Music and artificial intelligence

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Music and artificial intelligence (music and AI) is the development of music software programs which use AI to generate music. [1] 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. [2] 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.

Contents

Erwin Panofksy 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. [3] [4] AI music explores the foremost of these, creating music without the "intention" which is usually behind it, leaving composers who listen to machine-generated pieces feeling unsettled by the lack of apparent meaning. [5]

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 (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. [6]

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 1752. [7]

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 Lejaren Hiller and mathematician Leonard Isaacson. [5] : v–vii  In 1960, Russian researcher Rudolf Zaripov published worldwide first paper on algorithmic music composing using the Ural-1 computer. [8]

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 . [9]

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. [7] [10]

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. [11] 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 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. [12]

Emily Howell would continue to make advancements in musical artificial intelligence, publishing its first album From Darkness, Light in 2009. [13] 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. [14] [5] : 468–481  In August 2019, a large dataset consisting of 12,197 MIDI songs, each with their lyrics and melodies, [15] 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. [16]

Software applications

ChucK

Developed at Princeton University by Ge Wang and Perry Cook, ChucK is a text-based, cross-platform language. [17] 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. [18] The technology is used by SLOrk (Stanford Laptop Orchestra) [19] 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. [20] [21] The team started building the music generation technology in 2010, [22] formed a company around it in 2012, [23] and launched the website publicly in 2015. [21] The technology used was originally a rule-based algorithmic composition system, [24] which was later replaced with artificial neural networks. [20] 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. [25] In 2019, the company was acquired by ByteDance. [26] [27] [28]

MorpheuS

MorpheuS [29] 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 [30] AIVA has also been used to compose a Rock track called On the Edge, [31] as well as a pop tune Love Sick [32] in collaboration with singer Taryn Southern, [33] for the creation of her 2018 album "I am AI".

Google Magenta

20-second music clip generated by MusicLM using the prompt "hypnotic ambient electronic music"

Google's Magenta team has published several AI music applications and technical papers since their launch in 2016. [34] In 2017 they released the NSynth algorithm and dataset, [35] and an open source hardware musical instrument, designed to facilitate musicians in using the algorithm. [36] The instrument was used by notable artists such as Grimes and YACHT in their albums. [37] [38] 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. [39] 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. [40] [41]

Riffusion

This section is an excerpt from Riffusion.[ edit ]
AI-generated audio featuring bossa nova music with electric guitar.png
Generated spectrogram from the prompt "bossa nova with electric guitar" (top), and the resulting audio after conversion (bottom)

Riffusion is a neural network, designed by Seth Forsgren and Hayk Martiros, that generates music using images of sound rather than audio. [42]

The resulting music has been described as "de otro mundo" (otherworldly), [43] although unlikely to replace man-made music. [43] The model was made available on December 15, 2022, with the code also freely available on GitHub. [44]

The first version of Riffusion was created as a fine-tuning of Stable Diffusion, an existing open-source model for generating images from text prompts, on spectrograms, [42] resulting in a model which used text prompts to generate image files which could then be put through an inverse Fourier transform and converted into audio files. [44] While these files were only several seconds long, the model could also use latent space between outputs to interpolate different files together [42] [45] (using the img2img capabilities of SD). [46] It was one of many models derived from Stable Diffusion. [46]

In December 2022, Mubert [47] similarly used Stable Diffusion to turn descriptive text into music loops. In January 2023, Google published a paper on their own text-to-music generator called MusicLM. [48] [49]

Forsgren and Martiros formed a startup, also called Riffusion, and raised $4 million in venture capital funding in October 2023. [50] [51]

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. [52] [53]

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. [5] AI has also been seen in musical analysis where it has been used for feature extraction, pattern recognition, and musical recommendations. [54] 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. [55]


While helpful in generating new music, many issues have come up since artificial intelligence 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. [56]

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. [57]

Another major concern about artificial intelligence in music is copyright laws. Many questions have been asked about who owns AI generated music and productions, as today’s copyright laws require the work to be human-authorized in order to be granted copyright protection. One proposed solution is to create hybrid laws that recognize both the artificial intelligence that generated the creation and the humans that contributed to the creation.

In the United States, the current legal framework tends to apply traditional copyright laws to AI, despite its differences with the human creative process. [58] However, music outputs solely generated by AI are not granted copyright protection. In the compendium of the U.S. Copyright Office Practices, the Copyright Office has stated that it would not grant copyrights to "works that lack human authorship" and "the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author." [59] In February 2022, the Copyright Review Board rejected an application to copyright AI-generated artwork on the basis that it "lacked the required human authorship necessary to sustain a claim in copyright." [60] The usage of copyrighted music in training AI has also been a topic of contention. One instance of this was seen when SACEM, a professional organization of songwriters, composers, and music publishers demanded that PozaLabs, an AI music generation startup refrain from utilizing any music affiliated with them for training models [61] .

