An efficient emotion detection scheme for popular music

Chia Hung Yeh; Hung Hsuan Lin; Hsuan Ting Chang

doi:10.1109/ISCAS.2009.5118126

An efficient emotion detection scheme for popular music

Chia Hung Yeh^*, Hung Hsuan Lin, Hsuan Ting Chang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

18 Citations (Scopus)

Abstract

With the rapid growth of multimedia information, the ability to efficiently manage data from large amount of multimedia database has become a crucial issue. In this paper, a framework for music emotion detection is proposed. First, a Thayer's 2-Dimentinal model that represents the music emotion space is employed as our emotion model. Second, three features such as intensity, rhythm regularity, and tempo are extracted to describe a music clip. Then, features are trained by constructing Gaussian Mixture Models (GMM). Finally, the likelihood radios of test music clips to GMM are calculated for emotion identification. Experiemtal results show that the average recall and precision all are up to 80% for the database that is comprised of 145 music clips.

Original language	English
Title of host publication	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Pages	1799-1802
Number of pages	4
DOIs	https://doi.org/10.1109/ISCAS.2009.5118126
Publication status	Published - 2009
Externally published	Yes
Event	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei, Taiwan Duration: 2009 May 24 → 2009 May 27

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)	0271-4310

Other

Other	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Country/Territory	Taiwan
City	Taipei
Period	2009/05/24 → 2009/05/27

Keywords

Gaussian mixture model
Music emotion detection
Thayer's model

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ISCAS.2009.5118126

Cite this

Yeh, CH, Lin, HH & Chang, HT 2009, An efficient emotion detection scheme for popular music. in 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009., 5118126, Proceedings - IEEE International Symposium on Circuits and Systems, pp. 1799-1802, 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009, Taipei, Taiwan, 2009/05/24. https://doi.org/10.1109/ISCAS.2009.5118126

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title = "An efficient emotion detection scheme for popular music",

abstract = "With the rapid growth of multimedia information, the ability to efficiently manage data from large amount of multimedia database has become a crucial issue. In this paper, a framework for music emotion detection is proposed. First, a Thayer's 2-Dimentinal model that represents the music emotion space is employed as our emotion model. Second, three features such as intensity, rhythm regularity, and tempo are extracted to describe a music clip. Then, features are trained by constructing Gaussian Mixture Models (GMM). Finally, the likelihood radios of test music clips to GMM are calculated for emotion identification. Experiemtal results show that the average recall and precision all are up to 80% for the database that is comprised of 145 music clips.",

keywords = "Gaussian mixture model, Music emotion detection, Thayer's model",

author = "Yeh, {Chia Hung} and Lin, {Hung Hsuan} and Chang, {Hsuan Ting}",

year = "2009",

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language = "English",

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AU - Chang, Hsuan Ting

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AB - With the rapid growth of multimedia information, the ability to efficiently manage data from large amount of multimedia database has become a crucial issue. In this paper, a framework for music emotion detection is proposed. First, a Thayer's 2-Dimentinal model that represents the music emotion space is employed as our emotion model. Second, three features such as intensity, rhythm regularity, and tempo are extracted to describe a music clip. Then, features are trained by constructing Gaussian Mixture Models (GMM). Finally, the likelihood radios of test music clips to GMM are calculated for emotion identification. Experiemtal results show that the average recall and precision all are up to 80% for the database that is comprised of 145 music clips.

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KW - Music emotion detection

KW - Thayer's model

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An efficient emotion detection scheme for popular music

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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