One common complaint about pop music is that it all sounds the same.

The beats are generic, or the lyrics are cliched. Tracks lean on the same tropes: love won, love lost, proclamations of wealth and fame.

There is some merit to this argument, says Michael Mauskapf, a musicologist and sociologist who teaches at Columbia Business School.

"So, to get into the [US] Hot 100, to be part of that popular subset, you need to sound pretty normal," Professor Mauskapf tells the ABC.

He and a colleague, Noah Askin, did a study recently that tried to figure out what makes music successful.

They did it by looking at data collated by Echo Nest, a company owned by Spotify, breaking down 27,000 songs that appeared in the US Hot 100 over the past 60 years by how fast they were, how danceable and how reliant on acoustic sounds, among other measures.

And while they did find some homogeneity, they also found the opposite.

The Australian AI firm Popgun is based in an office that formerly housed the record label Warner Music. ( Double J: Paul Donoughue )

"For most songs, once you are into the Hot 100, once you are into that popular realm, you want to distinguish yourself as much as possible," Professor Mauskapf says.

The researchers were pleased to discover that scoring a hit song is not about how much money your record label puts behind you or how many followers you have on Instagram.

You need to do something new, something that helps you stand out, and it's not easy to say what that should be.

"Our analysis suggests that, yes, you can certainly listen to what's around you and try to match it and that will certainly serve you well, but there is an element of randomness and an element of art — for lack of a better word — that means you can't just scientifically determine what is going to become a hit."

There's a kicker in that last comment from Professor Mauskapf: the "scientifically" part.

What deep learning might mean for music production

Right now, there are companies developing software they say will help you write better songs, even hit songs.

"There is quite an emerging body of activity around commercial music production with AI," Dr Oliver Bown, a senior lecturer and researcher in computational creativity at UNSW, said.

They do this by feeding heaps and heaps of data into a computer program to teach it about music, to the point where, eventually, it can whack out a tune on its own.

A team at Google created AI Duet, software that will jam with you.

Popgun is using deep learning to build software that aids musicians. ( Double J: Paul Donoughue )

Scientists at Sony's CSL Research Lab went one step better, producing a whole song — in the vein of The Beatles — using AI, though with a little help from their friends (a human composer).

In Australia, a company called Popgun is using AI to develop software that it hopes will make music composition easy.

Popgun was co-founded by former Twitter employee Stephen Phillips and lists entrepreneur Graeme Wood as an investor.

The company uses a form of deep learning called unsupervised learning, where the AI learns the various features of songs — how a scale or a harmony works, for example — just by studying enough of them.

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"Everybody considers music to be a very human activity, and when they say that, it's because they can't explicitly explain the rules of what makes music good, or why they like it," Phillips says.

Unlike in Go — the board game where man was famously beaten by computer, a high-water mark in the world of machine learning — in music, there is no clear winner or loser.

There are some notes that sound bad in a progression, but lots that sound fine.

"The biggest problem ahead of everybody in this space is this subjective function of what makes a good song," Phillips says.

"We can't tell the difference between good and great. We sit here and generate 50 songs. We sit and listen to them and every now and then one is a standout and we don't understand yet why, or how, or how to make it that every time."

Phillips says the technology for getting machines to write music is developing quickly, with Popgun hoping to make a product announcement early next year.

Dr Bown agrees, saying there is a clear role for AI to generate a wide range of ideas, after which, "the human does what humans do best, which is curate and apply taste".

Phillips admits to some trepidation within the music business about what this will mean. (The irony of the fact his company now occupies the former Warner Music office in Brisbane is not lost on him.)

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Will the technology aid musicians, or take their jobs? Will this be the next Napster, laying waste to the music business just as it is starting to recover?

"It's not like it's driverless cars where there are drivers out of work," he says.

"Music is a social construct with people wanting to say something. And this is just going to let more people say it better than they have said it before."

That brings us back to the charts, popular music, and homogeneity

If everyone can use software to produce music designed to be a hit, will all music start to sound the same?

Professor Mauskapft says his research suggests, even with the ability of machine learning to analyse so much popular music and work out what makes it popular, you can't be guaranteed success in music.

"Will AI be able to develop a hit? I think it is certainly possible, in the future," he says.

"But I think you could reverse engineer a hit, but you can't reverse engineer the hit again and again. It's a moving target, and there is some element of semblance of art in that.

Popgun counts Wotif.com founder Graeme Wood as an investor. ( Double J: Paul Donoughue )

"In certain genres and certain time periods, people actually want something a little bit different."

It also raises a question of authorship, one that comes up a lot in discussions about artificial intelligence and creativity. If a computer program helped you pen a song calculated to be a chart-topper, can you really claim credit?

Mr Phillips thinks so.

AI is increasingly becoming a part of various kinds of creative practice, from poetry to digital media work, and some artists see it as a tool not unlike the camera or paintbrush.

"I don't see the algorithm owning anything," Mr Phillips says.

"I think it is going to make high-quality composition more accessible to more people."