Make a song curative: A spatio-temporal therapeutic music transfer model for anxiety reduction

Abstract

Anxiety is a prevalent mental health issue that affects both individuals and society. Music therapy has been found effective in reducing anxiety in clinical trials. However, the available music pieces for therapy are limited and may not meet the requirements of individuals from diverse backgrounds. To overcome this limitation, we propose a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) for anxiety reduction. The StTMTM can transfer a piece of music to a new therapeutic music piece that satisfies both the criteria for music therapy and the requirements of individuals. Our approach involves first developing a music feature extraction network that can extract spatio-temporal music features. We then train the network on different genres and transfer the learning results from genre classification to music transfer for anxiety reduction. We also propose a deep learning-based music transfer network with melody and accompaniment encoders and a newly designed loss function to modify a piece of music to be therapeutic. Experimental results on 92 subjects demonstrate that the transferred therapeutic music produced by the StTMTM exhibits exceptional performance in anxiety reduction. Overall, our model has the potential to make music therapy more accessible and effective for individuals with diverse backgrounds and needs.