Style learning and musical mimicry in Artificial Intelligence: modern approaches

Abstract

This article comprehensively examines the application of artificial intelligence technologies in musical style learning and imitation, analyzing both theoretical foundations and practical implementations. The research explores how deep learning models like VQ-VAE, Transformers, LSTM, and GANs are employed in music generation processes, with detailed discussion of style transfer techniques and hierarchical music modeling. The methodology involves a thorough literature review tracing the historical development from early statistical models and rule-based systems to modern deep learning approaches. Special attention is given to OpenAI's Jukebox as a case study, illustrating how its three-level hierarchical VQ-VAE architecture and Transformer-based prior models effectively capture both structural elements and timbral details of music across multiple temporal scales. Key findings demonstrate that modern AI systems can learn various aspects of musical style, from harmonic structures to melodic patterns, while enabling conditional generation based on artist, genre, or lyrics. The research highlights the progression from simple Markov chain models to sophisticated architectures capable of producing high-quality musical output that mimics specific artists' styles or musical genres. The article also addresses crucial ethical considerations around copyright, authenticity, and cultural representation, while exploring diverse applications spanning music education, therapeutic uses, experimental art, and personalized content creation. The conclusion suggests that AI-based music generation will continue to evolve with increased computational capacity, presenting new opportunities for creative expression while requiring thoughtful engagement with ethical and cultural dimensions of musical creation. The interdisciplinary nature of this field is emphasized, noting how it blurs boundaries between music theory, cognitive science, machine learning, and philosophy, ultimately raising profound questions about the nature of musical expression and creativity in the human-AI collaborative future.