Difference between revisions of "Artificial Intelligence in the Music Industry"

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===Recommendation Models===
 
===Recommendation Models===
Another way AI is used in the music industry is by creating recommendation models. One of the most prominent users of this tool today is [https://en.wikipedia.org/wiki/Spotify/ Spotify]. Spotify uses a host of machine learning techniques to predict and customize playlists for its users. The most prominent example of this is the "Discover Weekly" playlist, a collection of 30 songs curated specifically for the user each week based on search history, listening patterns, and predictive models. One way they do this is through [https://en.wikipedia.org/wiki/Collaborative_filtering#:~:text=In%20the%20newer%2C%20narrower%20sense,from%20many%20users%20(collaborating)/ collaborative filtering],which compares different users with similar behaviors to predict what a user might enjoy or want to listen to next. Another tool is [https://en.wikipedia.org/wiki/Natural_language_processing/ Natural Language Processing (NPL)], which analyses human speech patterns through text. AI accumulates words that are associated with different artists by scanning the internet for articles and posts written about them. They can then associate artists who have similar cultural vectors, or top terms which each other and recommend similar artists to their users. One final tool Spotify uses to curate user-specific playlists is audio models. Audio models are most useful when an artist is new and doesn’t have much online about them yet or many listeners. Audio models analyze raw audio tracks and categorize them with similar songs. This way, they are able to recommend lesser-known artists alongside popular tracks <ref> Sen, Ipshita. “How AI Helps Spotify Win in the Music Streaming World.” Outside Insight, 26 Nov. 2018, outsideinsight.com/insights/how-ai-helps-spotify-win-in-the-music-streaming-world. </ref>.  
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Another way AI is used in the music industry is by creating recommendation models. One of the most prominent users of this tool today is [https://en.wikipedia.org/wiki/Spotify/ Spotify]. Spotify uses a host of machine learning techniques to predict and customize playlists for its users. The most prominent example of this is the "Discover Weekly" playlist, a collection of 30 songs curated specifically for the user each week based on search history, listening patterns, and predictive models. One way they do this is through [https://en.wikipedia.org/wiki/Collaborative_filtering#:~:text=In%20the%20newer%2C%20narrower%20sense,from%20many%20users%20(collaborating)/ collaborative filtering],which compares different users with similar behaviors to predict what a user might enjoy or want to listen to next. Another tool is [https://en.wikipedia.org/wiki/Natural_language_processing/ Natural Language Processing (NPL)], which analyses human speech patterns through text. AI accumulates words that are associated with different artists by scanning the internet for articles and posts written about them. They can then associate artists who have similar cultural vectors, or top terms which each other and recommend similar artists to their users. One final tool Spotify uses to curate user-specific playlists is audio models. Audio models are most useful when an artist is new and doesn’t have much online about them yet or many listeners. Audio models analyze raw audio tracks and categorize them with similar songs. This way, they are able to recommend lesser-known artists alongside popular tracks <ref> Sen, Ipshita. “How AI Helps Spotify Win in the Music Streaming World.” Outside Insight, 26 Nov. 2018, outsideinsight.com/insights/how-ai-helps-spotify-win-in-the-music-streaming-world. </ref>.
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===Warner Bros and Endel===
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In 2019, Warner Music Group became the first major music label to sign a deal for the rights of an artificial intelligence software. The development company of this software, Endel, agreed to create 20 albums throughout the course of the year, each focusing on different moods. The development team uses algorithms to create “personalized” songs designed to lower the stress of listeners, boosting their productivity, and more. As the songs for each mood are still created through the core software, every album is created “just by pressing one button.” Although this news was met with some concern from artists and listeners alike, Endel does not view itself as competition for more established musicians, instead generating sounds designed to “blend with the background.” <ref name = "Wang"> Wang, Amy. "Warner Music Group Signs an Algorithm to a Record Deal" Rolling Stone, 23 March 2019, Warner Music Group Signs an Algorithm to a Record Deal </ref>
  
 
==Ethical Implications==
 
==Ethical Implications==

Revision as of 11:35, 2 April 2021

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Artificial Intelligence in the Music Industry is a recent phenomenon that has already had a significant impact on the way we conceive, produce, and consume music. Artificial intelligence (AI) as a system works by learning and responding in a way that mimics the thought processes of a human. Major corporations in the music industry have developed applications that can create, perform, and/or curate music through the power of AI's simulation of human intelligence [1] . This subset of the music industry is relatively new, but growing at a rapid rate. Although its advancements have been great, using AI in music has the potential to cause issues in the future for artists who could have to compete against machines for recognition. Potential legal discrepancies regarding copyright infringement are also of major concern.

