Some issues of mood classification for Chinese popular music

Author

Jianglong Zhang ; Xianglin Huang ; Lifang Yang ; Ye Xu

Author_Institution

Sch. of Comput., Commun. Univ. of China, Beijing, China

fYear

2015

fDate

3-6 May 2015

Firstpage

1193

Lastpage

1198

Abstract

Music mood can express inherent emotional meaning of a music clip. It´s used in music recommendation, music information retrieval, and music classification. In this paper, we follow the Thayer´s emotion plane, and extract three different features sets to apply the Chinese popular music mood-detection. We find that the distribution of music moods is quite different from west popular music. Moreover, some feature extract tools which are developed for west popular music aren´t suitable for Chinese popular music. In our experiment, we show that the valence dimension is harder to classification (best average precision: 64%) than arousal dimension (best average precision: 86%). Finally the support vector machine, k-nearest neighbors and Naïve Bayes algorithm are used to classifier the music mood. The performance of `exuberance´ mood is totally satisfactory, while the `depression´ and `contentment´ mood are hard to distinguish.

Keywords

Bayes methods; emotion recognition; feature extraction; information retrieval; music; pattern classification; recommender systems; support vector machines; Chinese popular music; Chinese popular music mood-detection; Naive Bayes algorithm; Thayer´s emotion plane; contentment mood; depression mood; feature set extraction; k-nearest neighbors; mood classification issues; music classification; music clip emotional meaning; music information retrieval; music mood; music recommendation; support vector machine; Feature extraction; Mood; Multiple signal classification; Rhythm; Timbre;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on

Conference_Location

Halifax, NS

ISSN

0840-7789

Print_ISBN

978-1-4799-5827-6

Type

conf

DOI

10.1109/CCECE.2015.7129446

Filename

7129446