Paper Info
Title
Popular music representation: chorus detection & emotion recognition
Abstract
This paper proposes a popular music representation strategy based on the song's emotion. First, a piece of popular music is decomposed into chorus and verse segments through the proposed chorus detection algorithm. Three descriptive features: intensity, frequency band and rhythm regularity are extracted from the structured segments for emotion detection. A hierarchical Adaboost classifier is employed to recognize the emotion of a piece of popular music. The general emotion of the music is classified according to Thayer's model into four emotions: happy, angry, depressed and relaxed. Experiments conducted on a 350-popular-music database show the average recall and precision of our proposed chorus detection are approximately 95 % and 84 %, respectively; and the average precision rate of emotion detection is 92 %. Additional tests are performed on songs with cover versions in different lyrics and languages, and the resultant precision rate is 90 %. The proposes approaches have been tested and proven by the professional online music company, KKBOX Inc. and show promising performance for effectively and efficiently identifying the emotions of a variety of popular music.
Year
DOI
Venue
2014
10.1007/s11042-013-1687-2
Multimedia Tools and Applications
Keywords
Field
DocType
rhythm,popular music,emotion,adaboost,chorus,verse,mfccs,Popular music,Chorus,Verse,MFCCs,Rhythm,Emotion,Adaboost
AdaBoost,Computer science,Emotion recognition,Precision and recall,Speech recognition,Popular music,Emotion detection,Lyrics,Chorus,Rhythm
Journal
Volume
Issue
ISSN
73
3
1380-7501
Citations 
PageRank 
References 
1
0.36
30
Authors
8
Name
Order
Citations
PageRank
Chia-Hung Yeh136742.15
Wen-Yu Tseng2335.67
Chia-Yen Chen36314.80
Yu-Dun Lin410.69
Yi-Ren Tsai510.36
Hsuan-I Bi610.36
Yu-Ching Lin710.36
Ho-Yi Lin810.36