Music Emotion Recognition through Sparse Canonical Correlation Analysis

For centuries, music has been an important part of various cultures and a special language for humans to express their thoughts and emotions. Music emotion plays an important role in music retrieval, mood detection and other music-related applications. Music emotion recognition (MER) has become a research hotspot in the world. The traditional music emotion recognition ignores that the subject of emotions is human. Music acts on the brain to finally produce emotions. Therefore, this paper studies the mapping relationship between music features and EEG features. Through the sparse canonical correlation method, the music features are projected onto the EEG features to obtain the new music feature vectors containing EEG information. The support vector machine was used to train and test the new music feature vectors, and good recognition results were obtained in both the self-built database and the public database. The method proposed in this paper combines the advantages of EEG signals that can reflect the most intuitive and accurate emotional expression. At the same time, our method has good transferability. When the EEG samples are representative, the projection vector is universal and can be directly used in other music database.