending-not heartrending.

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Section: Related Workmentioning

confidence: 99%

Section: Tune Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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