How to Use ChatGPT for Genre Tagging Your Music

If you’ve ever uploaded your track to a streaming platform or sent it to curators, you know the challenge: genre tagging isn’t as simple as it looks. Whether you’re filling out metadata for SoundCloud, a music video on YouTube, pitching to a playlist, or organizing your catalog, the “genre” field can make a big difference in how your music is discovered. Yet, when you turn to AI genre analysis platforms — such as SubmitHub, Sonoteller, Audio AI Dynamics, Cyanite.AI, or Bridge.Audio — you may find their results don’t always agree.

So why do five AI systems hear the same song five different ways?

Why AI Genre Analyses Diverge

At first glance, it might seem odd that one platform calls your track electropop, another future garage, and another downtempo electronica. But this variation actually reveals how differently these tools “listen.”

Each platform uses its own combination of training data, feature extraction, and genre taxonomies — three technical ingredients that determine what the system hears and how it labels it:

1. Training Data Bias
   Every AI model is trained on a corpus of labeled tracks. If that corpus leans heavily on certain genres or historical styles (say, Spotify playlists or Beatport charts), the system’s sense of what “techno” or “trap” means will reflect those contexts. Another model trained on radio data or indie submissions may emphasize completely different sonic attributes.
2. Feature Extraction Differences
   AI models “hear” music by turning audio into numbers — analyzing spectral features, rhythmic patterns, harmonic complexity, and timbral fingerprints. One system may focus more on beat structure and tempo; another might weight harmonic density or vocal tone. If your song blends stylistic boundaries (as much modern production does), the algorithm’s weighting of those features can pull it toward different genre labels.
3. Taxonomy and Label Granularity
   Some AIs use coarse labels (pop, rock, electronic), while others use very fine-grained taxonomies (dream pop, post-EDM, lo-fi house, indie electronic). These hierarchies aren’t standardized across the industry, so what one platform calls “synthpop” might sit inside “indie pop” in another system. The more granular the taxonomy, the greater the chance for disagreement.

Why Large Language Models Offer a Useful Alternative

If your goal is clarity rather than classification research, sometimes the best way to tag your music is through a language model conversation rather than an audio upload. ChatGPT, Claude, or similar LLMs can act as knowledgeable, flexible genre advisors.

Instead of relying on waveform analysis, an LLM works through semantic reasoning — connecting your descriptions of the track (its mood, instrumentation, BPM, vocal treatment, and influences) to its likely genre ecosystem. For example, you could tell ChatGPT:

“This song is electronic, mid-tempo around 110 BPM, with lush female vocals, chopped vocal textures, and shimmering synth pads. It feels dreamy and cinematic but still has a pop structure.”

A language model will cross-reference those cues with its learned knowledge of music culture — often identifying a cluster of related genres (dreampop, synthwave, electro-pop, cyberpop, chillwave, etc.). This conversational process can help you triangulate how curators, algorithms, and fans might perceive your sound.

The advantage is interpretive flexibility: you can refine your tags interactively (“make it more Spotify-friendly,” “narrow to microgenres,” or “suggest DJ-relevant tags”). The model’s suggestions won’t always be definitive — but they’ll be informed by cultural context rather than rigid feature extraction, helping you find the right balance between artistic accuracy and market visibility.

Putting It All Together

AI waveform analysis tools and language models serve different purposes. Platforms like Cyanite or Audio AI Dynamics are valuable for objective, signal-based classification — especially if you want to compare your mix to genre archetypes. ChatGPT or Claude, on the other hand, excel at contextual reasoning, helping you articulate where your track belongs in the broader cultural and industry landscape.

For best results, you can try both approaches: let the machine-listening platforms give you data-driven labels, then consult an LLM to interpret those results, consolidate overlaps, and choose the most resonant tags for your metadata, playlists, or press materials. A typical issue that arises is that online platforms typically don’t allow unlimited analyses, or will want to charge a fee for use of their platforms. So a chatbot AI may end up being the most cost-effective approach in the long term.