AI Starts Listening: Testing Gemini as a Mixing Tool for Music Producers

For years, music producers and audio engineers have joked about the day artificial intelligence would be able to hear a record and tell them how to mix it better. In a recent episode of a new series called Learn With Firm, that idea moved closer to reality. The host, a hobbyist producer and audio enthusiast, tested Gemini’s ability to analyze full audio files and offer detailed mixing and mastering advice. What he discovered was not a replacement for human engineers, but something more practical, an AI that is clearly listening, identifying issues, and responding with advice that often mirrors real studio language.

The experiment started with a problem most producers know well. “When will AI be able to tell me how to mix my song better or just analyze it to get general advice,” the host asked, describing the creative roadblocks that come up during long sessions. Until recently, large language models relied on text descriptions or screenshots, which often led to vague feedback. Mixing requires sound, context, and nuance, and without hearing the music, even smart advice can miss the point.

Teaching AI to actually hear the music

What separates Gemini from earlier tools, according to the host, is its ability to accept full audio files. “You can upload a full audio file to Gemini and it will listen to it,” he explained, adding that the system does more than transcribe lyrics. Instead, it extracts musical and audio features from the track and converts them into internal data it can reason with. That shift allows the model to comment on tone, balance, frequency buildup, and clarity in ways that feel grounded in the sound itself.

To push the test further, the host built a simple prototype app that sends Gemini both the audio file and a full spectrogram image of the track. “It gets a double whammy of context,” he said, describing how the system hears the song while also seeing its frequency content over time. The goal was not perfection, but better feedback than a text based prompt could ever deliver.

For the first test, the host uploaded one of his own demo tracks, an atmospheric song recorded in a bedroom environment. He admitted the record had emotional weight but could be improved. “I love this song. I know it can be better,” he said, even though he could not fully articulate what needed fixing. Gemini’s response was long, technical, and surprisingly specific.

Specific advice and exposed weaknesses

Gemini described the track as stylistically strong before moving into critique. It flagged frequency masking in the low mids and a lack of transient definition in the low end, pointing to a dense buildup between roughly 200 hertz and one kilohertz. As the host read through the response, he paused to react. “These are high level things that I don’t think of when I’m putting together these demos,” he said, acknowledging that the AI was identifying real issues.

The suggestions went further, recommending side chain compression between the kick and bass, targeted EQ cuts to reduce muddiness, and boosts to help rhythmic elements cut through dense layers. At one point, the host laughed at how precise the feedback became. “It’s getting very specific,” he said, noting that the model referenced exact frequency ranges and mix relationships he would normally struggle to explain.

Not every suggestion landed cleanly. Some critiques challenged stylistic choices he made intentionally, including a thick wall of sound common in dream pop and lo fi inspired music. Still, he found value in the process. “It’s definitely listening and coming up with legitimate sound advice,” he said, adding that the feedback exposed weaknesses that could be improved with more detailed prompts. In his view, the tool works best as a second perspective rather than a final authority.

Can AI judge a professional mix

To test Gemini’s limits, the host uploaded a professionally mixed mainstream pop record without telling the system it was already a finished release. The results revealed both the promise and the flaws of AI driven analysis. Gemini still suggested improvements, noting issues with glue, separation, and low end balance, even on a polished track.

Rather than dismissing the feedback, the host used it to highlight a larger truth about mixing. “Everyone is going to have their own opinion on how they like something to sound,” he said, explaining that even experienced engineers often disagree. Gemini, like a human, was applying general engineering principles without fully understanding genre expectations or artistic intent.

By the end of the episode, the host was clear about where the technology stands. “It might not fully replace an engineer just yet,” he said, “but this is the beginning of that.” He imagined a future where producers could work inside Pro Tools or Ableton with an AI assistant open alongside their session, dropping in pieces of a record and getting feedback in real time.

For now, Gemini’s role is supportive, not dominant. It offers guidance, alternative perspectives, and technical language that can help producers move past creative blocks. As the tools evolve, that role may expand. What this experiment shows is simple but significant. AI is no longer just reading about music. It is starting to listen.