iberation from mundane,menial tasks in these circumstances is tantamount to liberation from the ability tomake a living – and, more to the point, the ability to make a living as a musician.
AI will take job opportunities from musicians, who already struggle to make a living.
Third, commercial applications using machine learning to generate cheap musicshould be a cause for concern, even if the only kind of music presently at risk is thehistorically stigmatised genre of production music.
I agree, even though commercial music isn't the same as music created by artists, outsourcing it to AI removes career outlets for musicians.
As a consequence, what may seem like empty marketing hype at pre-sent may end up shaping the agenda for future work in this domain, encouragingcertain pursuits while suppressing others.
How much money is made in the AI music industry? I will research this next.
According to this line of argument,delegating to machines mundane and menial forms of creative work – like turningfinancial reports into news stories (Martin 2019) – might free up creative energies thatcould be more fruitfully directed elsewhere. Extended to production music, concedingthis domain to machines would presumably liberate musicians to pursue more aes-thetically rewarding activities
This is an argument that is still being discussed today, it does seem that AII is replacing creative pursuits rather than mundane tasks, leaving humans with no outlet left to be human.
machine learning by certain of these firms hardly represents the most innovativeapplications of such technologies
This article is 5 years old. I am reading it from today's lens, knowing that AI music technology has advanced far beyond where it was then, making these issues even more prevalent now.
this power would be redistributed to musicianswho, by and large, do not work directly for the companies in question, but whosemusic does.
In the AI music business, music is a profit; musicians contribute to the data, and can get compensation for their efforts through redistribution.
One possi-bility would be the creation of some kind of ownership fund, either targeting individ-ual firms or the music technology sector more broadly. In line with other workerownership funds proposed over the years (Guinan 2019; Gowan 2019), shares mightbe issued to a body representing those musicians whose creative output is exploitednot just by music AI companies but other music tech firms as well. The main appealof such funds is that they redistribute not just wealth, but economic power, includingthe power to determine how and where to invest resources.
Allocating a portion of profits for public use redistributes wealth, which is also good for the economy.
The proceeds would be directed to the Trust Fund, which then distributedthe monies raised to pay for free concerts across North America. Not only did this pro-vide underemployed musicians living outside major urban areas with paid work,redressing geographic disparities in cultural participation, but it also diminished some-what the winner-take-all tendencies that technologies of mass reproduction exacer-bate
This solved the royalty split issue of who should get credit; the solution was to redistribute the money to the public by helping musicians in need.
The same principle holds for machine learning techniques, despite the distance sepa-rating them from the Markov processes employed by Olson and Belar. What ties themtogether is a reliance on what Adrian Mackenzie refers to as ‘probabilization’, as‘formalisms derived from statistics’
AI music generation works the same way as randomly generated melodies; it randomly generates a song based on its training data, which can result in segments that emulate existing songs.
While certain trigrams are more probable and others less so, it’snot the case that an improbable sequence (like E4-D4-C#5) somehow counts for less,or that the single song where it appears contributes less than others. The song’s con-tribution isn’t the pattern, but its impact on the overall distribution of probabilities.
There is a difference between blatantly stealing a melody versus a statistically likely repetition. If you're only looking at three notes of a melody, you will find many songs with those same three notes in the same sequence.
If it is difficult to isolate the contribution made by anysingle input, this is because no input contributes in isolation.
The music industry thrives on recycled ideas; a new idea fuels a new genre, and samples are passed around dozens of times.
Again,within current copyright regimes this test applies only at the level of individual works.A prominent case in point is Robin Thicke and Pharrell Williams’ 2013 song ‘BlurredLines’. Following a lengthy lawsuit, in 2015 a jury found the two musicians guilty ofhaving infringed upon Marvin Gaye’s 1977 hit ‘Got to Give It Up’.
There is a line of what can and can't be flagged as a copyright infringement; an identical chord progression can't be sued for, but a sample without permission can.
like the shared conven-tions governing a genre – to produce a technical resource
Meaning some aspects of genres are not able to be copyrighted, things such as common genre drum beats, and chord progressions cannot be copyrighted.
determining thecontribution their works made to its training, and apportioning royalties accordingly.Such difficulties would appear to rule out, either in principle or in practice, anyclaim that authors of training data might have on works generated by a machinelearner trained on their music.
It would be very hard to keep track of the amount of stake both people and machines had in the creation of something to determine royalty splits.
Artist Rights Watch, for one, has called for musicians to invoke the marketingrestriction clause in recording and publishing contracts to refuse their music’s use ‘forAI purposes of any kind’
An answer to "Is any music safe from data harvesting?"
whose system istrained on a large number of musical ‘stems’ that an in-house composer in theiremploy creates for hire. Barring that, companies can assert some sort of exemption.
This is a fairer way to do this, as now the data isn't from somewhere they don't have permission to use.
Crucially, developers of commercial systems, unlike academicresearchers, aren’t obliged to reveal the sources of their training data
This is likely because they have access to data that they dont have direct permission to use.
