A First Look at Generative Artificial Intelligence Based Music Therapy for Mental Disorders

Lin Shen, Haojie Zhang, Cuiping Zhu, Ruobing Li, Kun Qian*, Wei Meng, Fuze Tian*, Bin Hu*, Bjorn W. Schuller, Yoshiharu Yamamoto

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

Mental disorders show a rapid increase and cause considerable harm to individuals as well as the society in recent decade. Hence, mental disorders have become a serious public health challenge in nowadays society. Timely treatment of mental disorders plays a critical role for reducing the harm of mental illness to individuals and society. Music therapy is a type of non-pharmaceutical method in treating such mental disorders. However, conventional music therapy suffers from a number of issues resulting in a lack of popularity. Thanks to the rapid development of Artificial Intelligence (AI), especially the AI Generated Content (AIGC), it provides a chance to address these issues. Nevertheless, to the best of our knowledge, there is no work investigating music therapy from AIGC and closed-loop perspective. In this paper, we summarise some universal music therapy methods and discuss their shortages. Then, we indicate some AIGC techniques, especially the music generation, for their application in music therapy. Moreover, we present a closed-loop music therapy system and introduce its implementation details. Finally, we discuss some challenges in AIGC-based music therapy with proposing further research direction, and we suggest the potential of this system to become a consumer-grade product for treating mental disorders.

源语言英语
期刊IEEE Transactions on Consumer Electronics
DOI
出版状态已接受/待刊 - 2024

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引用此

Shen, L., Zhang, H., Zhu, C., Li, R., Qian, K., Meng, W., Tian, F., Hu, B., Schuller, B. W., & Yamamoto, Y. (已接受/印刷中). A First Look at Generative Artificial Intelligence Based Music Therapy for Mental Disorders. IEEE Transactions on Consumer Electronics. https://doi.org/10.1109/TCE.2024.3514633