Music and artificial intelligence
Music and artificial intelligence is the development of music software programs that use AI to generate music. As with applications in other fields, AI in music also simulates mental tasks. A prominent feature is the capability of an AI algorithm to learn based on past data, such as in computer accompaniment technology, wherein the AI is capable of listening to a human performer and performing accompaniment. Artificial intelligence also drives interactive composition technology, wherein a computer composes music in response to a live performance. There are other AI applications in music that cover not only music composition, production, and performance but also how music is marketed and consumed. Several music player programs have also been developed to use voice recognition and natural language processing technology for music voice control. Current research includes the application of AI in music composition, performance, theory and digital sound processing. Composers/artists like Jennifer Walshe or Holly Herndon have been exploring aspects of music AI for years in their performances and musical works. Another original approach of humans “imitating AI” can be found in the 43-hour sound installation String Quartet by Georges Lentz.
20th century art historian Erwin Panofsky proposed that in all art, there existed three levels of meaning: primary meaning, or the natural subject; secondary meaning, or the conventional subject; and tertiary meaning, the intrinsic content of the subject. AI music explores the foremost of these, creating music without the "intention" that is usually behind it, leaving composers who listen to machine-generated pieces feeling unsettled by the lack of apparent meaning.
History
In the 1950s and the 1960s, music made by artificial intelligence was not fully original, but generated from templates that people had already defined and given to the AI, with this being known as rule-based systems. As time passed, computers became more powerful, which allowed machine learning and artificial neural networks to help in the music industry by giving AI large amounts of data to learn how music is made instead of predefined templates. By the early 2000s, more advancements in artificial intelligence had been made, with generative adversarial networks and deep learning being used to help AI compose more original music that is more complex and varied than possible before. Notable AI-driven projects, such as OpenAI’s MuseNet and Google’s Magenta, have demonstrated AI’s ability to generate compositions that mimic various musical styles.Timeline
Artificial intelligence finds its beginnings in music with the transcription problem: accurately recording a performance into musical notation as it is played. Père Engramelle's schematic of a "piano roll", a mode of automatically recording note timing and duration in a way which could be easily transcribed to proper musical notation by hand, was first implemented by German engineers J.F. Unger and J. Hohlfield in 1952.In 1957, the ILLIAC I produced the "Illiac Suite for String Quartet", a completely computer-generated piece of music. The computer was programmed to accomplish this by composer Leonard Isaacson and mathematician Lejaren Hiller.In 1960, Russian researcher Rudolf Zaripov published the first worldwide paper on algorithmic music composition using the Ural-1 computer.
In 1965, inventor Ray Kurzweil developed software capable of recognizing musical patterns and synthesizing new compositions from them. The computer first appeared on the quiz show I've Got a Secret that same year.
By 1983, Yamaha Corporation's Kansei Music System had gained momentum, and a paper was published on its development in 1989. The software utilized music information processing and artificial intelligence techniques to essentially solve the transcription problem for simpler melodies, although higher-level melodies and musical complexities are regarded even today as difficult deep-learning tasks, and near-perfect transcription is still a subject of research.
In 1997, an artificial intelligence program named Experiments in Musical Intelligence appeared to outperform a human composer at the task of composing a piece of music to imitate the style of Bach. EMI would later become the basis for a more sophisticated algorithm called Emily Howell, named for its creator.
In 2002, the music research team at the Sony Computer Science Laboratory in Paris, led by French composer and scientist François Pachet, designed the Continuator, an algorithm uniquely capable of resuming a composition after a live musician stopped.
Emily Howell would continue to make advancements in musical artificial intelligence, publishing her first album From Darkness, Light in 2009. Since then, many more pieces by artificial intelligence and various groups have been published.
In 2010, Iamus became the first AI to produce a fragment of original contemporary classical music, in its own style: "Iamus' Opus 1". Located at the Universidad de Malága in Spain, the computer can generate a fully original piece in a variety of musical styles. In August 2019, a large dataset consisting of 12,197 MIDI songs, each with their lyrics and melodies, was created to investigate the feasibility of neural melody generation from lyrics using a deep conditional LSTM-GAN method.
With progress in generative AI, models capable of creating complete musical compositions from a simple text description have begun to emerge. Two notable web applications in this field are Suno AI, launched in December 2023, and Udio, which followed in April 2024.
In November 2025 the AI generated song "Walk My Walk" presented as being by Breaking Rust topped the Billboard Country Digital Song Sales chart. The same year, AI band The Velvet Sundown attracted one million listeners on Spotify.
Streaming service Deezer started tagging AI generated songs and excluding them from its editorialized playlists. Their tool builds on former published research work on the nature of AI music's artefacts. In November 2025, the service claimed that 50,000 AI generated songs were uploaded daily, about a third of total uploads.
Anticipating the problem of humans not being able to find human music among the millions of tracks created using these AI tools, and understanding the limitations on tagging generative AI songs, Humanable launched in September of 2024 allowing songwriters and artists to declare under oath the 100% humanity of their work. The certified song can then display the H-Pick certification mark next to the certified song title in marketing the music, including on DSPs and other platforms that adopt the Humanable standard. The program drew media attention, including a televised segment in which the Humanable founder discussed it’s mission and certification process. https://www.youtube.com/watch?v=r4DH2Zuq4r4