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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs
StatePublished - 2013
Event2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan
Duration: 29 Oct 20131 Nov 2013

Publication series

Name2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013

Conference

Conference2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
Country/TerritoryTaiwan
CityKaohsiung
Period29/10/131/11/13

Keywords

  • happiness verification
  • Music emotion
  • support vector machine

Fingerprint

Dive into the research topics of 'Happiness detection in music using hierarchical SVMs with dual types of kernels'. Together they form a unique fingerprint.

Cite this