As AI music generation tools like ACE-Step emerge, musicians and producers are expressing clear preferences about how they want these technologies to integrate into their creative workflows. The recent release of ACE-Step, a self-described foundation model for music generation, has sparked discussions about the ideal role of AI in music creation.
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| An illustration of the ACE-Step framework highlighting its architecture and components involved in music generation |
Musicians Want AI as an Assistant, Not a Replacement
Musicians in the community are overwhelmingly expressing a desire for AI tools that complement their creative process rather than generate complete works. They want technologies that can fill specific gaps in production or enhance particular elements while leaving artistic control firmly in human hands.
As a musician, the things I want most from generative AI is: Being able to have the AI fill in a track in the song, but use the whole song as input to figure out what to generate. Ideally for drums this would be a combination of individual drum hits, effects and midi so I'm able to tweak it after generation.
This sentiment appears repeatedly throughout discussions, with users emphasizing their desire for AI to function as a creative assistant rather than a replacement. Many musicians want AI tools that can switch up baselines, recommend chord progressions, or suggest complementary instruments for their compositions - similar to how Adobe has implemented assistive AI in photo editing.
Current Limitations in Musical Expression
A significant concern raised by community members is that current AI music generators play it too safe, lacking the ability to push boundaries or challenge conventions in the way human artists do. Some users point out that even when prompted to create specific genres like Satanic Black Metal, the AI produces generic pop-rock instead.
This limitation extends to vocal styles as well, with users noting difficulties in getting AI systems to authentically reproduce African American Vernacular English (AAVE) in rap generation - despite rap's origins in Black culture. This highlights concerns about AI systems potentially sanitizing cultural expressions in music.
Musicians also note that AI-generated compositions often struggle with maintaining coherent structure over longer pieces, with progressions that meander aimlessly and eventually go to weird places.
Desired Features for Future AI Music Tools
Looking at community feedback reveals specific capabilities musicians are hoping for in future AI music tools. Many want the ability to input their keyboard playing and have AI transform it into different instruments with precise control over how it's played. Others desire tools that can analyze a vocal track and generate complementary accompaniment.
Several users mentioned existing tools that partially address these needs, such as Logic Pro's virtual drummers that can follow rhythmic timings from main instrument tracks. Services like Kits.ai and voice-to-instrument conversion tools are also gaining attention, though they currently require significant training time for quality results.
The community also shows interest in AI tools that could enable instrument-to-instrument conversions and provide musicians with the equivalent of 100 session musicians at my disposal to realize their creative visions.
As AI music generation continues to evolve, the message from musicians is clear: the most valuable tools will be those that enhance human creativity rather than attempt to replace it. The ideal AI music assistant would function as an intelligent instrument or collaborator that responds to human direction while providing technical capabilities beyond what the artist might personally possess.
For developers of systems like ACE-Step, focusing on these assistive capabilities rather than full song generation might better align with what musicians actually want from AI music technology.
Reference: ACE-Step
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| A screenshot of the GitHub repository for the ACE-Step project, highlighting its development and community engagement in AI music tools |