Title :
Automatic Music Emotion Classification Using a New Classification Algorithm
Author :
Sun, Xiaoyu ; Tang, Yongchuan
Author_Institution :
Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China
Abstract :
Music emotion is a special emotion that is aroused by music which is a media that can convey human affection. Music emotion classification is a popular topic in recent years. The mood of a music clip describes emotional expression. It is helpful in music understanding, music retrieval and some other interesting music related application. In this paper, a method is proposed using a framework named information cell mixture models (ICMM) to automate the task of music emotion classification. This framework has potential application in both unsupervised concept learning and supervised classification learning. This framework is acceptable for music mood classification because emotion is a vague concept and has a cognitive structure. The application of ICMM is also suitable for music emotion classification.
Keywords :
audio signal processing; cognition; emotion recognition; information retrieval; learning (artificial intelligence); music; signal classification; classification algorithm; cognitive structure; emotional expression; human affection; information cell mixture models; music clip; music emotion classification; music mood classification; music retrieval; music understanding; supervised classification learning; unsupervised concept learning; Acoustic signal detection; Application software; Classification algorithms; Computer science; Data mining; Educational institutions; Mood; Music; Stress; Support vector machines;
Conference_Titel :
Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on
Conference_Location :
Changsha
Print_ISBN :
978-0-7695-3865-5
DOI :
10.1109/ISCID.2009.281
