SleepGAN: Towards Personalized Sleep Therapy Music

Sleep deficiency and disorders are one of the most unsolved public health challenges of modern times. Music therapy is a promising approach, offering a cheap and non-invasive solution to improve sleep quality. However, the choice of therapeutic sleep music is highly limited for users because such music needs to be specially chosen and made by sleep therapists. It could potentially lead to the inefficiency of music therapy if users get bored after listening to the same set of music repeatedly. In this paper, we take the first step towards generating personalized sleep therapy music. Firstly, through an in-depth feature analysis, we investigate the importance of various musical and acoustic features of therapy music. Grounded on our findings, we design a style transfer framework called SleepGAN which induces therapeutic features into music from different genres. We show that, compared to baselines, the music generated by SleepGAN has a higher similarity to the sleep music designed by experts.