From Melody to Masterpiece: Exploring the Power of AI Music Generator Tools

Jason Dookeran
5 min readAug 4


Photo by Matt Botsford on Unsplash

AI music generator tools are among the newest utilities in the arsenal of music producers today. Bedroom Producers Blog found in a survey of over 1500 music producers, 30.1% of those were planning to use AI tools to speed up their music production. AI has led to a rift in the music production community, with some advocating AI as a tool to help music production while others see it as a threat to originality. No matter which side of the fence one is on, there’s no question that AI music generation tools will change the industry irreversibly. Here, we’ll look at the transformative power of these tools and their role in shaping the future of music creation.

Understanding AI Music Generation Tools

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AI production tools all work on the same principle, regardless of if they generate art or music. At the core of these innovations is a deep learning model. Deep learning models operate similarly to the human mind, processing data to retrieve patterns that make sense out of nonsense. In the case of AI music generator tools, they learn from being fed vast amounts of existing data in the form of music.

According to Nature-Inspired Computation and Swarm Intelligence, music generation AI models can fall into one of three categories, namely:

  • Probabilistic approaches that rely on implicit or explicit pattern recognition from data
  • Nonadaptive methods that rely wholly on human beings to develop structure from the generated data and
  • Evolutionary methods that adapt and respond to user interaction

Some openly available AI music generators include OpenAI’s Musenet, Brain FM, AIVA Technologies, and Soundful. Some of these generation sites come with an attached fee based on what you generate, and the copyright for the generated material may vary by provider.

The Creative Process with AI Music Generator Tools

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Professionals that utilize AI music creation tools have a unique creative process. According to tech writer and pop punk band member Claire L. Evans, the creative process using AI offers something that artists have been doing for years — coming up with a base idea and riffing on it.

In the case of traditional artists, something as simple as Beethoven could lead to something as eponymous as Smoke on the Water simply by being inverted. However, the experience of an artist working on a piece of music may not include knowledge of Beethoven’s 5th and make it impossible for that artist to even come up with the inversion.

AI offers a broader range of experiences for artists to experiment with. It’s a lot more structured than randomly generating a chord progression and coming up with ideas to fit on top of it. But essentially, it’s the same process, albeit fueled by a deep learning engine.

Impact on Music Production and Collaboration

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The industry is divided on whether AI music is creative or derivative. On one side of the equation are proponents of a non-AI approach where music should be organically generated as a process of the human mind. In the other camp are producers who see AI as a brilliant way to rapidly prototype tracks and create something that sounds good with minimal effort.

In the middle of the debate are the majority of producers who see AI as a great place to start a composition. The way AI learns makes it inherently susceptible to repetition and plagiarism. However, using it for a commercial music production opens the door to potential lawsuits or claims of a dull, derivative composition.

AI music generation tools democratize music production, making it easier for anyone to create music, even if they don’t know the basics of music theory. It also helps to enhance the existing creativity of producers by giving them a way to prototype something they can refine later.

The Future of Music Creation and AI

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One of the most recent “wins” that AI has managed is the song “Heart on My Sleeve,” which uses AI-generated copies of artists The Weeknd and Drake. This approach may be the first in a long line of AI-generated music that may even surpass the original artists.

Imagine, for a moment, having new music by artists like Kurt Cobain, Bob Marley, and Freddie Mercury, all from an AI engine that closely mimics their style and tone. For some music enthusiasts, this seems like a dream come true. For others, it looks like a dystopian nightmare.

With AI being able to generate lifelike productions from these artists, what incentive is there for new artists to enter the realm of music? We’re already inundated by more music than we can possibly listen to. Adding more music from the old masters might just drive new bands under since they can’t compete with such a well-established brand. As for the estates of these deceased musicians, who gets the royalties for their voice and likeness? Would they even be entitled to royalties since AI was responsible for generating these new songs?

AI music generation tools carry a substantial ethical burden, which still needs to be adequately explored and understood. Ownership of music produced by these engines usually falls to the user. But who pays for the music that the engine was trained on? Aren’t the artists of the initial training material due some part of the profits for using their work?

AI music generator utilities will continue to improve with time. Producers already use them to get a starting point they can work from. They may replace artists in the future or serve as a way to carry on their work after they pass on. But in such a case, the ethics of these generation tools must be appropriately dealt with.



Jason Dookeran

Freelance author, ghostwriter, and crypto/blockchain enthusiast. I write about personal finance, emerging technology and freelancing