Can We Teach a Machine to Sing?

If their claims are true, this service could fundamentally change the way music is created and delivered for a whole variety of background tasks from restaurant ambient noise to the quintessential elevator music.

Of music streaming services, the premise of an always-available, never hung-over, and infinitely scalable band seems like a godsend to those in the music industry who are trying to help navigate it through its digital transformation safely.

The core premise with their model is that much of the expertise that studio techs bring to music production process is routine and automatable through machine learning.

In theory, leveraging LANDR’s platform would not only widen the margin creative artists could make from the sale of their music, but it also could open up a much wider array of independent or part-time musicians to the world of professional music – in a way, increasing the overall creative mix of content in the world by removing the middle-men between creativity and profit.

As Amper pointed out, there are plenty of use cases where music does not have to be the absolute best creatively – think studying music for example.

The real question, of course, is what value do music customers place on the act of creativity that goes into making a hit song? Will a machine ever be capable of drawing the same types of crowds as Taylor Swift or The Weeknd? The companies innovating in this space will ultimately be beholden to their customers, who will decide with their wallets over time.

While music is an art form that touches us perhaps more emotionally and more frequently than many others within the liberal arts, there are many more fundamentally pivotal human activities that make up today’s knowledge economy.

This article was summarized automatically with AI / Article-Σ ™/ BuildR BOT™.

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1st attempt 🙂 ariana grande lovin livin

https://github.com/MattVitelli/GRUV for starts

wavenet singing synthesis

 

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