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Music emotion recognition using two level classification

Pouyanfar, S ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/IranianCIS.2014.6802519
  3. Abstract:
  4. Rapid growth of digital music data in the Internet during the recent years has led to increase of user demands for search based on different types of meta data. One kind of meta data that we focused in this paper is the emotion or mood of music. Music emotion recognition is a prevalent research topic today. We collected a database including 280 pieces of popular music with four basic emotions of Thayer's two Dimensional model. We used a two level classifier the process of which could be briefly summarized in three steps: 1) Extracting most suitable features from pieces of music in the database to describe each music song; 2) Applying feature selection approaches to decrease correlations between features; 3) Using SVM classifier in two level to train these features. Finally we increased accuracy rate from 72.14% with simple SVM to 87.27% with our hierarchical classifier
  5. Keywords:
  6. Feature selection ; Music emotion recognition ; Two level classiffication ; Feature extraction ; Intelligent systems ; Digital music data ; Hierarchical classifiers ; Music emotions ; Music information retrieval ; Research topics ; SVM classifiers ; Two dimensional model ; Classification (of information)
  7. Source: 2014 Iranian Conference on Intelligent Systems, ICIS 2014 ; Feb , 2014 ; 9781479933501
  8. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6802519&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6798982%29