THE APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) IN CONTEMPORARY MUSIC PRODUCTION AND PERFORMANCE
Composer Xuan Hieu (Nong Van Hieu)
Lecturer, Faculty of Arts – Van Hien University
Email: hieunv@vhu.edu.vn
In the context of the Fourth Industrial Revolution, artificial intelligence (AI) has emerged as one of the most influential technologies shaping contemporary social life. Beyond its traditional applications in science and engineering, AI has increasingly penetrated creative industries, including music. The involvement of AI in music composition, production, and performance is not merely a technical innovation; rather, it is progressively transforming creative structures, artistic labor practices, and the relationships among artists, musical works, and audiences.
Music, traditionally regarded as an art form deeply rooted in human emotion and aesthetic thinking, has long been influenced by advances in science and technology. The history of music demonstrates that each technological breakthrough has brought significant changes to musical language and creative methods. From the invention of musical notation and mechanical instruments to sound recording technologies and digital music production software, humans have continually expanded their expressive capacities through technology. However, the emergence of AI marks a distinct phase, as machines are no longer limited to supportive roles but are now capable of directly participating in the generation of musical content.
Early research into computer-generated music dates back to the mid-twentieth century, most notably with Illiac Suite (1956) by Lejaren Hiller and Leonard Isaacson. Based on the rules of traditional music theory, this work represented a foundational experiment that raised the question of whether machines could engage in artistic creativity. In subsequent decades, algorithmic music and rule-based compositional models continued to develop. A prominent example is David Cope’s Experiments in Musical Intelligence (EMI) project in the 1980s, which enabled computers to analyze the styles of classical composers and generate new works exhibiting similar stylistic characteristics.
In the twenty-first century, the rapid advancement of machine learning, particularly deep learning, has brought about a fundamental shift in AI-driven music. Rather than merely imitating predefined rules, contemporary AI systems can learn from large datasets, identify complex musical patterns, and generate new structures based on probabilistic models and internal relationships among musical elements. Projects such as Google’s Magenta, OpenAI’s MuseNet and Jukedeck, AIVA, and Suno have moved AI music beyond academic research into widespread practical use in music composition and production.
In the field of composition, AI systems today are capable of generating complete musical works with minimal human intervention. Trained on thousands or even millions of musical pieces, these systems learn how to organize melody, harmony, rhythm, and form. Some platforms allow users to select styles, moods, or genres, after which the AI automatically produces corresponding musical outputs. The song Break Free by Taryn Southern—where the instrumental track was composed using Amper AI while the lyrics and vocal melody were created by a human—illustrates how AI can function as a creative partner, assisting humans in idea development and significantly reducing composition time.
Beyond composition, AI is widely applied in music production and audio processing. Tools for beat generation, automatic arrangement, virtual instrument simulation, and digital signal processing are becoming increasingly prevalent. By analyzing frequency spectra, dynamics, and sound structures, AI can effectively support mixing and mastering processes. Platforms such as LANDR and iZotope Ozone employ AI to analyze audio tracks and suggest adjustments in tonal balance, compression, and loudness, thereby enhancing production quality and reducing reliance on individual experience. This is particularly significant in the context of the growing trend toward independent and home-based music production.
AI’s influence extends beyond the studio into the realm of musical performance. AI systems are capable of generating real-time audio and visual effects that interact directly with performers and audiences. The work of artist Holly Herndon, who employs an AI system named “Spawn,” exemplifies the integration of human performance and artificial intelligence, where AI not only reproduces sound but also actively participates in artistic expression. Additionally, the emergence of virtual performers such as Hatsune Miku has challenged traditional notions of musicianship and performance, raising new questions regarding identity, presence, and aesthetic value in performing arts.
AI also plays an increasingly important role in analyzing audience feedback. By processing data from social media, online platforms, and behavioral analytics tools, AI helps artists and producers gain deeper insights into audience preferences, emotions, and responses. This enables flexible adjustments to content and performance formats, fostering more interactive experiences. However, excessive reliance on data and algorithms carries the risk of homogenizing tastes, potentially leading to stylistic repetition and a reduction in aesthetic diversity.
Alongside its clear benefits, the application of AI in music raises significant challenges, particularly concerning copyright and intellectual property rights. As AI systems learn from existing works to generate new content, the boundary between creation and imitation becomes blurred. Current legal frameworks have yet to keep pace with technological developments, resulting in ongoing debates about authorship and ownership of AI-generated works. Moreover, while AI can produce structurally coherent music, emotional depth, humanistic values, and artistic messages remain closely tied to lived experience and human consciousness—qualities that machines struggle to replicate.
In music education, AI offers promising opportunities while simultaneously necessitating adjustments in teaching methodologies. AI can assist in musical analysis, personalize learning processes, and help students experiment with compositional ideas. However, without proper guidance, overreliance on AI may diminish independent thinking and fundamental skill development. Therefore, music education should approach AI as a supportive tool rather than a replacement for traditional artistic training.
In conclusion, artificial intelligence is gradually becoming an integral component of the contemporary musical ecosystem. AI expands creative possibilities, enhances production efficiency, and introduces innovative performance forms. Nevertheless, the core value of music remains rooted in human creativity—shaped by emotion, aesthetic judgment, and cultural responsibility. In this context, the task of artists, researchers, and music educators is not to reject or wholly depend on AI, but to master the technology, harness its positive potential, and simultaneously preserve the identity and humanistic values of musical art.