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AI Music is exploding. In fact, you're probably listening to AI generated music and don't even realise it. But rest assured, the musicians whose beats, rhymes and livelihood were stolen to train the models are well aware.
They're reminded every time they check their empty bank accounts.
99,000 new songs are uploaded to streaming platforms every day. According to Deezer, almost one in five are now made by our artificial friends.
You wouldn't play a single one at your funeral.
And it gets worse.
According to Ditto music, 59% of musicians use AI in some aspect of their music.
It's a highway to hell.
How do the real musicians get paid?
How do the record labels keep track of their... tracks?
What about session musicians, producers, songwriters and the bass player?
How do they all get their fair reward?
And how do you prove their input in the output of a model?
Yes, it is all very very very difficult.
Thankfully, amongst the madness, the always excellent Cherie Hu, Yung Spielburg and Alexander Flores of Water & Music researched and wrote about what's at stake, how technology can be used to solve the riddle and which companies are trying to shake that moneymaker.
We read their research.
Please share with a music lover.
Cheers
Mark and Jeremy
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(00:00) The Intersection of Music and AI
(03:26) Understanding Music Attribution
(03:51) Sonic Characteristics and AI Influence
(06:39) The Complexity of AI Music Generation
(07:36) The Value Equation in AI Music Creation
(08:08) Understanding Influence Functions in Music AI
(09:44) Challenges of Attribution in AI-Generated Music
(11:38) Exploring Embeddings and Their Role in Music AI
(14:17) Watermarking and Its Limitations in Music Attribution
(15:30) Synthetic Data and Its Implications for Music AI
(17:48) Innovative Solutions for Music Rights Attribution
(18:01) Distinguishing Compositional vs. Recording Contributions
(19:59) The Impact of AI on the Music Industry's Inequities
(23:03) Trust and Technology in Music Attribution
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