Music software utilizes artificial intelligence (AI) to generate, classify, or recommend music. Similar to its applications in other fields, AI in music simulates complex human cognitive processes.

A prominent feature is the capability of an AI algorithm to learn from historical data, such as in computer accompaniment technology, where the AI listens to a human performer and accompanies them in real-time. AI also drives interactive composition technologies that respond dynamically to live performances. Modern applications expand across the entire industry lifecycle, transforming music composition, production, and performance, as well as how music is marketed and consumed. Furthermore, conversational agents leverage voice recognition and natural language processing to enable voice-controlled music playback, bridging current research in music theory with advanced digital sound processing.

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. By the early 2000s, more advancements in artificial intelligence had been made, with generative adversarial networks (GANs) 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.

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.

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 (Illinois Automatic Computer) 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 (EMI) 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 (Malága University) 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 (including lyrics) 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.

Composers and artists like Jennifer Walshe and 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(s) by Georges Lentz. Software applications

ChucK

Developed at Princeton University by Ge Wang and Perry Cook, ChucK is a text-based, cross-platform language.ChucK => Strongly-timed, On-the-fly Audio Programming Language . Chuck.cs.princeton.edu. Retrieved on 2010-12-22. By extracting and classifying the theoretical techniques it finds in musical pieces, the software is able to synthesize entirely new pieces from the techniques it has learned. The technology is used by SLOrk (Stanford Laptop Orchestra)Driver, Dustin. (1999-03-26) Pro - Profiles - Stanford Laptop Orchestra (SLOrk), pg. 1 . Apple. Retrieved on 2010-12-22. and PLOrk (Princeton Laptop Orchestra). Jukedeck

Jukedeck was a website that let people use artificial intelligence to generate original, royalty-free music for use in videos. The team started building the music generation technology in 2010, formed a company around it in 2012, and launched the website publicly in 2015. The technology used was originally a rule-based algorithmic composition system, which was later replaced with artificial neural networks. The website was used to create over 1 million pieces of music, and brands that used it included Coca-Cola, Google, UKTV, and the Natural History Museum, London. In 2019, the company was acquired by ByteDance.

MorpheuS MorpheuS is a research project by Dorien Herremans and Elaine Chew at Queen Mary University of London, funded by a Marie Skłodowská-Curie EU project. The system uses an optimization approach based on a variable neighborhood search algorithm to morph existing template pieces into novel pieces with a set level of tonal tension that changes dynamically throughout the piece. This optimization approach allows for the integration of a pattern detection technique in order to enforce long term structure and recurring themes in the generated music. Pieces composed by MorpheuS have been performed at concerts in both Stanford and London.

AIVA

Created in February 2016, in Luxembourg, AIVA is a program that produces soundtracks for any type of media. The algorithms behind AIVA are based on deep learning architectures AIVA has also been used to compose a Rock track called On the Edge, as well as a pop tune Love Sick in collaboration with singer Taryn Southern, for the creation of her 2018 album "I am AI".

Google Magenta

Google's Magenta team has published several AI music applications and technical papers since their launch in 2016. In 2017 they released the NSynth algorithm and dataset, and an open source hardware musical instrument, designed to facilitate musicians in using the algorithm. The instrument was used by notable artists such as Grimes and YACHT in their albums. In 2018, they released a piano improvisation app called Piano Genie. This was later followed by Magenta Studio, a suite of 5 MIDI plugins that allow music producers to elaborate on existing music in their DAW. In 2023, their machine learning team published a technical paper on GitHub that described MusicLM, a private text-to-music generator which they'd developed.

Riffusion

Spike AI Spike AI is an AI-based audio plug-in, developed by Spike Stent in collaboration with his son Joshua Stent and friend Henry Ramsey, that analyzes tracks and provides suggestions to increase clarity and other aspects during mixing. Communication is done by using a chatbot trained on Spike Stent's personal data. The plug-in integrates into digital audio workstation.

Musical applications Artificial intelligence can potentially impact how producers create music by giving reiterations of a track that follow a prompt given by the creator. These prompts allow the AI to follow a certain style that the artist is trying to go for. AI has also been seen in musical analysis where it has been used for feature extraction, pattern recognition, and musical recommendations. New tools that are powered by artificial intelligence have been made to help aid in generating original music compositions, like AIVA (Artificial Intelligence Virtual Artist) and Udio. This is done by giving an AI model data of already-existing music and having it analyze the data using deep learning techniques to generate music in many different genres, such as classical music or electronic music. Musical and choral classrooms are already implementing AI driven programs to aid in musical learning, and development of creative skills in students where AI has shown to provide a statistically significant increase.

Ethical and legal considerations

Several musicians, such as Dua Lipa, Elton John, Nick Cave, Paul McCartney, and Sting, have criticized the use of AI in music and are encouraging the UK government to act on this matter. Another example of this protest is the silent 2025 album Is This What We Want?.

Some artists have encouraged the use of AI in music, such as Grimes.

While helpful in generating new music, many issues have come up since AI has begun making music. Some major concerns include how the economy will be impacted with AI taking over music production, who truly owns music generated by AI, and a lower demand for human-made musical compositions. Some critics argue that AI diminishes the value of human creativity, while proponents see it as an augmentative tool that expands artistic possibilities rather than replacing human musicians.

Additionally, concerns have been raised about AI's potential to homogenize music. AI-driven models often generate compositions based on existing trends, which some fear could limit musical diversity. Addressing this concern, researchers are working on AI systems that incorporate more nuanced creative elements, allowing for greater stylistic variation.