Each fall, I begin my course\u00a0on the intersection of music and artificial intelligence<\/a>\u00a0by asking my students if they\u2019re concerned about AI\u2019s role in composing or producing music.<\/p>\n So far, the question has always elicited a resounding \u201cyes.\u201d<\/p>\n Their fears can be summed up in a sentence: AI will create a world where music is plentiful, but musicians get cast aside.<\/p>\n In the upcoming semester, I\u2019m anticipating a discussion about Paul McCartney, who in June 2023 announced that he and a team of audio engineers had used machine learning to uncover a \u201clost\u201d vocal track of John Lennon\u00a0by separating the instruments from a demo recording<\/a>.<\/p>\n But resurrecting the voices of\u00a0long-dead artists<\/a>\u00a0is just the tip of the iceberg in terms of what\u2019s possible \u2013 and what\u2019s already being done.<\/p>\n In an interview<\/a>, McCartney admitted that AI represents a \u201cscary\u201d but \u201cexciting\u201d future for music. To me, his mix of consternation and exhilaration is spot on.<\/p>\n Here are three ways AI is changing the way music gets made \u2013 each of which could threaten human musicians in various ways:<\/p>\n Many programs can already generate music with a simple prompt from the user, such as \u201cElectronic Dance with a Warehouse Groove.\u201d<\/p>\n Fully generative apps<\/a>\u00a0train AI models on extensive databases of existing music. This enables them to learn musical structures, harmonies, melodies, rhythms, dynamics, timbres and form, and generate new content that stylistically matches the material in the database.<\/p>\n There are many examples of these kinds of apps. But the most successful ones, like\u00a0Boomy<\/a>, allow nonmusicians to generate music and then post the AI-generated results on Spotify to earn money.\u00a0Spotify recently removed many of these Boomy-generated tracks<\/a>, claiming that this would protect human artists\u2019 rights and royalties.<\/p>\n The two companies quickly came to an agreement that allowed Boomy to re-upload the tracks. But the algorithms powering these apps still have a\u00a0troubling ability to infringe upon existing copyright<\/a>, which might go unnoticed to most users. After all, basing new music on a data set of existing music is bound to cause noticeable similarities between the music in the data set and the generated content.<\/p>\n Furthermore, streaming services like Spotify and\u00a0Amazon Music<\/a>\u00a0are naturally incentivized to develop their own\u00a0AI music-generation technology<\/a>. Spotify, for instance,\u00a0pays 70% of the revenue of each stream<\/a>\u00a0to the artist who created it. If the company could generate that music with its own algorithms, it could cut human artists out of the equation altogether.<\/p>\n Over time, this could mean more money for giant streaming services, less money for musicians \u2013 and a less human approach to making music.<\/p>\n Machine-learning-enabled apps that help musicians balance all of the instruments and clean up the audio in a song \u2013 what\u2019s known as mixing and mastering \u2013 are valuable tools for those who lack the experience, skill or resources to pull off professional-sounding tracks.<\/p>\n Over the past decade, AI\u2019s integration into music production has revolutionized how music is mixed and mastered. AI-driven apps like\u00a0Landr<\/a>,\u00a0Cryo Mix<\/a>\u00a0and\u00a0iZotope\u2019s Neutron<\/a>\u00a0can automatically analyze tracks, balance audio levels and remove noise.<\/p>\n These technologies streamline the production process, allowing musicians and producers to focus on the creative aspects of their work and leave some of the technical drudgery to AI.<\/p>\n While these apps undoubtedly take some work away from professional mixers and producers, they also allow professionals to quickly complete less lucrative jobs,\u00a0such as mixing or mastering for a local band<\/a>, and focus on high-paying commissions that require more finesse. These apps also allow musicians to produce more professional-sounding work without involving an audio engineer they can\u2019t afford.<\/p>\n Using \u201ctone transfer\u201d algorithms\u00a0via apps like Mawf<\/a>, musicians can transform the sound of one instrument into another.<\/p>\n Thai musician and engineer\u00a0Yaboi Hanoi\u2019s<\/a>\u00a0song \u201cEnter Demons & Gods<\/a>,\u201d which won the third international\u00a0AI Song Contest<\/a>\u00a0in 2022, was unique in that it was influenced not only by Thai mythology, but also by the sounds of native Thai musical instruments, which have a non-Western system of intonation. One of the most technically exciting aspects of Yaboi Hanoi\u2019s entry was the reproduction of a traditional Thai woodwind instrument \u2013\u00a0the pi nai<\/a>\u00a0\u2013\u00a0which was resynthesized<\/a>\u00a0to perform the track.