When it comes to raw computational power, machines are well on their way. But, is teaching a machine to mimic creativity true creativity? What about the relationship between creativity and intelligence? Scientists in Germany have trained computers to paint in the style of Van Gogh and Picasso, and the computers’ images aren’t all that bad. “In a paper published last year, titled, “When Will AI Exceed Human Performance? Evidence from AI Experts,” elite researchers in artificial intelligence predicted that “human level machine intelligence,” or HLMI, has a 50 percent chance of occurring within 45 years and a 10 percent chance of occurring within 9 years.” Writing for the MIT School of Engineering Carolyn Blais writes If a real person were mixing it they might spot issues and take action to deal with any problems they may bring in the mixing process.ĪI isn’t about specifics, it’s about generalities. Most of the time the plugin will do a great job and some of the specifics won’t matter, but some times they will. However it won’t know it’s a 1970s MusicMan being played by a bass player who tends to play the third string harder than the rest and who has intonation problems on the 12th fret plus some rattle off one of the frets. It might also figure out from the timbre of the sound if you’ve used a pick or a finger style. It might be able to work out what kind of strings you have round or flats. It should be able to spot the key and tempo you are playing in, that’s easy. You put one on a track and it’s smart enough to know you’ve put it on a bass guitar track. To unpack this a little more, let’s use the example of AI-based mixing plugins. In other words, all AI based systems work in generalities not specifics. That’s not to say automated mastering is bad, in many cases it does a great job, but it’s working with rules, not exceptions. That’s why we don’t chat with Siri all day, in fact most of the time we are shouting at our AI services as we grapple with their shortcomings.Īutomated mastering is great for automating many of the audio processes mastering engineers undertake, but don’t confuse process with craft. However, as with any input based system, bad data in equals bad data out, there are limitations. AI uses machine learning to get better at what it does, the more data it has to work with and the more feedback it gets. It’s not going to sit down with you and have a chat about the tracks.ĪI features in all parts of our lives, be that Siri, Google or Apple Home devices. It’s a computer making its best guess about what to do with your music. Without wishing to state the obvious, it’s not a human. With those algorithms, it aims to deal with EQ, compression, and loudness, preparing tracks for final distribution either via online platforms or more conventional formats such as CD and vinyl. Given their track record for delivering great software, we have little doubt this service should offer an excellent result and prove to be popular, given their existing customer base.Īutomated mastering uses AI (artificial intelligence) to simulate the decisions made by a mastering engineer. More recently Plugin Alliance Founder, Dirk Ulrich, announced Mastering Studio, another AI-based mastering service. Short of time before the Grammys, Gwen Stefani’s production team used the web-based program LANDR to master the song “Make Me Like You.” Since the early days of LANDR it has grown from online mastering to offer a more complete package Ģ020: Collaboration tools & services marketplaceįor LANDR the big battle has always been to establish credibility amongst the pro community, latterly it has received more positive feedback and has been used on top projects. LANDR (pronounced lander) is the product of a Quebec-based AI company MixMagic founded in 2014. Perhaps the first significant online mastering process to arrive on the scene and make a splash is LANDR, suggested by some as the devil incarnate.
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