Genre and mood classification using lyric features

Author

Ying, Teh Chao ; Doraisamy, Shyamala ; Abdullah, Lili Nurliyana

Author_Institution

Fac. of Comput. Sci. & Inf. Technol., UPM, Serdang, Malaysia

fYear

2012

fDate

13-15 March 2012

Firstpage

260

Lastpage

263

Abstract

Musical genre and mood classification techniques combining lyric and audio features for Music Information Retrieval (MIR) have been studied widely in recent years. This paper investigates the performances of musical genre and mood classification using only lyric features. In this preliminary study, the Part-of-Speech (POS) feature is utilizes for classification of a collection of 600 songs. Ten musical genre and mood categories were selected respectively based on a summary from the literature. Experiments show that classification accuracies for mood categories outperform genres.

Keywords

information retrieval; music; musical acoustics; pattern classification; MIR; POS feature; audio features; lyric features; music genre classification; music information retrieval; music mood classification; part-of-speech feature; song collection; Accuracy; Feature extraction; Hidden Markov models; Internet; Libraries; Mood; Music information retrieval; Lyrics Analysis; Music Information Retrieval; Musical Genre; Musical Mood;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Retrieval & Knowledge Management (CAMP), 2012 International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4673-1091-8

Type

conf

DOI

10.1109/InfRKM.2012.6204985

Filename

6204985