Key points are not available for this paper at this time.
As a matter of fact, composing music based on machine leaning has been a common trend contemporarily. This study delves into the emerging field of AI music composition, tracing its development from early stage to contemporary. Moreover, significant models such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and MusicVAE are examined in depth. This study also explores a wide range of applications of AI music, including its role in areas such as education and therapy. Despite these advances, the paper still highlights the limitations of AI music, particularly its struggles to match human creativity and emotional depth. In addition, the paper discusses the ethical and social issues surrounding AI music, such as copyright disputes and employment implications. Looking forward, the future of AI music is the development of intelligent creative tools, personalized music recommendations, and therapeutic interventions. This research not only provides a comprehensive overview of AI music but also underscores its potential to transform how one creates, consume, and interact with music in the digital age.
Zhen Chen (Fri,) studied this question.