Thatthese companies have title to the algorithms they developed isn’t in dispute; what is in dispute,however, is whether the works their systems produce belongs to them, some other party, ornobody at all.
Should AI products be stripped of all ownership and become public property?
A variety of legal doctrines have been mobi-lised in support of each of these candidates. Some have appealed to utilitarian theoryto buoy the claims of programmers and/or owners of AI systems, arguing that grant-ing them rights to AI-generated works will encourage the continued growth of the AIsector
How will this be resolved?
Yet AIs, unlike humans, are insensible to such rewards,whether monetary or symbolic. Insofar as ‘machines need no incentive to work’,
This is an important detail when answering the question of how much each part influences the creation. AIs aren't like humans; they don't need rest.
Granting authors a temporary monopoly over their creations is regarded as animportant spur to creation, one that ideally harmonises individual and general interest:artists are rewarded for their investments of time, effort, and resources
In AI music creation, the question that needs to be answered is what is the power balance, how much of the creation process is influenced by a users prompt, the data from artists, the process the machine went through to create the song, and the programmers who made the AI music generator.?
What is more, there’s little appetite within legal circles for reforming statutesto grant machines rights on the works they produce.
Is it ethical to pay a machine?
in UK lawmachines aren’t eligible for copyright assignment, with rights defaulting to their creatorsor owners.
Is this still true in 2025?
machine turned out, their recombination in different configurations
At what point does a song generated from existing material become "original" enough to evade copyright?
Another, less visible place wherethe same transition can be seen is in traditional production music companies, whichhave also adopted a platform model. But in contrast to these and other, more familiardigital platforms (like Facebook or Amazon), the platformization of commercial musicAI doesn’t involve one group of users being connected to another, but instead agroup of users being connected to an AI system.
Is any music safe from data harvesting? There appears to be no safeguard against having your music being used in training data.
he fact that onedoesn’t pay with money doesn’t mean one isn’t paying in some other way, usingsome other currency. As with so many other digital services, payment is still beingmade: it is simply that it is being made in the form of personal data
That is how these types of services stay afloat, by profiting off your data, and the data it is trained on
do not sell products to clients, but services. A case inpoint is Mubert, a company that bridges the consumer and business-to-business mar-kets. For brands, content producers, and/or brick-and-mortar businesses, Mubert offersa range of subscription plans. For a flat monthly fee, one can generate as muchbespoke music as one needs or desires ‘for free’
This means the business avoids copyright responsibility, as the user is the one who actually generates the music.
25,000 MIDI files on its site,in such genres as klezmer, tango, and the blues, while bitmidi.com boasts roughly113,000 MIDI files, from an equally diverse range of genres and styles.
So, MIDI websites have significantly affected the music industry, which doesn't exactly answer the research question, but is adjacent. Also I just checked out BitMIdi, it was really weird, it has a ton of instrumenetal versions of songs, basically the elevator music versions of songs. It's really wierd, its versions of songs with chip tune drums, midi saxohpone, and midi strings.
Weav Run, whose appadjusts tracks according to the cadence of one’s stride whilst walking or running, withnot just the tempo of a track changing in real time, but also its texture, timbre, andarrangement (Weav Music 2019). A third example is AI Music, whose founder describesits applications as a means of ‘shape-shifting’ music so that it can adjust to differentlistening situations
This is a really cool idea.
suchmusic is not intended for direct consumption by end users, but is marketed instead toother cultural producers, typically for use in mixed media products like games, adver-tisements, or online web content.
Does this mean royalty-free music, or non-copyrighted music?
the ‘MusicComposing Machine’ developed at RCA in the 1950s
Wow, i had no idea something like this existed that long ago, I want learn more about how it actually functioned.
one that transposes tracks from one musical genre to another
How does this work?
Since 2015 there has been a marked growth in the number of startups and technol-ogy companies seeking to commercialise music produced using artificial intelligence.
I had no idea that generative AI was around 10 years ago; I thought there was only narrow AI with tools such as Siri and Grammarly. This opens my eyes to the hidden landscape of AI in the past decades. The truth is, AI has been around for many decades, and looks very different now than it did before.
the article sketches a couple ofalternative models (levy-based trust funds, ownership funds) thatcould provide a more equitable institutional
the goal of the research in this article is to find a more erthical split in profits among AI models, the user, and the artists that are part of training data.
the music that constitutes the trainingset necessary for machine learners to learn. Given the massivedatasets mobilised to train machine learners, existing copyrightregimes prove inadequate in the face of the questions of distribu-tive justice
This means that AI models are trained on music they don't have ownership over, producing music that people profit off of, created from material that was protected under copyright.
recently dis-cussion has focused on who (or what) should be awarded rightsover the products of so-called ‘expressive AI’: Its programmers? Itsusers? Or the AI itself?
There is a discourse in who should profit off of AI generated music.