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Affective calibration of musical feature sets in an emotionally intelligent music composition system

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Williams, D., Kirke, A., Miranda, E., Daly, I., Hwang, F., Weaver, J. and Nasuto, S. (2017) Affective calibration of musical feature sets in an emotionally intelligent music composition system. ACM Transactions on Applied Perception, 14 (3). pp. 1-13. ISSN 1544-3965

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To link to this item DOI: 10.1145/3059005

Abstract/Summary

Affectively driven algorithmic composition (AAC) is a rapidly growing field that exploits computer-aided composition in order to generate new music with particular emotional qualities or affective intentions. An AAC system was devised in order to generate a stimulus set covering nine discrete sectors of a two-dimensional emotion space by means of a 16-channel feed-forward artificial neural network. This system was used to generate a stimulus set of short pieces of music, which were rendered using a sampled piano timbre and evaluated by a group of experienced listeners who ascribed a two-dimensional valence-arousal coordinate to each stimulus. The underlying musical feature set, initially drawn from the literature, was subsequently adjusted by amplifying or attenuating the quantity of each feature in order to maximize the spread of stimuli in the valence-arousal space before a second listener evaluation was conducted. This process was repeated a third time in order to maximize the spread of valence-arousal coordinates ascribed to the generated stimulus set in comparison to a spread taken from an existing prerated database of stimuli, demonstrating that this prototype AAC system is capable of creating short sequences of music with a slight improvement on the range of emotion found in a stimulus set comprised of real-world, traditionally composed musical excerpts.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Centre for Integrative Neuroscience and Neurodynamics (CINN)
Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:71647
Publisher:Association for Computing Machinery

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