How AI Is Being Used to Compose Classical Music

Artificial intelligence is no longer confined to science labs or technology companies. It is increasingly becoming part of the creative process, including one of the world's oldest and most revered art forms: classical music. From completing unfinished masterpieces to helping composers generate new ideas, AI is changing how music is created, studied and performed. While it is unlikely to replace human creativity, it is opening up exciting possibilities for collaboration between technology and artistic imagination.

Artificial Intelligence Meets Classical Music

For centuries, classical music has evolved alongside technological innovation. The invention of the piano transformed keyboard writing. Recording technology changed how audiences experienced concerts. Computers revolutionised music notation, editing and production.

Artificial intelligence represents the latest chapter in that evolution.

Unlike traditional software, which follows a fixed set of instructions, AI systems learn by analysing enormous amounts of data. In music, this often means studying thousands of scores from different composers, periods and styles. By recognising patterns in melody, harmony, rhythm, orchestration and musical structure, AI models can generate original musical ideas that resemble the works they have analysed.

This does not mean AI "understands" music in the human sense. It has no emotions, memories or artistic intentions. Instead, it predicts which musical events are likely to follow one another based on statistical relationships learned from existing works. Yet the results can often be remarkably convincing, raising fascinating questions about creativity, originality and the future of composition.

For today's composers, AI is increasingly viewed not as a competitor, but as another tool in the creative process, much like notation software, digital audio workstations or virtual instruments.

The Early Days of AI Composition

Although generative AI has become a global talking point in recent years, the idea of computers composing music is far from new.

Researchers began experimenting with algorithmic composition as early as the 1950s, using mathematical rules to generate simple musical patterns. These early systems produced music that was often technically correct but lacked the sophistication and expressive qualities associated with great composers.

A major breakthrough came in the 1980s with the work of American composer and computer scientist David Cope. His project, Experiments in Musical Intelligence (EMI), analysed the works of composers including Bach, Mozart and Chopin before generating new compositions in their respective styles.

Cope's work divided opinion. Some listeners were astonished by how convincing the music sounded, while others questioned whether stylistic imitation could ever be considered genuine creativity. Regardless of the debate, EMI demonstrated that computers could produce music sophisticated enough to challenge assumptions about authorship and originality. Many researchers regard it as one of the foundations upon which today's AI music systems have been built.

How AI Learns to Compose Music

Modern AI systems rely on machine learning, a branch of artificial intelligence that enables computers to recognise patterns within large datasets.

Thousands of musical scores are first converted into digital formats such as MIDI or MusicXML, allowing algorithms to examine every aspect of a composition. The AI studies relationships between notes, harmonic progressions, rhythmic patterns, orchestration, dynamics and formal structures.

Rather than memorising complete pieces, the system learns probabilities. For example, after analysing hundreds of string quartets by Haydn, Mozart and Beethoven, it begins to understand which harmonic progressions commonly follow others, how themes are developed and how musical phrases are structured.

When asked to compose, the AI predicts what notes, chords or rhythms are most likely to follow, gradually constructing entirely new passages of music.

Recent advances in deep learning and transformer-based models have dramatically improved these capabilities. Some systems can now generate convincing piano works, chamber music or orchestral textures from a short musical prompt or even a written description.

AI Projects Shaping Music Composition

Several research projects have helped define the current landscape of AI-generated music.

Google's Magenta is among the most influential. Built as an open-source research project, Magenta explores how machine learning can support artistic creativity rather than replace it. Its tools allow musicians to generate melodies, harmonise themes, continue musical phrases and experiment with new compositional ideas. Many researchers and educators have used Magenta to demonstrate how AI can function as a collaborative creative partner rather than an autonomous composer.

Google has also introduced MusicLM, an experimental system capable of generating music directly from written descriptions. A prompt such as "a lyrical string quartet with a romantic character" or "an energetic orchestral overture inspired by late nineteenth-century composers" can produce surprisingly coherent musical excerpts. While these systems remain research projects, they demonstrate how rapidly AI-assisted composition is evolving.

Among commercial platforms, AIVA (Artificial Intelligence Virtual Artist) has become one of the best-known examples of AI-assisted composition. Originally developed to generate orchestral music for films, advertisements and video games, AIVA enables composers to create drafts that can later be refined and personalised. Rather than replacing professional composers, it is commonly used to accelerate the early stages of the creative process.

