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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. He decided to investigate AI's uses and trends of algorithm-based audio mastering in a new paper released in November.

澳大利亚伍伦贡大学的研究员托马斯·伯奇内尔(Thomas Birtchnell)解释说:“母带处理是一种黑色艺术。”“虽然并不是很清楚掌母带处理起什么作用,但音乐又回归了,而且听起来更好。”Birtchnell是一个音乐家,当他听说了基于人工智能的母带处理服务,比如LANDR,它为人类母带处理提供了廉价的替代方法,从而引起了他的兴趣。在11月发布的一篇新论文中,他决定调查人工智能在基于算法的音频处理方面的用途和趋势。


"In the space of music creation, I think that mastering is one of the more cut-and-dried practices that can be formalized relatively easily." Mastering is still creative, and humans can hear things that programs can't. But some aspects of mastering -- like equalizing the loudness levels of different songs on a CD or trying to match the spectral content in bass and high frequencies -- are a lot simpler to automate than composing a piece of music or doing music production.


"Maybe this is some indication of AI in creative practice, and I really think it is, but I think it's a long way from creative work -- even though there can be creative aspects," said Dannenberg.


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."

Ryan Petersen是纳什维尔的制作人和词作者,几年前曾和LANDR合作过,最终放弃了这项服务,回到了人类同事身边。他说,虽然这种算法在技术上令人印象深刻,但它的不足之处在于,在软件中专门用于创造性学习的部分缺乏一种趣味算法。他说:“基本上他们的引擎通过查找上传的歌曲来不断学习,但这意味着它总是在寻找过去。”“它从来不展望未来,去看如何创造下一个很酷的东西。”


Birtchnell believes the creative takeover by AI will eventually occur in the future. "As the algorithms improve, there is scope to start impacting on professional developers," he said. "So we might see in the future a tipping point where AI is on par with people, similar to white collar work where surgeons are replaced by robots, or driverless cars on roads. It always seems to be very soon but we don't know yet when it will happen."


Reprinted with permission from Inside Science, an editorially independent news product of the American Institute of Physics, a nonprofit organization dedicated to advancing, promoting and serving the physical sciences.

《科学内幕》是美国物理学会(American Institute of Physics)的独立新闻产品,是一个致力于推动、推广和服务物理科学的非营利组织。