The Illinois Automatic Computer (ILLIAC) [1].

History

A Look Into AI

AI as a concept came into popular consciousness in the 1950s with British mathematician Alan Turing's paper on how to build an 'intelligent machine' and how to test its intelligence[2]. Since then, interest in AI as a potential field of study has waxed and waned. The use of AI in media, like books and movies, has increased awareness of AI in both the general public and academia. Recently, we have seen an exponential increase in the implementation of AI in our everyday lives, from virtual assistants like Amazon's Alexa and Apple's Siri to fraud detection in personal banking [3].

AI In Music

The first recorded instance of computer-generated music was in 1951 by Turing. Turing built the BBC outside-broadcast unit at the Computing Machine Library in Manchester, England, and used it to generate a variety of different melodies. To do so, he programmed individual musical notes into the computer. Artificial intelligence was first used to create music in 1957 by Lejaren Hiller and Leonard Isaacson from the University of Illinois at Urbana–Champaign [4]. Hiller and Isaacson programmed the ILLIAC (Illinois Automatic Computer) to generate music written start-to-finish by artificial intelligence. Around the same time, Russian researcher R.Kh.Zaripov published the first widely-available paper on algorithmic music composing. He used the historical Russian computer, URAL-1, to do so [4] [1].

Since those milestones, research and software in AI generated music has flourished. In 1974, the first International Computer Music Conference (ICMC) was hosted at Michigan State University in East Lansing, Michigan. The ICMC is now an annual event hosted by the International Computer Music Association (ICMA) for AI composers and researchers alike [1].

Applications

Music Production

One of the most notable breakthroughs in computer-generated music was the Experiments in Musical Intelligence (EMI) system. Developed in 1981 by David Cope, an American composer and scientist at the University of California, Santa Cruz, EMI was able to analyze different types of music and create unique compositions by genre. It has now created more than a thousand different musical works based on over 30 different composers [4] [1] [5].

In 2016, Google, a leader in AI technology, released Magenta. Its mission statement was to become “an open-source research project exploring the role of machine learning as a tool in the creative process” [6]. Instead of learning from hard-coded rules, Magenta learns by example from other humans. In this way, it acts as an assistant to humans in the creative process, rather than a machine-part replacement[7].

Recommendation Models

Another way AI is used in the music industry is by creating recommendation models. One of the most prominent users of this tool today is Spotify. Spotify uses a host of machine learning techniques to predict and customize playlists for its users. The most prominent example of this is the "Discover Weekly" playlist, a collection of 30 songs curated specifically for the user each week based on search history, listening patterns, and predictive models. One way they do this is through collaborative filtering,which compares different users with similar behaviors to predict what a user might enjoy or want to listen to next. Another tool is Natural Language Processing (NPL), which analyses human speech patterns through text. AI accumulates words that are associated with different artists by scanning the internet for articles and posts written about them. They can then associate artists who have similar cultural vectors, or top terms which each other and recommend similar artists to their users. One final tool Spotify uses to curate user-specific playlists is audio models. Audio models are most useful when an artist is new and doesn’t have much online about them yet or many listeners. Audio models analyze raw audio tracks and categorize them with similar songs. This way, they are able to recommend lesser-known artists alongside popular tracks [8].

Warner Bros and Endel

In 2019, Warner Music Group became the first major music label to sign a deal for the rights of an artificial intelligence software. The development company of this software, Endel, agreed to create 20 albums throughout the course of the year, each focusing on different moods. The development team uses algorithms to create “personalized” songs designed to lower the stress of listeners, boosting their productivity, and more. As the songs for each mood are still created through the core software, every album is created “just by pressing one button.” Although this news was met with some concern from artists and listeners alike, Endel does not view itself as competition for more established musicians, instead generating sounds designed to “blend with the background.” [9]

Ethical Implications

The Commodification of Music

Some ethical concerns about the future of music and AI have arisen over recent years, one of which is the commodification of music. With the popularization of AI-generated music on the horizon, there is concern that music will be, or is currently being, made and sold solely for profit. Music has always been widely seen as an essentially-human art medium that pulls from emotion and experience. Many would assume real music is something no machine could recreate. However, emotion and experience might not be key ingredients to a chart-topping song these days. AI technology can easily generate songs that are trendy and catchy with the power to analyze and remix music that is already popular. This also poses the threat of music becoming homogenous or lacking variety [10].