OpenAI also contributed to this field through MuseNet, a research model capable of generating music in a variety of styles, including classical, jazz and popular music. Although MuseNet is no longer actively developed, it demonstrated how large-scale neural networks could generate increasingly sophisticated musical structures and inspired further research into generative music systems.

Completing Unfinished Masterpieces

Perhaps the most widely publicised use of AI in classical music has been its role in completing unfinished works by famous composers.

The best-known example is Beethoven's unfinished Symphony No. 10. For centuries, only sketches and fragments survived, leaving scholars to speculate about how the work might have developed.

To mark the composer's 250th anniversary, an international team of musicologists, composers and AI researchers collaborated on the Beethoven X project. The AI analysed Beethoven's surviving sketches alongside his complete body of work, generating possible continuations that researchers and musicians then evaluated and refined before producing a performable score.

The project attracted worldwide attention. Supporters viewed it as an extraordinary demonstration of collaboration between technology and musicology, while critics argued that no machine could truly recreate Beethoven's artistic vision. Even those involved in the project stressed that the result should be regarded as an informed interpretation rather than an authentic completion.

The project nevertheless demonstrated one of AI's greatest strengths. Rather than replacing scholarly expertise, it provided researchers with new ways of exploring historical possibilities that might otherwise have remained purely theoretical.

Similar techniques have since been explored when studying incomplete works by other composers, including Franz Schubert and Gustav Mahler, although these projects remain relatively rare.

AI as a Creative Assistant

For most contemporary composers, AI is not a substitute for creativity but a source of inspiration.

Instead of asking software to compose an entire symphony, many musicians use AI to generate starting points that can later be refined through traditional compositional techniques.

AI can assist by:

• suggesting melodic ideas
• generating harmonic progressions
• producing contrapuntal textures
• creating rhythmic variations
• exploring orchestration possibilities
• developing thematic transformations
• proposing alternative endings or transitions

This process resembles working with a creative assistant who never tires of offering fresh ideas. The composer remains responsible for selecting, adapting and shaping the material into a coherent artistic statement.

Many composers report that AI is particularly valuable during the earliest stages of composition, when overcoming the blank page can often be the greatest challenge. By generating multiple possibilities within seconds, AI allows musicians to experiment more freely before making their own artistic decisions.

Increasingly, these capabilities are finding their way into the software composers already use every day. AI-assisted features are beginning to appear in music notation programs, digital audio workstations and composition tools, helping musicians harmonise melodies, organise orchestral textures, identify notation errors and streamline repetitive tasks. Rather than changing the essence of composition, these developments allow composers to spend more time focusing on interpretation, expression and musical storytelling.

AI in Film, Television and Video Game Music

Beyond the concert hall, AI is already becoming a practical tool for composers working in film, television and video games.

These industries often demand large volumes of music within tight production schedules. AI can quickly generate sketches, textures or harmonic ideas that composers later refine into polished scores. Rather than beginning every project with a blank manuscript, musicians can explore multiple musical directions in a matter of minutes.

For example, a composer might ask an AI system to generate an orchestral cue with a sense of mystery or a pastoral theme for strings and woodwinds. The generated material serves as a starting point rather than the finished product. Human composers still shape the pacing, orchestration and emotional arc to suit the story on screen.

This collaborative workflow is becoming increasingly common, particularly for independent productions and game developers with limited budgets. AI helps speed up routine tasks, allowing composers to devote more attention to the artistic decisions that audiences ultimately notice.

AI in Music Education

Artificial intelligence is also changing how composition is taught.

Students studying harmony, counterpoint and orchestration often benefit from immediate feedback, something AI can increasingly provide. Modern educational tools can analyse student compositions, identify harmonic inconsistencies, suggest alternative voice leading or explain why a particular chord progression may sound more convincing.

AI can also generate examples in the styles of different composers, helping students compare how Bach approached counterpoint, how Mozart structured sonata form or how Debussy used harmony and colour.

For aspiring composers, these systems offer opportunities to experiment without fear of making mistakes. Students can instantly hear different orchestrations, compare multiple harmonisations or explore variations on their own musical ideas.

None of this replaces experienced teachers, whose guidance remains essential for developing artistic judgement and musical expression. Instead, AI offers another educational resource that can make advanced compositional techniques more accessible to learners around the world.

Can AI Write Like Mozart or Bach?