<\/p>\n A variant of this technology lies at the core of the\u00a0Vocaloid voice synthesis software<\/a>, which allows users to produce convincingly human vocal tracks with swappable voices.<\/p>\n Unsavory applications of this technique<\/a>\u00a0are popping up outside of the musical realm. For example, AI voice swapping has been used to scam people out of money.<\/p>\n But musicians and producers can already use it to realistically reproduce the sound of any instrument or voice imaginable. The downside, of course, is that this technology can rob instrumentalists of the opportunity to perform on a recorded track.<\/p>\n While I applaud Yaboi Hanoi\u2019s victory, I have to wonder if it will encourage musicians to use AI to fake a cultural connection where none exists.<\/p>\n In 2021, Capitol Music Group made headlines by signing an \u201cAI rapper\u201d that had been given the avatar of a Black male cyborg, but which was really the work of Factory New non-Black software engineers. The backlash was swift, with the record label roundly excoriated\u00a0for blatant cultural appropriation<\/a>.<\/p>\n But AI musical cultural appropriation is easier to stumble into than you might think. With the extraordinary size of songs and samples that comprise the data sets used by apps like Boomy \u2013 see the open source \u201cMillion Song Dataset\u201d\u00a0for a sense of the scale<\/a>\u00a0\u2013 there\u2019s a good chance that a user may unwittingly upload a newly generated track that pulls from a culture that isn\u2019t their own, or cribs from an artist in a way that too closely mimics the original. Worse still, it won\u2019t always be clear who is to blame for the offense, and current U.S. copyright laws are contradictory and woefully inadequate to the task of regulating these issues.<\/p>\n These are all topics that have come up in my own class, which has allowed me to at least inform my students of the dangers of unchecked AI and how to best avoid these pitfalls.<\/p>\n At the same time, at the end of each fall semester, I\u2019ll again ask my students if they\u2019re concerned about an AI takeover of music. At that point, and with a whole semester\u2019s experience investigating these technologies, most of them say they\u2019re excited to see how the technology will evolve and where the field will go.<\/p>\n Some dark possibilities do lie ahead for humanity and AI. Still, at least in the realm of musical AI, there is cause for some optimism \u2013 assuming the pitfalls are avoided.<\/p>\n This article is republished from\u00a0<\/em>The Conversation<\/a>\u00a0under a Creative Commons license. Read the\u00a0original article\u00a0<\/a>by\u00a0<\/em>Jason Palamara<\/em>,<\/a> Assistant Professor of Music Technology, Indiana University<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":" Each fall, I begin my course\u00a0on the intersection of music and artificial intelligence\u00a0by asking my students if they\u2019re concerned about AI\u2019s role in composing or producing music. So far, the question has always elicited a resounding \u201cyes.\u201d Their fears can be summed up in a sentence: AI will create a world where music is plentiful, […]<\/p>\n","protected":false},"author":2,"featured_media":140189,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[245,239,243,242,244,202,184,241,240,189],"class_list":["post-140188","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nft","tag-azuki","tag-board-ape","tag-erc115","tag-erc721","tag-metamask","tag-nft","tag-nft-technology","tag-opensea","tag-sbt","tag-soul-bound-token"],"_links":{"self":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/140188","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/comments?post=140188"}],"version-history":[{"count":2,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/140188\/revisions"}],"predecessor-version":[{"id":147422,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/140188\/revisions\/147422"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/media\/140189"}],"wp:attachment":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/media?parent=140188"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/categories?post=140188"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/tags?post=140188"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}1. Song composition<\/h2>\n
<\/figcaption><\/figure>\n2. Mixing and mastering<\/h2>\n
![\"\"](\"https:\/\/nftnow.com\/wp-content\/uploads\/2023\/08\/Untitled-design-34.jpg\")
<\/figure>\n<\/div>\n3. Instrumental and vocal reproduction<\/h2>\n
AI\u2019s Wild West moment<\/h2>\n