Three-time Grammy Award-winning mixing engineer Andrew Scheps worked on Adele’s 21RHCP Stadium Arcadium (both of whom won Grammys) and mixed for Black Sabbath, Metallica, Beyonce, Lady Gaga, Neil Diamond and man, many others. So when he shares his views, we want to listen – and he has some great advice for others, as well as some very interesting insights into emerging AI technology in this recent conversation with the Londoner. Institute for Contemporary Musical Performance (opens in a new tab).
Scheps’ perspectives come from an increasingly clear journey; “My job as a mixer is to shape it sonically and just try to amplify what’s great in what’s already there,” he recalls. “But I think the creative side is kind of deciding what things to highlight and then how to do it.”
“I think the journey comes from understanding more and more that that’s all that matters, and everything else along the way doesn’t matter,” he adds. “It doesn’t matter if it’s a good mix. If it sounds good, it’s great. And if it’s not, it doesn’t matter how good or bad it sounds. Nothing matters if this isn’t right and it’s not about the music.”
Scheps thinks it’s important to learn your production story; “who did what and who worked with whom.” This desire to learn from its predecessors led to a whole series of containment called Andrew talks to great people (opens in a new tab) who saw him in conversation with luminaries including John Leckie, Sylvia Massy and Joe Barresi.
“What was great was that a lot of them talked about their mentors,” Scheps says. “Even Al Schmidt [who won 20 Grammys for his work with artists before his death in 2021] talk about how it started, and it started in the 50s. So to be able to basically go back to the beginning of commercial record production and understand that history, and also how many branches there are. »
Scheps believes that learning who made not only your favorite records, but also other notable releases outside of your comfort zones, is important in any journey as a producer and engineer. This grounding has seen him work with top metal and pop bands. “It is extremely important to listen widely,” he notes. “When you’re younger you gravitate towards a certain genre of music because it’s very social – you’ve found your tribe and you love that music and you hate everything else. But as you get older it’s less of a social pressure, you can start analyzing what you like.”
The Scheps notes here will be traits that emerge – the things you like about music, like minor or major songs. And these can cross genres, as well as the things you hate. “When you realize that, you can really start exploring,” he says. And in this exploration, you will discover the details of production and mixing. But this anchoring of musical research is obviously only the beginning.
“You need to know your tools inside out,” adds Scheps. “If you’re in a concert that requires you to know Professional toolsyou can’t just say, “Well, I’m a Logic guys’. Because you’re there to use Pro Tools and you have to know it very, very well or else you’re just slowing down the creative process. Know the technical side so well that you can then be creative.
“The second thing is to try to work with people at the same stage of their career as you – and I’ve said this a million times before, but it’s really important. If you’re just starting out, find bands that are starting And then you’re learning together and everyone’s cool and they’re on the same page – there’s a lot of conversations you don’t have to have And you’re not going to be at the same time either home blame yourself for not getting a job you’re not actually ready for and nothing will make up for the training.
“I don’t know about the Malcolm Gladwell 10,000 hour thing – I don’t know if you can put a number on that, but the more you do the better you get. I always feel like I I’m not that good and the whole thing takes up a lot of my time and I completely miss things. When I listen the next day I’m like, ‘How could I let that happen?’ So it’s a lifelong thing. You’ll never be like, ‘Oh, I get it, now I’m fine. “There are people where it looks like that, but they’re just more talented and it’s really, really hard work. Don’t give that up and keep it in mine – spend as much time as possible doing the thing.
And in the face of all that hard work and years of hands-on learning, we now have emerging AI technology in production. Well Scheps has a reasoned answer to this which is very well considered.
“It’s always going to be very biased by the data that it’s trained on, so I think the technology and some of the software that’s been written is amazing – all the iZotope stuff…it’s really amazing but if you try extending it to things like songwriting…although there’s already K-Pop stuff going on in that area. [with] mixing, mastering – there are now many mastering sites online that are all based on algorithms, but what they’re based on is someone saying, “This is what good songs sound like when they’re mastered”. But what has always kept things going is what you’ve never heard before. Beck Odelay frightened people. AC/DC back in black scared people off, because that was new and that was what was great, and you’re never going to get that with machine learning. It’s impossible – it goes against what machine learning is. So I think some of the tools are really, really amazing, but I think if you let them do their thing, then the reality of “this thing can take your job” is true because then you just let it.
But Scheps isn’t dismissive of emerging machine learning technology — on the contrary, he believes it can help teach the mixers of tomorrow.
“Where I think it’s really helpful, especially for young people, is using something like [iZoptope] Ozone – let him listen to your track, suggest a bunch of things, then rip it. See what’s going on, every little thing, and try to rearrange things. See what you like and dislike and practice doing what he did. And it’s an amazing teaching tool, but as far as throwing a bunch of notes at a computer and having it make a record for you, I’d say it’s just going to be a bunch of boring records. It’s easy for me to talk about machine learning and overlook it, but when you think about the data set these things are currently trained on, they’re the entire output of everyone on the planet, because things could be recorded and documented.
“ChatGPT is an easy thing to reference because it’s right there everyone’s heard of it now and it’s exploded onto the scene,” Scheps offers. “But if you ask him to write an essay for you, it’ll be different. every time but it’s never come to a conclusion that no one else has thought of. That will never happen. So being able to generate music is really cool and I think it has great applications, like in the gaming world with composers – to be able to sow seeds of music and then generate a dynamic score as you play a game. The possibilities for this stuff are truly amazing, but if you think you’re going to get creative new results, I’d say there’s a bug. Because by definition, machine learning cannot create new things. It merges a gigantic amount of existing things.”
Then he smiles; “And I would be wrong!”
Check out the full interview at the top of the page.