Music Recommendation System based on Context and EEG Data

An effective music recommendation can reduce the effort given by a music listener in choosing a piece of music to be heard. Music recommendations can not only be obtained by similar genre or audio similarity, because the music chosen by the music listeners can be different in different contexts. In this research, a case base for music recommendation system was developed based on the music listeners context in order to make it easier to choose music that suits the current situation. The context used in this study is the personal context of the music listeners consisting of age, gender, emotional states, favorite activities, and musical preferences by genre. An emotional state often called a mood will be detected using brainwave sensors. Brainwaves or Electro Encephalogram (EEG) will be classified based on the emotional state of the listeners and the Case based reasoning (CBR) method used to determine music recommendations based on the context of the listener and EEG data. The system tested to 20 participants and obtained music recommendation accuracy value of 64.5 %.