The situation in the European Union (EU) is similar to the US, because its legal framework also emphasizes the role of human involvement in a copyright-protected work. [62] According to the European Union Intellectual Property Office and the recent jurisprudence of the Court of Justice of the European Union, the originality criterion requires the work to be the author's own intellectual creation, reflecting the personality of the author evidenced by the creative choices made during its production, requires distinct level of human involvement. [62] The reCreating Europe project, funded by the European Union's Horizon 2020 research and innovation program, delves into the challenges posed by AI-generated contents including music, suggesting legal certainty and balanced protection that encourages innovation while respecting copyright norms. [62] The recognition of AIVA marks a significant departure from traditional views on authorship and copyrights in the realm of music composition, allowing AI artists capable of releasing music and earning royalties. This acceptance marks AIVA as a pioneering instance where an AI has been formally acknowledged within the music production. [63]

The recent advancements in artificial intelligence made by groups such as Stability AI, OpenAI, and Google has incurred an enormous sum of copyright claims leveled against generative technology, including AI music. Should these lawsuits succeed, the machine learning models behind these technologies would have their datasets restricted to the public domain. [64] Strides towards addressing ethical issues have been made as well, such as the collaboration between Sound Ethics(a company promoting ethical AI usage in the music industry) and UC Irvine, focusing on ethical frameworks and the responsible usage of AI [65] .

Musical deepfakes

A more nascent development of AI in music is the application of audio deepfakes to cast the lyrics or musical style of a pre-existing song to the voice or style of another artist. This has raised many concerns regarding the legality of technology, as well as the ethics of employing it, particularly in the context of artistic identity. [66] Furthermore, it has also raised the question of to whom the authorship of these works is attributed. As AI cannot hold authorship of its own, current speculation suggests that there will be no clear answer until further rulings are made regarding machine learning technologies as a whole. [67] Most recent preventative measures have started to be developed by Google and Universal Music group who have taken into royalties and credit attribution to allow producers to replicated the voices and styles of artists. [68]

"Heart on My Sleeve"

In 2023, an artist known as ghostwriter977 created a musical deepfake called "Heart on My Sleeve" that cloned the voices of Drake and The Weeknd  by inputting an assortment of vocal-only tracks from the respective artists into a deep-learning algorithm, creating an artificial model of the voices of each artist, to which this model could be mapped onto original reference vocals with original lyrics. [69] The track was submitted for Grammy consideration for the best rap song and song of the year. [70]  It went viral and gained traction on TikTok and received a positive response from the audience, leading to its official release on Apple Music, Spotify, and YouTube in April 2023. [71] Many believed the track was fully composed by an AI software, but the producer claimed the songwriting, production, and original vocals (pre-conversion) were still done by him. [69] It would later be rescinded from any Grammy considerations due to it not following the guidelines necessary to be considered for a Grammy award. [71]  The track would end up being removed from all music platforms by Universal Music Group. [71] The song was a watershed moment for AI voice cloning, and models have since been created for hundreds, if not thousands, of popular singers and rappers.

"Where That Came From"

In 2013, country music singer Randy Travis suffered a stroke which left him unable to sing. In the meantime, vocalist James Dupré toured on his behalf, singing his songs for him. Travis and longtime producer Kyle Lehning released a new song in May 2024 titled "Where That Came From", Travis's first new song since his stroke. The recording uses AI technology to re-create Travis's singing voice, having been composited from over 40 existing vocal recordings alongside those of Dupré. [72] [73]

Technical approaches

Artificial intelligence music encompasses a number of technical approaches used for music composition, analysis, classification, and suggestion. Techniques used are drawn from deep learning, machine learning, natural language processing, and signal processing. Current systems are able to compose entire musical compositions, parse affective content, accompany human players in real-time, and acquire patterns of user and context-dependent preferences. [74] [75] [76] [77]

Symbolic music composition

Symbolic music generation is the generation of music in discrete symbolic forms such as MIDI, where note and timing are precisely defined. Early systems employed rule-based systems and Markov models, but modern systems employ deep learning to a large extent. Recurrent Neural Networks (RNNs), and more precisely Long Short-Term Memory (LSTM) networks, have been employed in modeling temporal dependencies of musical sequences. They may be used to generate melodies, harmonies, and counterpoints in various musical genres. [78]

Transformer models such as Music Transformer and MuseNet became more popular for symbolic generation due to their ability to model long-range dependencies and scalability. These models were employed to generate multi-instrument polyphonic music and stylistic imitations. [79]

