Human-centric music medical therapy exploration system

Music emotion analytic is useful for many human-centric applications such as medical intervention. Existing studies have shown that music is a low risk, adjunctive and therapeutic medical intervention. However, there is little existing research about the types of music with identified emotions that have the most effect for different medical applications. We would like to discover various music emotions through machine learning analytic so as to identify modelsof how music conveys emotion features, and determine its effectiveness for medical intervention and treatment. We are developing a Human-centric Music Medical Therapy Exploration System which could recognize music emotion features from Chinese Folk Music Library, and recommend suitable music to playback for medical intervention and treatment. Our networked system is based on Support Vector Machine(SVM) algorithm to construct the models for music emotion recognition and information retrieval. Our main contributions are as follows: Firstly, we built up the Chinese folk music emotions and features library; secondly, we conducted evaluation and comparison with other algorithms such as Back Propagation(BP) and Linear Regression to set up the model construction for music emotion recognition and proved that SVM has the best performance; lastly, we integrated blood pressure and heartbeat data analytic into our system to visualize the emotion fluctuation in different music affection and make recommendation for suitable humancentric music medical therapy for hypertensive patients.