Abstract
We classified KANSEI (emotional) information for musical compositions by using only the notes in the music score. This is in contrast to the classification of music by using audio files, which are taken from a performance with the emotional information processed by the instrumentalists. The first is classification into one of two classes, duple meter or irregular meter. The second is classification into one of the two classes, slow vs. fast (threshold tempo: ♩ = 110). The classification of the musical meter is based on identifying the meter indicated in the score. For tempo classification, we generally used the tempo indication in the score, but we evaluate classification that includes tempo revisions through a subject’s emotions to be accurate. We performed classification for both the meter and tempo evaluations with a recognition rate above 70 % by using self-organizing maps for unsupervised online training. Particularly, in the tempo classification, a computer successfully processed the emotional information directed.
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Acknowledgements
We thanks to Kawamura’s students, Mika Watanabe and Soh Sato for their supports who participated in the experiment.
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Kawamura, S., Yoshida, H. (2016). KANSEI (Emotional) Information Classifications of Music Scores Using Self Organizing Map. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_50
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DOI: https://doi.org/10.1007/978-3-319-42007-3_50
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