Audio-based music generation

This method generates music as raw audio waveforms instead of symbolic notation. DeepMind's WaveNet is an early example that uses autoregressive sampling to generate high-fidelity audio. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are being used more and more in new audio texture synthesis and timbre combination of different instruments. [80]

NSynth (Neural Synthesizer), a Google Magenta project, uses a WaveNet-like autoencoder to learn latent audio representations and thereby generate completely novel instrumental sounds. [81]

Music information retrieval (MIR)

Music Information Retrieval (MIR) is the extraction of musically relevant information from audio recordings to be utilized in applications such as genre classification, instrument recognition, mood recognition, beat detection, and similarity estimation. CNNs on spectrogram features have been very accurate on these tasks. [82] SVMs and k-Nearest Neighbors (k-NN) are also used for classification on features such as Mel-frequency cepstral coefficients (MFCCs).

Hybrid and interactive systems

Hybrid systems combine symbolic and sound-based methods to draw on their respective strengths. They can compose high-level symbolic compositions and synthesize them as natural sound. Interactive systems in real-time allow for AI to instantaneously respond to human input to support live performance. Reinforcement learning and rule-based agents tend to be utilized to allow for human–AI co-creation in improvisation contexts. [83]

Affective computing and emotion-aware music systems

Affective computing techniques enable AI systems to classify or create music based on some affective content. The models use musical features such as tempo, mode, and timbre to classify or influence listener emotions. Deep learning models have been trained for classifying music based on affective content and even creating music intended to have affective impacts. [84]

AI-based music recommendation systems

Music recommenders employ AI to suggest tracks to users based on what they have heard, their tastes, and information available in context. Collaborative filtering, content-based filtering, and hybrid filtering are most widely applied, deep learning being utilized for fine-tuning. Graph-based and matrix factorization methods are used within commercial systems like Spotify and YouTube Music to represent complex user-item relationships. [85]

AI for automatic mixing and mastering

AI is also used in audio engineering automation such as mixing and mastering. Such systems level, equalize, pan, and compress to give well-balanced sound outputs. Software such as LANDR and iZotope Ozone utilize machine learning in emulating professional audio engineers' decisions. [86]

Lyrics generation and songwriting aid

Natural language generation also applies to songwriting assistance and lyrics generation. Transformer language models like GPT-3 have also been proven to be able to generate stylistic and coherent lyrics from input prompts, themes, or feeling. There even exist AI programs that assist with rhyme scheme, syllable count, and poem form. . [87]

Multimodal and cross-modal systems

Recent developments include multimodal AI systems that integrate music with other media, e.g., dance, video, and text. These can generate background scores in synchronization with video sequences or generate dance choreography from audio input. Cross-modal retrieval systems allow one to search for music using images, text, or gestures. [88]

Cultural impact

The advent of AI music has caused heated cultural debates, especially its impacts on creativity, morality, and audience. As much as there have been praises about the democratization of music production, there have been fears raised about its impacts on producers, audience, and society in general.

Reactions and controversies

The most contentious application of AI music creation has been its misuse to produce offensive work. The music AI platforms have been used in several instances to produce songs with offensive lyrics that were racist, antisemitic, or contained violence and have tested moderation and accountability in generative AI platforms.[ [89] ] The case has renewed argument about accountability in users and developers in producing moral outputs in generative models.

Aside from that, there have been several producers and artists denouncing the use of AI music due to threats to originality, handmade craftsmanship, and cultural authenticity. The music created by AIs lacks the emotional intelligence and lived life upon which human work relies, according to its critics. The concern comes in an era when there are steadily more songs made from AIs appearing on platforms and which others consider lowering human artistry.[ [90] ]

Musicians vs. consumers

Interestingly enough, while professional musicians have been uniformly dismissive about using AI in music production, the general consumer has been receptive or neutral to the idea. Surveys have found that the average consumer doesn't know or care whether they hear music made by human beings or AI and that a high percentage says that it doesn't affect their enjoyment.[ [90] ] The contrast between artist sentiment and consumer sentiment holds far-reaching consequences in terms of the future economics within the music industry and the worth assigned to human creativity.

Public perception and general perception

The cultural value placed on AI music is similarly related to overall popular perceptions regarding generative AI. How generative AI-produced work—whether music or writing—is received in human terms has been found to be dependent upon such factors as emotional meaning and authenticity.[ [91] ] Whereby the outputs from AIs prove persuasive and engaging, audience may be willing to accept writers who do not exist in human form with the potential to reshape conventions regarding writers and creativity.

Future directions

The field of music and artificial intelligence is still evolving. Some of the key future directions for advancement include advancements in generation models, changes in how humans and AI collaborate musically, and the development of legal and ethical frameworks to address the technology's impact.

