Happiness detection in music using hierarchical SVMs with dual types of kernels

Yu Hao Chin, Chang Hong Lin, Ernestasia Siahaan, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

In this paper, we proposed a novel system for detecting happiness emotion in music. Two emotion profiles are constructed using decision value in support vector machine (SVM), and based on short term and long term feature respectively. When using short term feature to train models, the kernel used in SVM is probability product kernel. If the input feature is long term, the kernel used in SVM is RBF kernel. SVM model is trained from a raw feature set comprising the following types of features: rhythm, timbre, and tonality. Each SVM is applied to targeted emotion class with calm emotion as the background class to train hyperplanes respectively. With the eight hyperplanes trained from angry, happy, sad, relaxed, pleased, bored, nervous, and peaceful, each test clip can output four decision values, which are then regarded as the emotion profile. Two profiles are fusioned to train SVMs. The final decision value is then extracted to draw DET curve. The experiment result shows that the proposed system has a good performance on music emotion recognition.

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主出版物標題2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs
出版狀態已出版 - 2013
事件2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan
持續時間: 29 10月 20131 11月 2013

出版系列

名字2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013

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???event.eventtypes.event.conference???2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
國家/地區Taiwan
城市Kaohsiung
期間29/10/131/11/13

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