Artificial Intelligence-Based Music Therapy Technology and Applications

Ruoting Liang

doi:10.54254/2755-2721/2025.PO24725

Applied and Computational EngineeringOpen access

Artificial Intelligence-Based Music Therapy Technology and Applications

Research Article

Open Access

Artificial Intelligence-Based Music Therapy Technology and Applications

Ruoting Liang ^1*

¹ North University of China, Taiyuan, Shanxi, China

^*Corresponding author: 2488839062@qq.com

Published on 4 July 2025

ACE Vol.174

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-235-5

ISBN (Online): 978-1-80590-236-2

Download Cover

Abstract

Due to its non-pharmacological nature and low cost, music therapy has gained increasing attention in the fields of medicine and mental health and has been applied in the treatment of various diseases. However, traditional music therapy is limited by the lack of standardized treatment duration and frequency, as well as its inability to meet individual patient needs, hindering further development and broader application. This paper reviews relevant studies, highlighting the therapeutic effects of current music therapy methods on various diseases, and explores the application and efficacy of artificial intelligence (AI) technologies such as Random Forest (RF), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) in supporting music therapy. The results show that music therapy can help improve patients’ negative emotions and alleviate symptoms, while AI technologies can enhance and optimize traditional music therapy in areas such as music selection and treatment plan formulation, leading to more personalized and effective outcomes. In conclusion, music therapy demonstrates potential in clinical applications, and integrating AI technologies may offer more individualized and effective treatment plans. Future research and practice in this area should be further strengthened.

Keywords:

Music therapy, clinical application of music therapy, neural networks, machine learning, deep learning

View PDF

References

[1]. Behzad, A. , Feldmann-Schulz, C. , Lenz, B. , Clarkson, L. , Ludwig, C. , & Luttenberger, K. , et al. (2024). Taketina music therapy for outpatient treatment of depression: study protocol for a randomized clinical trial. Journal of Clinical Medicine, 13(9), 13.

[2]. Jaschke, A. C. , Howlin, C. , Pool, J. , Greenberg, Y. D. , Atkinson, R. , & Kovalova, A. , et al. (2024). Study protocol of a randomized control trial on the effectiveness of improvisational music therapy for autistic children. BMC Psychiatry, 24(1).

[3]. A, T. R. , D, J. P. R. D. C. A. , B, E. C. , C, V. B. , C, N. B. , & B, F. X. V. , et al. A randomized controlled trial of 25 sessions comparing music therapy and music listening for children with autism spectrum disorder. Psychiatry Research, 293.

[4]. Johnston, D. , Egermann, H. , Kearney, G. , & Särkämö, Teppo. (2018). Innovative computer technology in music based interventions for individuals with autism - moving beyond traditional interactive music therapy techniques. Cogent Psychology, 5(1), 1-18.

[5]. Boussaid, S. , Majdouba, M. B. , Jriri, S. , Abbes, M. , Jammali, S. , & Ajlani, H. , et al. (2020). Fri0618-hpreffects of music therapy on pain, anxiety, and vital signs in chronic inflammatory rheumatic diseases patients during biological drugs infusion. Annals of the Rheumatic Diseases, 79(Sup1), 1.

[6]. Colin, C., Prince, V., Bensoussan, J. L., & Picot, M. C. (2023). Music therapy for health workers to reduce stress, mental workload and anxiety: a systematic review. Journal of Public Health, 45(3), e532-e541.

[7]. Rajendran, T. (2022). Addressing the need for personalizing music therapy in integrative oncology. Journal of Integrative Medicine, 20(4), 281-283.

[8]. Su, Y., Liu, Y., Xiao, Y., Ma, J., & Li, D. (2024). A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications. Frontiers in Neuroscience, 18, 1400444.

[9]. Nunes, I. B., de Santana, M. A., Charron, N., Silva, H. S. E., de Lima Simões, C. M., Lins, C., ... & dos Santos, W. P. (2024). Automatic identification of preferred music genres: an exploratory machine learning approach to support personalized music therapy. Multimedia Tools and Applications, 83(35), 82515-82531.

[10]. Chen, J., Pan, F., Zhong, P., He, T., Qi, L., Lu, J., ... & Zheng, Y. (2020). An automatic method to develop music with music segment and long short term memory for tinnitus music therapy. IEEE Access, 8, 141860-141871.

[11]. Wang, Z., Guan, X., Li, E., & Dong, B. (2024). A study on music therapy aimed at psychological trauma recovery for bereaved families driven by artificial intelligence. Frontiers in Psychology, 15, 1436324.

[12]. Rahman, J. S., Gedeon, T., Caldwell, S., Jones, R., & Jin, Z. (2021). Towards effective music therapy for mental health care using machine learning tools: human affective reasoning and music genres. Journal of Artificial Intelligence and Soft Computing Research, 11(1), 5-20.

[13]. Choi, S., Park, J. I., Hong, C. H., Park, S. G., & Park, S. C. (2024). Accelerated construction of stress relief music datasets using CNN and the Mel-scaled spectrogram. PloS one, 19(5), e0300607.

[14]. Chen, J., Han, J., Su, P., & Zhou, G. (2025). Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN–TCN Architecture with Integrated Entropy Regularization and Pooling. Entropy, 27(3), 317.

[15]. Zhang, Y., Zhang, C., Cheng, L., & Qi, M. (2022). The use of deep learning-based gesture interactive robot in the treatment of autistic children under music perception education. Frontiers in Psychology, 13, 762701.

References

[7]. Rajendran, T. (2022). Addressing the need for personalizing music therapy in integrative oncology. Journal of Integrative Medicine, 20(4), 281-283.

Cite this article

Liang,R. (2025). Artificial Intelligence-Based Music Therapy Technology and Applications. Applied and Computational Engineering,174,10-17.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Data Visualization Methods for Evaluatio

ISBN: 978-1-80590-235-5(Print) / 978-1-80590-236-2(Online)

Editor: Marwan Omar, Elisavet Andrikopoulou

Conference website: https://2025.confcds.org/portsmouth.html

Conference date: 30 July 2025

Series: Applied and Computational Engineering

Volume number: Vol.174

ISSN: 2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).