Applications and Advances of Artificial Intelligence in Music Generation:A Review

TL;DR

This review comprehensively summarizes recent advances in AI-driven music generation, covering key technologies, datasets, evaluation methods, and practical applications, while highlighting challenges and future research directions.

Contribution

It provides a systematic categorization of AI music generation approaches, surveys current literature, and analyzes practical impacts and challenges in the field.

Findings

Comprehensive categorization of AI music generation technologies

Survey of emerging datasets and evaluation methods

Analysis of practical applications and future challenges

Abstract

In recent years, artificial intelligence (AI) has made significant progress in the field of music generation, driving innovation in music creation and applications. This paper provides a systematic review of the latest research advancements in AI music generation, covering key technologies, models, datasets, evaluation methods, and their practical applications across various fields. The main contributions of this review include: (1) presenting a comprehensive summary framework that systematically categorizes and compares different technological approaches, including symbolic generation, audio generation, and hybrid models, helping readers better understand the full spectrum of technologies in the field; (2) offering an extensive survey of current literature, covering emerging topics such as multimodal datasets and emotion expression evaluation, providing a broad reference for related research; (3) conducting a detailed analysis of the practical impact of AI music generation in various application domains, particularly in real-time interaction and interdisciplinary applications, offering new perspectives and insights; (4) summarizing the existing challenges and limitations of music quality evaluation methods and proposing potential future research directions, aiming to promote the standardization and broader adoption of evaluation techniques. Through these innovative summaries and analyses, this paper serves as a comprehensive reference tool for researchers and practitioners in AI music generation, while also outlining future directions for the field.