Semantic differential is often used to investigate the relationship between music and other sensory modalities such as colors, tastes, vision, and odors. This work proposes an exploratory approach including open-ended responses and subsequent machine learning to study cross-modal associations, based on a recently developed sensory scale that does not use any explicit verbal description. Twenty-five participants were asked to report a piece of music they considered close to the feel/look/experience of a given sensory stimulus. Results show that the associations reported by the participants can be explained, at least in part, by a set of features related to some timbric and tonal aspects of music.
CITATION STYLE
Murari, M., Chmiel, A., Tiepolo, E., Zhang, J. D., Canazza, S., Rodà, A., & Schubert, E. (2020). Key Clarity is Blue, Relaxed, and Maluma: Machine Learning Used to Discover Cross-Modal Connections Between Sensory Items and the Music They Spontaneously Evoke. In Advances in Intelligent Systems and Computing (Vol. 1256 AISC, pp. 214–223). Springer. https://doi.org/10.1007/978-981-15-7801-4_22
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