The Future of Healing Sounds: AI-Generated Music in Therapeutic Settings
Abstract
Abstract
The dramatic and recent developments in Generative Artificial Intelligence are appearing with more possibilities and affecting our lives in various areas. At the same time, integrating Generative AI Music Technology into the evolving practice of music therapy creates new channels for innovation. As a result, Generative AI Music Technology opens new possibilities for music therapy and its technological development regarding speed of creation and flexibility. This paper discusses the underlying logic and working principles of Generative AI Music, which is founded on Large Language Models (LLMS), Machine Learning (ML), Recurrent Neural Networks (RNN), Long and Short-Term Memory Networks (LSTM), and Transformer to arrange the music sequence and generate music. By analyzing the therapeutic effects of music therapy on post-stroke depression patients, the positive impact of Generative AI Music on music therapy is explored and presented. There is evidence that AI music generation technology can improve patients' therapeutic participation in music therapy and analyze the therapeutic needs of specific individuals to create personalized therapeutic music that is diverse and applicable. Challenges and constraints are outlined. In addition, the paper discusses the issues of identification and copyright attribution of works generated by existing AI technologies, as well as the ethical issues facing integrating AI music generation technologies into music therapy. In summary, the potential impact of AI music generation technologies on the future of music therapy and future research directions are proposed.
Keywords
Artificial Intelligence Music Generation, Music Therapy, Ethical, Ethics Copyright Issues
The Future of Healing Sounds: AI-Generated Music in Therapeutic Settings
CASB 117
Abstract
The dramatic and recent developments in Generative Artificial Intelligence are appearing with more possibilities and affecting our lives in various areas. At the same time, integrating Generative AI Music Technology into the evolving practice of music therapy creates new channels for innovation. As a result, Generative AI Music Technology opens new possibilities for music therapy and its technological development regarding speed of creation and flexibility. This paper discusses the underlying logic and working principles of Generative AI Music, which is founded on Large Language Models (LLMS), Machine Learning (ML), Recurrent Neural Networks (RNN), Long and Short-Term Memory Networks (LSTM), and Transformer to arrange the music sequence and generate music. By analyzing the therapeutic effects of music therapy on post-stroke depression patients, the positive impact of Generative AI Music on music therapy is explored and presented. There is evidence that AI music generation technology can improve patients' therapeutic participation in music therapy and analyze the therapeutic needs of specific individuals to create personalized therapeutic music that is diverse and applicable. Challenges and constraints are outlined. In addition, the paper discusses the issues of identification and copyright attribution of works generated by existing AI technologies, as well as the ethical issues facing integrating AI music generation technologies into music therapy. In summary, the potential impact of AI music generation technologies on the future of music therapy and future research directions are proposed.