How Artificial Intelligence is Making Inroads in the Music Industry | Culture - Inside Science News Service

Algorithms for mixing and mastering audio are having a growing impact on what we hear, explains Katharine Gammon, freelance science writer based in Santa Monica, California.

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When a song plays on the radio, there are invisible forces at work that go beyond the creative scope of the writing, performing and producing of the song. One of those ineffable qualities is audio mastering, a process that smooths out the song and optimizes the listening experience on any device. Now, artificial intelligence algorithms are starting to work their way into this undertaking.

"Mastering is a bit of a black art," explained Thomas Birtchnell, a researcher at the University of Wollongong in Australia. "While it's not always clear what mastering does, the music comes back and it sounds better." Birtchnell, a musician himself, was intrigued when he heard about AI-based mastering services like LANDR that offer inexpensive alternatives to human-based mastering. Many younger and newer artists use LANDR to master tracks they are releasing to launch their careers (they offer a monthly service that costs $9 for four tracks). He decided to investigate AI's uses and trends of algorithm-based audio mastering in a new paper released in November.

The traditional way of audio mastering generally requires a room with specialized acoustics. A person can hear flaws in the music, such as issues in the spectral range or the stereo balance, and remove glitches, pops and crackles. "It's quality control," explained Birtchnell. They also add loudness, which is the idea of making the sound fuller. It's quixotically different from volume, he pointed out, "containing more presence and energy."...

Ryan Petersen, a Nashville-based producer and songwriter, played around with LANDR a few years ago and ultimately abandoned the service to return to human colleagues. He said that while the algorithm is technologically impressive, it fell short because it lacked a taste algorithm in the part of the software dedicated to creative learning. "They've basically said their engine keeps learning by looking toward songs that get uploaded into it -- but that means it's always looking toward the past," he said. "It's never looking into the future to see how to create the next cool thing."
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Source: Inside Science News Service