Make Your Favorite Music Curative: Music Style Transfer for Anxiety Reduction

Abstract

Anxiety is the most common mental problem that affects nearly 300 million individuals worldwide. The situation is even worse recently. In clinical practice, music therapy has been used for more than forty years because of its effectiveness and few side effects in emotion regulation. This paper proposes a novel style transfer model to generate the therapeutic music according to user's preference. It is widely recognized that the favorite music greatly increases the engagement of the user, hence results in much better curative effects. But in general, users can provide only one or several favorite songs, which are insufficient for the customization of therapeutic music. To address this difficulty, a new domain adaption algorithm that transfers the learning result for music genre classification to the music personalization, is designed. Targeting the joint minimization of the loss functions, three convolutional neural networks are utilized to generate the therapeutic music with only one labelled data of favorite song. The experiment on the anxiety suffers shows that the customized therapeutic music has achieved better and stable performance in anxiety reduction.