Abstract

Music has lyrics and audio. That‟s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. „ooh‟, „ah‟, „yeah‟, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%.

Original languageEnglish
Pages (from-to)1720-1730
Number of pages11
JournalInternational Journal of Electrical and Computer Engineering
Volume8
Issue number3
DOIs
Publication statusPublished - Jun 2018

Keywords

  • Audio features CBE Corpus based emotion Emotion Lyric features Music Classification

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