Advancements in generation models

Future research and development is expected to move beyond established techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). More recent architectures such as diffusion models and transformer based networks [92] are showing promise for generating more complex, nuanced, and stylistically coherent music. These models may lead to higher quality audio generation and better long term structure in music compositions.

Human-AI collaboration

Besides the act of generation itself, a significant future direction of interest involves deepening the collaboration between human musicians and AI. Developments are increasingly focused on understanding the way these collaborations can occur, and how they can be facilitated to be ethically sound. [93] This involves studying musicians perceptions and experiences with AI tools to inform the design of future systems. Research actively explores these collaborative models in different domains. For instance, studies investigate how AI can be co-designed with professionals such as music therapists to act as supportive partners in complex creative and therapeutic processes, [94] showing a trend towards developing AI not just as an output tool, but as an integrated component designed to augment human skills.

Regulatory changes and ethical considerations

As AI generated music becomes more capable and widespread, legal and ethical frameworks worldwide are expected to continue adapting. Current policy discussions have been focusing on copyright ownership, the use of AI to mimic artists (deepfakes), and fair compensation for artists. [95] Recent legislative efforts and debates, such as those concerning AI safety and regulation in places like California, show the challenges involved in balancing innovation with potential risks and societal impacts. [96] Tracking these developments is crucial for understanding the future of AI in the music industry. [97]

