Discovering Emotion Features in Symbolic Music
نویسندگان
چکیده
Current music recommender systems only use basic information for recommending music to its listeners. These usually include artist, album, genre, tempo and other song information. Online recommender systems would include ratings and annotation tags by other people as well. We propose a recommender system that recommends music depending on how the listener wants to feel while listening to the music. The user-specific model we use is derived by analyzing brain waves of the subject while he was actively listening to emotion-inducing music. The brain waves are analyzed in order to derive the emotional state of the listener for different segments of the music using an emotion spectral analysis method. The emotional state is used to label segments of music that are fed into a supervised machine learning technique to build an emotion model. This emotion model is used to identify the different music features that are important for recognizing specific emotional states.
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