Computers generating new music through AI

Copyright Infringement

One of the biggest ethical dilemmas that AI-generated music is facing is copyright infringement. Allowing AI systems to listen to copyrighted music and then generate similar songs without compensating or citing the original artist could result in huge legal complications. As laws currently stand now, copyright infringement can only occur if AI creates a song that sounds similar to an existing song and claims it as its own. This is a hard line to draw because it may depend on how similar the songs are to make this call — what does "similar" look like and can it be defined in a legal context? This law was written without AI systems in mind, meaning it is likely that they never took into consideration the implications of its consequences, like a machine listening to and pulling from an artist's entire discography to create one song. From an artist's point of view, it may seem unfair to not be credited or compensated for something like this [11] [12].

AI's Impact on Employment

Since its beginnings, AI has threatened human-employment in various fields. This phenomenon has already begun in the music industry and will continue as AI is increasingly implemented. While it makes many jobs easily replaceable by machines, it can also provide new opportunities for human-centric careers [13]. Like many aspects of AI's impact on music, how exactly its effect on employment and opportunities will play out in the future is unclear.

See Also

References

  1. 1.0 1.1 1.2 1.3 1.4 Freeman, Jeremy. “Artificial Intelligence and Music — What the Future Holds?” Medium, 24 Feb. 2020, medium.com/@jeremy.freeman_53491/artificial-intelligence-and-music-what-the-future-holds-79005bba7e7d.
  2. Anyoha, Rockwell. “The History of Artificial Intelligence” Harvard University, August 28, 2017, https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/.
  3. Adams, R. “10 Powerful Examples Of Artificial Intelligence In Use Today.” Forbes, 6 Nov. 2017, www.forbes.com/sites/robertadams/2017/01/10/10-powerful-examples-of-artificial-intelligence-in-use-today/?sh=337d635e420d.
  4. 4.0 4.1 4.2 Li, Chong. “A Retrospective of AI + Music - Prototypr.” Medium, 25 Sept. 2019, blog.prototypr.io/a-retrospective-of-ai-music-95bfa9b38531.
  5. Cope, David. "Experiments in Musical Intelligence" University of California, Santa Cruz, http://artsites.ucsc.edu/faculty/cope/experiments.htm
  6. Magenta. Google, magenta.tensorflow.org.
  7. “How Google Is Making Music with Artificial Intelligence.” Science | AAAS, 8 Dec. 2017, www.sciencemag.org/news/2017/08/how-google-making-music-artificial-intelligence.
  8. Sen, Ipshita. “How AI Helps Spotify Win in the Music Streaming World.” Outside Insight, 26 Nov. 2018, outsideinsight.com/insights/how-ai-helps-spotify-win-in-the-music-streaming-world.
  9. Wang, Amy. "Warner Music Group Signs an Algorithm to a Record Deal" Rolling Stone, 23 March 2019, Warner Music Group Signs an Algorithm to a Record Deal
  10. Staff. “What Does Commodification Mean for Modern Musicians?” Dorico, 13 Mar. 2018, blog.dorico.com/2018/01/commodification-music-mean-modern-musicians.
  11. “How AI Is Benefiting The Music Industry?” Tech Stunt, 20 Aug. 2020, techstunt.com/how-ai-is-benefiting-the-music-industry.
  12. “We’ve Been Warned About AI and Music for Over 50 Years, but No One’s Prepared.” The Verge, 17 Apr. 2019, www.theverge.com/2019/4/17/18299563/ai-algorithm-music-law-copyright-human.
  13. Thomas, Mike. “ARTIFICIAL INTELLIGENCE'S IMPACT ON THE FUTURE OF JOBS” builtin, 8 Apr. 2020, https://builtin.com/artificial-intelligence/ai-replacing-jobs-creating-jobs.