See also

References

  1. ↑ D. Herremans; C.H.; Chuan, E. Chew (2017). "A Functional Taxonomy of Music Generation Systems". ACM Computing Surveys. 50 (5): 69:1–30. arXiv: 1812.04186 . doi:10.1145/3108242. S2CID   3483927.
  2. ↑ Dannenberg, Roger. "Artificial Intelligence, Machine Learning, and Music Understanding" (PDF). Semantic Scholar. S2CID   17787070. Archived from the original (PDF) on 23 August 2018. Retrieved 23 August 2018.
  3. ↑ "Erwin Panofsky, Studies in Iconology: Humanistic Themes in the Art of the Renaissance. Oxford 1939" (PDF). Archived (PDF) from the original on 29 December 2023. Retrieved 3 March 2024.
  4. ↑ Dilly, Heinrich (2020), Arnold, Heinz Ludwig (ed.), "Panofsky, Erwin: Zum Problem der Beschreibung und Inhaltsdeutung von Werken der bildenden Kunst", Kindlers Literatur Lexikon (KLL) (in German), Stuttgart: J.B. Metzler, pp. 1–2, doi:10.1007/978-3-476-05728-0_16027-1, ISBN   978-3-476-05728-0 , retrieved 3 March 2024
  5. 1 2 3 4 Miranda, Eduardo Reck, ed. (2021). "Handbook of Artificial Intelligence for Music". SpringerLink. doi:10.1007/978-3-030-72116-9. ISBN   978-3-030-72115-2. Archived from the original on 10 September 2024. Retrieved 10 September 2024.
  6. ↑ "AI and the Sound of Music". www.yalelawjournal.org. Retrieved 26 February 2025.
  7. 1 2 Roads, Curtis (1985). "Research in music and artificial intelligence". ACM Computing Surveys. 17 (2): 163–190. doi:10.1145/4468.4469. Archived from the original on 7 April 2024. Retrieved 6 March 2024.
  8. ↑ Zaripov, Rudolf (1960). "Об алгоритмическом описании процесса сочинения музыки (On algorithmic description of process of music composition)". Proceedings of the USSR Academy of Sciences . 132 (6).
  9. ↑ "Ray Kurzweil". National Science and Technology Medals Foundation. Archived from the original on 10 September 2024. Retrieved 10 September 2024.
  10. ↑ Katayose, Haruhiro; Inokuchi, Seiji (1989). "The Kansei Music System". Computer Music Journal. 13 (4): 72–77. doi:10.2307/3679555. ISSN   0148-9267. JSTOR   3679555. Archived from the original on 26 March 2020. Retrieved 6 March 2024.
  11. ↑ Johnson, George (11 November 1997). "Undiscovered Bach? No, a Computer Wrote It". The New York Times. Archived from the original on 19 March 2024. Retrieved 29 April 2020. Dr. Larson was hurt when the audience concluded that his piece -- a simple, engaging form called a two-part invention -- was written by the computer. But he felt somewhat mollified when the listeners went on to decide that the invention composed by EMI (pronounced Emmy) was genuine Bach.
  12. ↑ Pachet, François (September 2003). "The Continuator: Musical Interaction With Style". Journal of New Music Research. 32 (3): 333–341. doi:10.1076/jnmr.32.3.333.16861. hdl: 2027/spo.bbp2372.2002.044 . ISSN   0929-8215.
  13. ↑ Lawson, Mark (22 October 2009). "This artificially intelligent music may speak to our minds, but not our souls". The Guardian . ISSN   0261-3077 . Retrieved 10 September 2024.
  14. ↑ "Iamus: Is this the 21st century's answer to Mozart?". BBC News. 2 January 2013. Archived from the original on 10 September 2024. Retrieved 10 September 2024.
  15. ↑ yy1lab (13 November 2024), yy1lab/Lyrics-Conditioned-Neural-Melody-Generation, archived from the original on 6 January 2024, retrieved 19 November 2024{{citation}}: CS1 maint: numeric names: authors list (link)
  16. ↑ Nair, Vandana (11 April 2024). "AI-Music Platform Race Accelerates with Udio". Analytics India Magazine. Archived from the original on 19 April 2024. Retrieved 19 April 2024.
  17. ↑ ChucK => Strongly-timed, On-the-fly Audio Programming Language Archived 18 November 2003 at the Wayback Machine . Chuck.cs.princeton.edu. Retrieved on 2010-12-22.
  18. ↑ "Foundations of On-the-fly Learning in the ChucK Programming Language" (PDF). Archived (PDF) from the original on 21 April 2024. Retrieved 3 March 2024.
  19. ↑ Driver, Dustin. (1999-03-26) Pro - Profiles - Stanford Laptop Orchestra (SLOrk), pg. 1 Archived 18 January 2012 at the Wayback Machine . Apple. Retrieved on 2010-12-22.
  20. 1 2 "From Jingles to Pop Hits, A.I. Is Music to Some Ears". The New York Times . 22 January 2017. Archived from the original on 28 February 2023. Retrieved 3 January 2023.
  21. 1 2 "Need Music For A Video? Jukedeck's AI Composer Makes Cheap, Custom Soundtracks". techcrunch.com. 7 December 2015. Archived from the original on 3 January 2023. Retrieved 3 January 2023.
  22. ↑ "What Will Happen When Machines Write Songs Just as Well as Your Favorite Musician?". motherjones.com. Archived from the original on 3 February 2023. Retrieved 3 January 2023.
