Research Article
Features, Models, and Applications of Deep Learning in Music Composition
Issue:
Volume 9, Issue 3, September 2025
Pages:
155-162
Received:
20 April 2025
Accepted:
12 June 2025
Published:
15 July 2025
DOI:
10.11648/j.ajist.20250903.11
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Views:
Abstract: Due to the swift advancement of artificial intelligence and deep learning technologies, computers are assuming an increasingly prominent role in the realm of music composition, thereby fueling innovations in techniques for music generation. Deep learning models such as RNNs, LSTMs, Transformers, and diffusion models have demonstrated outstanding performance in the music generation process, effectively handling temporal relationships, long-term dependencies, and complex structural issues in music. Transformers, with their self-attention mechanism, excel at capturing long-term dependencies and generating intricate melodies, while diffusion models exhibit significant advantages in audio quality, producing higher-fidelity and more natural audio. Despite these breakthroughs in generation quality and performance, challenges remain in areas such as efficiency, originality, and structural coherence. This research undertakes a comprehensive examination of the utilization of diverse and prevalent deep learning frameworks in music generation, emphasizing their respective advantages and constraints in managing temporal correlations, prolonged dependencies, and intricate structures. It aims to provide insights to address current challenges in efficiency and control capabilities. Additionally, the research explores the potential applications of these technologies in fields such as music education, therapy, and entertainment, offering theoretical and practical guidance for future music creation and applications. Furthermore, this study highlights the importance of addressing the limitations of current models, such as the computational intensity of Transformers and the slow generation speed of diffusion models, to pave the way for more efficient and creative music generation systems. Future work may focus on combining the strengths of different models to overcome these challenges and to foster greater originality and diversity in AI-generated music. By doing so, we aim to push the boundaries of what is possible in music creation, leveraging the power of AI to inspire new forms of artistic expression and enhance the creative process for musicians and composers alike.
Abstract: Due to the swift advancement of artificial intelligence and deep learning technologies, computers are assuming an increasingly prominent role in the realm of music composition, thereby fueling innovations in techniques for music generation. Deep learning models such as RNNs, LSTMs, Transformers, and diffusion models have demonstrated outstanding pe...
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