Spotting AI-Generated Songs: What Listeners and Creators Should Know
Artificial intelligence is reshaping the way music is created and distributed. Platforms like Suno and Udio allow anyone to produce complete tracks from a short text prompt, generating melodies, lyrics, and even full vocal performances. For casual listeners, this may not matter at all. But for others, knowing whether a song is machine-made or human-created feels increasingly important. As the technology develops, it’s becoming harder to tell the difference with a simple listen.
Checking the Artist’s Footprint
One of the most reliable ways to investigate is by looking at the artist’s presence beyond the song itself. Established musicians usually have signs of real-world activity: social media accounts, live show footage, label releases, or interviews. If those traces are missing, the project may be purely synthetic.
A recent viral example is the band Velvet Sundown, which rose quickly on streaming charts before listeners began questioning their authenticity. The group’s limited online presence and AI-like promotional material led many to suspect the project wasn’t backed by real musicians. Platforms later flagged their music as AI-generated, underscoring how background research can reveal more than the sound itself.
Photo Credit: Velvet Sundown
Platform Transparency
Some streaming services are beginning to take on the task of labeling AI music directly. Deezer, for instance, has introduced technology to detect the unique patterns common to AI production. When a track is flagged, the app adds a visible “AI-generated content” tag. The company reports that nearly one in five daily uploads now fall into this category, a figure that highlights how quickly synthetic music is entering circulation.
Emerging Detection Tools
Beyond streaming platforms, new detection services are being developed to help identify AI-generated songs. IRCAM Amplify, a subsidiary of the French music research institute IRCAM, offers a tool that analyzes uploaded audio files and provides a probability score of AI involvement. Early testing shows strong accuracy with some generators, but misclassifications still happen. These tools can be useful indicators, but they should not yet be treated as conclusive proof.
Reading the Lyrics
Lyrics can also reveal clues. While some creators provide their own words, many rely on AI-generated text, which often leads to predictable patterns. Repetitive rhyming, awkward phrasing, or oddly common word choices can be giveaways. Listeners have noted that words like “neon,” “shadows,” and “whispers” appear unusually often in AI-penned lyrics. This isn’t foolproof, human writers use these words too but in context, it can raise questions about a song’s origin.
The Challenge of Certainty
Despite these methods, there is no guaranteed way to identify AI-generated music. Earlier versions of generative tools left behind audio artifacts that could be spotted by ear, such as unusual reverbs or unnatural vocal tones. But as the models continue to improve, those markers are becoming less reliable. As Manuel Mousallam of Deezer points out, distinguishing human-made tracks from AI-generated ones is only getting more difficult.
Final Thoughts
The rise of AI in music is forcing both listeners and industry professionals to rethink questions of authenticity, transparency, and authorship. While background research, platform tags, detection tools, and lyrical analysis can all provide valuable insight, none of them offer complete certainty. For now, the best approach is a combination of these methods, along with an awareness that the boundaries between human and machine creativity are becoming increasingly blurred.
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