  23. ↑ Cookson, Robert (7 December 2015). "Jukedeck's computer composes music at touch of a button". Financial Times. Archived from the original on 3 January 2023. Retrieved 3 January 2023.
  24. ↑ "Jukedeck: the software that writes music by itself, note by note". Wired UK. Archived from the original on 3 January 2023. Retrieved 3 January 2023.
  25. ↑ "Robot rock: how AI singstars use machine learning to write harmonies". standard.co.uk. March 2018. Archived from the original on 3 January 2023. Retrieved 3 January 2023.
  26. ↑ "TIKTOK OWNER BYTEDANCE BUYS AI MUSIC COMPANY JUKEDECK". musicbusinessworldwide.com. 23 July 2019. Archived from the original on 8 February 2023. Retrieved 3 January 2023.
  27. ↑ "As TikTok's Music Licensing Reportedly Expires, Owner ByteDance Purchases AI Music Creation Startup JukeDeck". digitalmusicnews.com. 23 July 2019. Archived from the original on 3 January 2023. Retrieved 3 January 2023.
  28. ↑ "An AI-generated music app is now part of the TikTok group". sea.mashable.com. 24 July 2019. Archived from the original on 29 January 2023. Retrieved 3 January 2023.
  29. ↑ D. Herremans; E. Chew (2016). "MorpheuS: Automatic music generation with recurrent pattern constraints and tension profiles". IEEE Transactions on Affective Computing. arXiv: 1812.04832 . doi:10.1109/TAFFC.2017.2737984. S2CID   54475410.
  30. ↑ "A New AI Can Write Music as Well as a Human Composer". Futurism. 9 March 2017. Archived from the original on 19 April 2024. Retrieved 19 April 2024.
  31. ↑ Technologies, Aiva (24 October 2018). "The Making of AI-generated Rock Music with AIVA". Medium. Archived from the original on 25 October 2018. Retrieved 19 April 2024.
  32. ↑ Lovesick | Composed with AIVA Artificial Intelligence - Official Video with Lyrics | Taryn Southern. 2 May 2018. Archived from the original on 19 July 2018. Retrieved 25 October 2018.
  33. ↑ Southern, Taryn (10 May 2018). "Algo-Rhythms: The future of album collaboration". TechCrunch. Archived from the original on 25 October 2018. Retrieved 19 April 2024.
  34. ↑ "Welcome to Magenta!". Magenta. 1 June 2016. Archived from the original on 1 February 2023. Retrieved 19 April 2024.
  35. ↑ Engel, Jesse; Resnick, Cinjon; Roberts, Adam; Dieleman, Sander; Eck, Douglas; Simonyan, Karen; Norouzi, Mohammad (2017). "Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders". PMLR. arXiv: 1704.01279 .
  36. ↑ Open NSynth Super, Google Creative Lab, 13 February 2023, archived from the original on 19 December 2022, retrieved 14 February 2023
  37. ↑ "Cover Story: Grimes is ready to play the villain". Crack Magazine. Archived from the original on 3 June 2023. Retrieved 14 February 2023.
  38. ↑ "What Machine-Learning Taught the Band YACHT About Themselves". Los Angeleno. 18 September 2019. Archived from the original on 28 January 2023. Retrieved 14 February 2023.
  39. ↑ "Magenta Studio". Magenta. Archived from the original on 13 January 2023. Retrieved 19 April 2024.
  40. ↑ "MusicLM". google-research.github.io. 2023. Archived from the original on 1 February 2023. Retrieved 19 April 2024.
  41. ↑ Sandzer-Bell, Ezra (16 February 2024). "Best Alternatives to Google's AI-Powered MusicLM and MusicFX". AudioCipher. Archived from the original on 1 February 2023. Retrieved 19 April 2024.
  42. 1 2 3 Coldewey, Devin (15 December 2022). "Try 'Riffusion,' an AI model that composes music by visualizing it".
  43. 1 2 Llano, Eutropio (15 December 2022). "El generador de imágenes AI también puede producir música (con resultados de otro mundo)".
  44. 1 2 Nasi, Michele (15 December 2022). "Riffusion: creare tracce audio con l'intelligenza artificiale". IlSoftware.it.
  45. ↑ "Essayez "Riffusion", un modèle d'IA qui compose de la musique en la visualisant". 15 December 2022.
  46. 1 2 "文章に沿った楽曲を自動生成してくれるAI「Riffusion」登場、画像生成AI「Stable Diffusion」ベースで誰でも自由に利用可能". GIGAZINE. 16 December 2022.
  47. ↑ "Mubert launches Text-to-Music interface – a completely new way to generate music from a single text prompt". 21 December 2022.
  48. ↑ "MusicLM: Generating Music From Text". 26 January 2023.
  49. ↑ "5 Reasons Google's MusicLM AI Text-to-Music App is Different". 27 January 2023.
  50. ↑ Gal, Dr. Itay (10 February 2025). "Free A.I. music creation platform launches, competing with Suno". The Jerusalem Post. Retrieved 16 February 2025.
  51. ↑ Nuñez, Michael (30 January 2025). "Riffusion's free AI music platform could be the Spotify of the future". VentureBeat. Retrieved 16 February 2025.
  52. ↑ Levine, Mike (4 October 2024). "Spike AI — A Mix Product of the Week". Mix . Future US. Archived from the original on 14 November 2024. Retrieved 19 November 2024.
