Paper Info
Title
KANSEI (Emotional) Information Classifications of Music Scores Using Self Organizing Map.
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.
Year
DOI
Venue
2016
10.1007/978-3-319-42007-3_50
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
KANSEI information,Emotional information,Music score classification,Self-organizing,Feature map
Computer science,Musical,Kansei,Self-organizing map,Natural language processing,Artificial intelligence,Metre (music)
Conference
Volume
ISSN
Citations 
9799
0302-9743
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Satoshi Kawamura1254.82
Hitoaki Yoshida202.70