In recent years, artificial intelligence (AI) has made remarkable strides in the field of music generation, revolutionizing the way music is created and applied across various domains. A comprehensive review of the latest research advancements in AI music generation has been conducted by researchers Yanxu Chen, Linshu Huang, and Tian Gou, shedding light on key technologies, models, datasets, evaluation methods, and practical applications.
The review presents a systematic framework that categorizes and compares different technological approaches in AI music generation. These approaches include symbolic generation, audio generation, and hybrid models. Symbolic generation involves creating music using musical notation, while audio generation focuses on producing raw audio waveforms. Hybrid models combine elements of both symbolic and audio generation to leverage the strengths of each approach. This comprehensive framework helps readers understand the full spectrum of technologies in the field, providing a clear and organized overview.
The researchers also offer an extensive survey of current literature, covering emerging topics such as multimodal datasets and emotion expression evaluation. Multimodal datasets integrate various types of data, such as audio, text, and images, to enhance the richness and diversity of music generation. Emotion expression evaluation focuses on assessing the ability of AI-generated music to convey specific emotions, a crucial aspect for creating emotionally resonant compositions. This broad reference provides valuable insights for researchers and practitioners working in related areas.
One of the key contributions of the review is its detailed analysis of the practical impact of AI music generation in various application domains. The study highlights the significance of real-time interaction and interdisciplinary applications, offering new perspectives and insights. Real-time interaction enables AI systems to generate music dynamically in response to user input or environmental stimuli, creating immersive and interactive experiences. Interdisciplinary applications explore the integration of AI music generation with other fields, such as gaming, film scoring, and therapeutic interventions, expanding the potential uses of this technology.
The review also summarizes the existing challenges and limitations of music quality evaluation methods. Evaluating the quality of AI-generated music is a complex task that involves assessing factors such as melodic coherence, harmonic structure, rhythmic accuracy, and overall aesthetic appeal. The researchers propose potential future research directions aimed at promoting the standardization and broader adoption of evaluation techniques. By addressing these challenges, the field can move towards more objective and reliable methods for assessing the quality of AI-generated music.
Overall, this comprehensive review serves as a valuable reference tool for researchers and practitioners in AI music generation. It not only outlines the current state of the art but also provides a roadmap for future research and development. By fostering innovation and collaboration, this review aims to advance the field of AI music generation, unlocking new possibilities for creativity and expression in the digital age.