  53. ↑ "Spike Stent offers his expertise in Spike AI". Sound on Sound . Archived from the original on 17 December 2024. Retrieved 19 November 2024.
  54. ↑ Zhang, Yifei (December 2023). "Utilizing Computational Music Analysis and AI for Enhanced Music Composition: Exploring Pre- and Post-Analysis". Journal of Advanced Zoology. 44 (S-6): 1377–1390. doi: 10.17762/jaz.v44is6.2470 . S2CID   265936281.
  55. ↑ Mańdziuk, Jacek (16 November 2024). "Artificial intelligence in music: recent trends and challenges". ResearchGate.
  56. ↑ "Applications and Advances of Artificial Intelligence in Music Generation:A Review". arxiv.org. Retrieved 26 February 2025.
  57. ↑ Mańdziuk, Jacek (16 November 2024). "Artificial intelligence in music: recent trends and challenges". ResearchGate.
  58. ↑ "Art created by AI cannot be copyrighted, says US officials – what does this mean for music?". MusicTech. Archived from the original on 2 April 2023. Retrieved 27 October 2022.
  59. ↑ "Can (and should) AI-generated works be protected by copyright?". Hypebot. 28 February 2022. Archived from the original on 28 October 2022. Retrieved 27 October 2022.
  60. ↑ Re: Second Request for Reconsideration for Refusal to Register A Recent Entrance to Paradise (Correspondence ID 1-3ZPC6C3; SR # 1-7100387071) (PDF) (Report). Copyright Review Board, United States Copyright Office. 14 February 2022. Archived (PDF) from the original on 24 July 2023. Retrieved 28 October 2022.
  61. ↑ PozaLabs (December 8, 2024). "Pozalabs Responds to SACEM, Addresses Ethical AI Music Generation Concerns". GlobalNewswire. Retrieved April 5th, 2025. https://www.globenewswire.com/news-release/2024/12/09/2993436/0/en/Pozalabs-Responds-to-SACEM-Addresses-Ethical-AI-Music-Generation-Concerns.html
  62. 1 2 3 Bulayenko, Oleksandr; Quintais, João Pedro; Gervais, Daniel J.; Poort, Joost (February 28, 2022). "AI Music Outputs: Challenges to the Copyright Legal Framework" Archived 28 March 2024 at the Wayback Machine . reCreating Europe Report. Retrieved 2024-04-03.
  63. ↑ Ahuja, Virendra (June 11, 2021). "Artificial Intelligence and Copyright: Issues and Challenges" Archived 26 April 2024 at the Wayback Machine . ILI Law Review Winter Issue 2020. Retrieved 2024-04-03.
  64. ↑ Samuelson, Pamela (14 July 2023). "Generative AI meets copyright". Science. 381 (6654): 158–161. doi:10.1126/science.x.
  65. ↑ SoundEthics (February 4, 2025). "Sound Ethics and UC Irvine Partner to Shape the Future of Ethical AI in the Music Industry". Newswire. Retrieved April 5th, 2025. https://www.newswire.com/news/sound-ethics-and-uc-irvine-partner-to-shape-the-future-of-ethical-ai-22514569
  66. ↑ "DeepDrake ft. BTS-GAN and TayloRVC: An Exploratory Analysis of Musical Deepfakes and Hosting Platforms" (PDF). Archived (PDF) from the original on 26 March 2024. Retrieved 6 March 2024.
  67. ↑ "AI and Deepfake Voice Cloning: Innovation, Copyright and Artists' Rights" (PDF). Archived (PDF) from the original on 3 June 2024. Retrieved 6 March 2024.
  68. ↑ Murgia, Madhumita; Nicolaou, Anna (8 August 2023). "Google and Universal Music negotiate deal over AI 'deepfakes'". Financial Times. Archived from the original on 28 March 2024. Retrieved 3 April 2024.
  69. 1 2 Robinson, Kristin (11 October 2023). "Ghostwriter, the Mastermind Behind the Viral Drake AI Song, Speaks For the First Time". Billboard. Archived from the original on 5 April 2024. Retrieved 3 April 2024.
  70. ↑ "Drake/The Weeknd deepfake song "Heart on My Sleeve" submitted to Grammys". The FADER. Archived from the original on 3 April 2024. Retrieved 3 April 2024.
  71. 1 2 3 "The AI deepfake of Drake and The Weeknd will not be eligible for a GRAMMY". Mixmag. Archived from the original on 3 April 2024. Retrieved 3 April 2024.
  72. ↑ Marcus K. Dowling (6 May 2024). "Randy Travis' shocks music industry with AI pairing for 'Where That Came From.' How the song came together". The Tennesseean. Retrieved 6 May 2024.
  73. ↑ Maria Sherman (6 May 2024). "With help from AI, Randy Travis got his voice back. Here's how his first song post-stroke came to be". AP News. Archived from the original on 7 May 2024. Retrieved 6 May 2024.
  74. ↑ Mycka, Jan; Mańdziuk, Jacek (2024). "Artificial intelligence in music: recent trends and challenges". Neural Computing and Applications. 37 (2): 801–839. doi: 10.1007/s00521-024-10555-x .
  75. ↑ Briot, Jean-Pierre; Hadjeres, Gaëtan; Pachet, François-David (2017). "Deep learning techniques for music generation – A survey". arXiv: 1709.01620 [cs.SD].
  76. ↑ Herremans, Dorien; Chuan, Ching-Hua; Chew, Elaine (2017). "A functional taxonomy of music generation systems". ACM Computing Surveys. 50 (5): 69. arXiv: 1812.04186 . doi:10.1145/3108242.