One of the most fascinating questions surrounding AI music is whether it can genuinely compose like the great masters.

The answer depends on what we mean by "like".

Modern AI systems can imitate the musical language of composers with remarkable accuracy. Listeners may hear convincing Bach-style fugues, Mozart-inspired piano sonatas or Beethoven-like string quartets. The harmony, phrasing and formal structure often sound authentic enough that casual listeners may struggle to distinguish them from historical works.

However, imitation is not the same as originality.

Mozart did not merely follow established conventions. He expanded them. Beethoven transformed the symphony. Stravinsky challenged audiences with entirely new musical languages. Their innovations emerged from personal experience, cultural influences and artistic ambition rather than statistical analysis.

AI excels at recognising patterns that already exist. It is far less capable of deliberately breaking those patterns in ways that redefine an art form.

As a result, AI is currently better viewed as an exceptional stylistic imitator than a revolutionary composer.

The Human Element

Perhaps the greatest difference between AI and human composers lies in intention.

When Gustav Mahler wrote his symphonies, he was responding to profound personal questions about life, death and spirituality. Dmitri Shostakovich composed under political oppression. Benjamin Britten's music reflected his social concerns and relationships.

Artificial intelligence experiences none of these things.

It does not feel grief, joy, hope, anxiety or love. It does not attend concerts, observe the world or develop lifelong artistic convictions. Every musical decision it makes is based on patterns extracted from data rather than lived experience.

This distinction helps explain why many listeners continue to value human composition so highly. Great music is not simply an arrangement of pleasing sounds. It is often an expression of personality, culture and emotion.

Even if AI eventually produces technically flawless symphonies, many people will still seek music created by individuals with something meaningful to communicate.

Copyright, Ethics and Authorship

As AI-generated music becomes more sophisticated, important legal and ethical questions are emerging.

Many AI systems are trained using vast collections of existing music. This raises concerns about copyright and fair use. Should composers whose works contribute to AI training receive compensation? How much influence from existing music is acceptable before a new composition becomes derivative?

Questions also arise over authorship.

If a composer provides a prompt, edits the AI's output and orchestrates the final score, who should be credited as the composer? The software developer? The user? Both?

Copyright laws differ across countries, and many legal systems are still adapting to AI-generated creative works. Courts and policymakers continue to debate how intellectual property should apply to music created with artificial intelligence.

There is also concern that AI could flood streaming platforms with inexpensive background music, making it more difficult for emerging composers to earn recognition and sustainable incomes.

At the same time, supporters argue that every major technological innovation has prompted similar debates. Recording technology, synthesisers and digital sampling all raised concerns before becoming accepted parts of musical life.

Will AI Replace Classical Composers?

Despite rapid advances, most musicians believe the answer is no.

Throughout history, technological innovations have changed how music is created without replacing creativity itself.

The piano expanded what composers could write. Recording transformed performance. Music notation software replaced handwritten manuscripts for many professionals. Digital audio workstations revolutionised film scoring.

None of these developments eliminated the need for composers.

AI is likely to follow the same path.

Its greatest strength lies in assisting with repetitive or exploratory tasks. It can rapidly generate ideas, analyse scores and automate aspects of the compositional process. Yet deciding what music should ultimately say, how it should develop and why it matters remains a profoundly human responsibility.

Indeed, many professional composers already compare AI to an assistant rather than an author. It can make suggestions, but it cannot decide which ideas carry artistic meaning.

The Future of AI in Classical Music

Artificial intelligence is still in its early stages, and its influence on classical music will almost certainly continue to grow.

Researchers are developing systems capable of producing increasingly sophisticated orchestral writing, responding to performers in real time and adapting music dynamically during live performances. Future AI tools may help composers experiment with new harmonic languages, simulate orchestral rehearsals or create interactive works that evolve according to audience participation.

At the same time, conservatoires, orchestras and publishers will continue debating how AI should be used responsibly. Questions surrounding transparency, copyright and artistic integrity are unlikely to disappear.

What seems increasingly clear is that the future is unlikely to be defined by a choice between human composers and artificial intelligence.

Instead, the most exciting possibilities lie in collaboration.

Throughout history, composers have embraced new technologies that expanded their creative horizons. Artificial intelligence may become another instrument in that tradition, offering fresh ways to explore musical ideas while leaving imagination, judgement and emotional expression firmly in human hands.