{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:26:12Z","timestamp":1740201972400,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>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 show a positive trend of system's efficiency when database size is increasing.<\/jats:p>","DOI":"10.3233\/978-1-61499-472-5-88","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:24Z","timestamp":1740133644000},"source":"Crossref","is-referenced-by-count":0,"title":["Emotion Identification System for Musical Tunes based on Characteristics of Acoustic Signal Data"],"prefix":"10.3233","author":[{"family":"Endrjukaite Tatiana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Kiyoki Yasushi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Modelling and Knowledge Bases XXVI"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:34:17Z","timestamp":1740134057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=272&spage=88"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-472-5-88","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2014]]}}}