  77. ↑ Sturm, Bob L.; Søndergaard, Martin (2019). "Music Information Retrieval and Artificial Intelligence: Best Friends or Worst Enemies?". Transactions of the International Society for Music Information Retrieval. 2 (1): 1–19. doi: 10.5334/tismir.28 (inactive 2 April 2025).{{cite journal}}: CS1 maint: DOI inactive as of April 2025 (link)
  78. ↑ Briot, Jean-Pierre; Hadjeres, Gaëtan; Pachet, François-David (2017). "Deep learning techniques for music generation – A survey". arXiv: 1709.01620 [cs.SD].
  79. ↑ Huang, Cheng-Zhi Anna (2018). "Music Transformer: Generating Music with Long-Term Structure". International Conference on Learning Representations. arXiv: 1809.04281 .
  80. ↑ Briot, Jean-Pierre; Hadjeres, Gaëtan; Pachet, François-David (2017). "Deep learning techniques for music generation – A survey". arXiv: 1709.01620 [cs.SD].
  81. ↑ Engel, Jesse (2017). "Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders". Proceedings of the 34th International Conference on Machine Learning. arXiv: 1704.01279 .
  82. ↑ Sturm, Bob L.; Søndergaard, Martin (2019). "Music Information Retrieval and Artificial Intelligence: Best Friends or Worst Enemies?". Transactions of the International Society for Music Information Retrieval. 2 (1): 1–19. doi: 10.5334/tismir.28 (inactive 2 April 2025).{{cite journal}}: CS1 maint: DOI inactive as of April 2025 (link)
  83. ↑ Herremans, Dorien; Chuan, Ching-Hua; Chew, Elaine (2017). "A functional taxonomy of music generation systems". ACM Computing Surveys. 50 (5): 69. arXiv: 1812.04186 . doi:10.1145/3108242.
  84. ↑ Kim, Yejin; Elliott, Mark T.; Kim, Youngmoo E. (2020). "Modeling Musical Affect Using Deep Neural Networks". Proceedings of the International Society for Music Information Retrieval (ISMIR).
  85. ↑ Schedl, Markus (2021). "Deep Learning in Music Recommendation Systems: A Survey". Journal of New Music Research. 50 (3): 232–247. doi:10.1080/09298215.2021.1939563 (inactive 2 April 2025).{{cite journal}}: CS1 maint: DOI inactive as of April 2025 (link)
  86. ↑ Bocko, Mark (2019). "The Role of Artificial Intelligence in Automated Music Production". Audio Engineering Society Conference.
  87. ↑ Vechtomova, Olga (2021). "Lyrics Generation with Neural Networks: Challenges and Opportunities". Transactions of the International Society for Music Information Retrieval.
  88. ↑ Choi, Keunwoo (2019). "Theoretical model for the Seebeck coefficient in superlattice materials with energy relaxation". Journal of Applied Physics. 126 (5). arXiv: 1908.01090 . Bibcode:2019JAP...126e5105V. doi:10.1063/1.5108607.
  89. ↑ Segarra, Edward. "Many musicians are speaking out against AI in music. But how do consumers feel?". USA TODAY. Retrieved 4 April 2025.
  90. 1 2 Wiggers, Kyle (3 June 2024). "People are using AI music generators to create hateful songs". TechCrunch. Retrieved 4 April 2025.
  91. ↑ Chu, Haoran; Liu, Sixiao (1 October 2024). "Can AI tell good stories? Narrative transportation and persuasion with ChatGPT". Journal of Communication. 74 (5): 347–358. doi:10.1093/joc/jqae029. ISSN   0021-9916.
  92. ↑ Chen, Yanxu; Huang, Linshu; Gou, Tian (2024). "Applications and Advances of Artificial Intelligence in Music Generation:A Review". arXiv: 2409.03715 [cs.SD].
  93. ↑ Newman, Michele; Morris, Lidia; Lee, Jin Ha (7 December 2023). "Human-AI Music Creation: Understanding the Perceptions and Experiences of Music Creators for Ethical and Productive Collaboration". Zenodo. doi:10.5281/zenodo.1026522.
  94. ↑ Sun, Jingjing; Yang, Jingyi; Zhou, Guyue; Jin, Yucheng; Gong, Jiangtao (2024). "Understanding Human-AI Collaboration in Music Therapy Through Co-Design with Therapists". Proceedings of the CHI Conference on Human Factors in Computing Systems. pp. 1–21. doi:10.1145/3613904.3642764. ISBN   979-8-4007-0330-0.
  95. ↑ "Innovation and Artists’ Rights in the Age of Generative AI". Georgetown Journal of International Affairs. 10 July 2024. Retrieved March 30, 2025. https://gjia.georgetown.edu/2024/07/10/innovation-and-artists-rights-in-the-age-of-generative-ai/
  96. ↑ Mehmood, Irfan; Mahroof, Kamran (October 3, 2024). "California’s governor blocked landmark AI safety laws. Here’s why it’s such a key ruling for the future of AI worldwide". The Conversation. Retrieved March 30, 2025. https://theconversation.com/californias-governor-blocked-landmark-ai-safety-laws-heres-why-its-such-a-key-ruling-for-the-future-of-ai-worldwide-240182
  97. ↑ Hight, Jewly (25 April 2024). "AI music isn't going away. Here are 4 big questions about what's next". NPR. Retrieved March 30, 2025. https://www.npr.org/2024/04/25/1246928162/generative-ai-music-law-technology

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