Music Emotion Recognition Using Two Level Classification

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 metadata that we focused in this paper is the emotion or mood of music. Music emotion recognition is a prevalent research topictoday. We collected a database including 280 pieces of popular music with four basic emotions of Thayer's two Dimensionalmodel. We used a two level classifier the process of which couldbe briefly summarized in three steps: 1) Extracting most suitable features from pieces of music in the database to describe eachmusic song; 2) Applying feature selection approaches to decrease correlations between features; 3) Using SVM classifier in twolevel to train these features. Finally we increased accuracy rate from 72.14% with simple SVM to 87.27% with our hierarchical classifier.

کلیدواژه ها:

Music emotion recognition ، Feature extraction ، Two level classiffication ، Feature selection ، Music information retrieval