Music emotion recognition with consideration of personal preference

Chuan Yu Chang, Chi Keng Wu, Chun Yen Lo, Chi-Jen Wang, Pau-Choo Chung

研究成果: Conference contribution

4 引文 (Scopus)

摘要

In order to discover the relationship between music and the emotion that it may evoke, twenty-one features have been extracted to describe music. A feature selection algorithm called sequential floating forward selection (SFFS) is utilized to find discriminative features. An estimation of the correlation coefficient was applied to determine features of music that evoke an emotion. These features were then used to train two support vector machines (SVMs) for an individual subject to classify music that evokes happiness, anger, sadness, and peacefulness. Experimental results show that the proposed approach can be used to classify music that evokes an emotion for an individual subject with high classification accuracy.

原文English
主出版物標題2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011
DOIs
出版狀態Published - 2011 十二月 13
事件2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011 - Poitiers, France
持續時間: 2011 九月 52011 九月 7

出版系列

名字2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011

Other

Other2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011
國家France
城市Poitiers
期間11-09-0511-09-07

指紋

Support vector machines
Feature extraction

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

引用此文

Chang, C. Y., Wu, C. K., Lo, C. Y., Wang, C-J., & Chung, P-C. (2011). Music emotion recognition with consideration of personal preference. 於 2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011 [6076843] (2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011). https://doi.org/10.1109/nDS.2011.6076843
Chang, Chuan Yu ; Wu, Chi Keng ; Lo, Chun Yen ; Wang, Chi-Jen ; Chung, Pau-Choo. / Music emotion recognition with consideration of personal preference. 2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011. 2011. (2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011).
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abstract = "In order to discover the relationship between music and the emotion that it may evoke, twenty-one features have been extracted to describe music. A feature selection algorithm called sequential floating forward selection (SFFS) is utilized to find discriminative features. An estimation of the correlation coefficient was applied to determine features of music that evoke an emotion. These features were then used to train two support vector machines (SVMs) for an individual subject to classify music that evokes happiness, anger, sadness, and peacefulness. Experimental results show that the proposed approach can be used to classify music that evokes an emotion for an individual subject with high classification accuracy.",
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Chang, CY, Wu, CK, Lo, CY, Wang, C-J & Chung, P-C 2011, Music emotion recognition with consideration of personal preference. 於 2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011., 6076843, 2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011, 2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011, Poitiers, France, 11-09-05. https://doi.org/10.1109/nDS.2011.6076843

Music emotion recognition with consideration of personal preference. / Chang, Chuan Yu; Wu, Chi Keng; Lo, Chun Yen; Wang, Chi-Jen; Chung, Pau-Choo.

2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011. 2011. 6076843 (2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011).

研究成果: Conference contribution

TY - GEN

T1 - Music emotion recognition with consideration of personal preference

AU - Chang, Chuan Yu

AU - Wu, Chi Keng

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AU - Wang, Chi-Jen

AU - Chung, Pau-Choo

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N2 - In order to discover the relationship between music and the emotion that it may evoke, twenty-one features have been extracted to describe music. A feature selection algorithm called sequential floating forward selection (SFFS) is utilized to find discriminative features. An estimation of the correlation coefficient was applied to determine features of music that evoke an emotion. These features were then used to train two support vector machines (SVMs) for an individual subject to classify music that evokes happiness, anger, sadness, and peacefulness. Experimental results show that the proposed approach can be used to classify music that evokes an emotion for an individual subject with high classification accuracy.

AB - In order to discover the relationship between music and the emotion that it may evoke, twenty-one features have been extracted to describe music. A feature selection algorithm called sequential floating forward selection (SFFS) is utilized to find discriminative features. An estimation of the correlation coefficient was applied to determine features of music that evoke an emotion. These features were then used to train two support vector machines (SVMs) for an individual subject to classify music that evokes happiness, anger, sadness, and peacefulness. Experimental results show that the proposed approach can be used to classify music that evokes an emotion for an individual subject with high classification accuracy.

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Chang CY, Wu CK, Lo CY, Wang C-J, Chung P-C. Music emotion recognition with consideration of personal preference. 於 2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011. 2011. 6076843. (2011 7th International Workshop on Multidimensional (nD) Systems, nDS 2011). https://doi.org/10.1109/nDS.2011.6076843