@inBook{
 id = {9b9eaca9-ae81-342e-a52d-68f0dad20e3b},
 title = {Emotion Identification System for Musical Tunes based on Characteristics of Acoustic Signal Data},
 type = {inBook},
 year = {2014},
 keywords = {emotions,music,repetitions,tune’s internal homogeneity,tune’s thumbnail},
 created = {2014-12-10T23:37:56.000Z},
 pages = {88 - 107},
 volume = {272},
 websites = {http://www.iospress.nl/book/information-modelling-and-knowledge-bases-xxvi/},
 publisher = {IOS Press},
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 profile_id = {1a39a66b-a9cd-3b44-8498-85d3ad34e830},
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 last_modified = {2014-12-11T05:27:35.000Z},
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 citation_key = {Endrjukaite2014a},
 abstract = {We design and implement a music-tune analysis system to realize automatic emotion identification and prediction based on acoustic signal data. To compute physical elements of music pieces we define three significant tunes parameters. These are: repeated parts or repetitions inside a tune, thumbnail of a music piece, and homogeneity pattern of a tune. They are significant, because they are related to how people perceive music pieces. By means of these three parameters we can express the essential features of emotional-aspects of each piece. Our system consists of music-tune features database and computational mechanism for comparison between different tunes. Based on Hevner’s emotions adjectives groups we created a new way of emotion presentation on emotion’s plane with two axes: activity and happiness. That makes it possible to determine perceived emotions of listening to a tune and calculate adjacent emotions on a plane. Finally, we performed a set of experiments on western classical and popular music pieces, which presented that our proposed approach reached 72% precision ratio and shows a positive trend of system’s efficiency when database size is increasing.},
 bibtype = {inBook},
 author = {Endrjukaite, Tatiana and Yasushi Kiyoki, undefined},
 book = {Information Modelling and Knowledge Bases XXVI}
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