A new global survey by Deezer and Ipsos found that 97% of people cannot tell the difference between fully AI-generated and human-made songs. 9000 People from 8 different countries which were the United States, Canada, Brazil, the UK, France, the Netherlands, Germany and Japan. It looked at the public views on AI’s rise in music, transparency in labelling and exactly how fair AI music is for artists.
Half of those surveyed said they felt uncomfortable after realising they could not recognise which tracks were made by machines. About 71% said the results surprised them. Deezer said around 50 000 fully AI-generated songs are uploaded to its platform every day, making up 34% of all new music deliveries. The company said it now detects and clearly tags AI-generated songs so listeners can identify them easily.
Audiences, as per the survey, want to know exactly where AI is used with labels, and fair pay for musicians when their work is used to train AI models. Many listeners also want streaming services to be open about how they manage AI-made content and artist royalties.
Why Is It Hard to Spot AI Music?
The study confirmed that AI has officially reached a level where it can imitate human creativity almost perfectly. Most people could not tell the difference because AI now mimics emotional tones, harmonies and sound textures that feel natural. Deezer said the test tracks were created to be as realistic as possible.
Modern AI tools can produce convincing vocals, mix instruments professionally and generate catchy melodies. They use data from millions of songs to predict what sounds appealing to the human ear. The result is music that feels familiar but often lacks a personal story or emotional depth.
The more AI music keeps getting popular the more pressure streaming platforms face to protect artists’ rights and maintain transparency for listeners. Deezer’s tagging system is one of the first steps toward helping audiences understand what they are hearing.
10 Easy Ways to Tell If a Song Is AI-Generated (According to Imagine Art)
Imagine Art shared a step-by-step guide to help listeners spot machine-made tracks. The tips are designed for ordinary listeners rather than sound engineers, so its easier for anyone to recognise artificial music.
1. Listen for Loops
AI songs often reuse short sections repeatedly. If the track feels stuck in the same pattern without development, it could be machine-generated.
2. Check for Emotional Flow
Human-made songs usually rise and fall emotionally. If the track sounds flat and unchanging, it may lack the feeling that comes from a real artist.
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3. Look at the Song Structure
Most human songs follow a story: verse, chorus, bridge and ending. AI sometimes struggles with this and jumps around without direction.
4. Read the Lyrics Aloud
AI lyrics often sound odd or meaningless when spoken. They can rhyme but fail to express emotion or tell a story.
5. Notice Transitions
Changes between verses or choruses can sound too smooth, as if joined automatically. That mechanical precision is a giveaway.
6. Study the Vocals
Synthetic voices may sound too perfect. They often lack breathing, variation and emotional tone that come naturally to humans.
7. Check the Metadata
File details or credits can expose AI origins. If the composer is unnamed or software like Soundraw or Amper appears, it’s probably AI.
8. Use AI Detectors
Web tools such as DeepFake Music Analyser or AudioLDM Inspector can analyse patterns and rate how likely a song is machine-made.
9. Compare with Known AI Songs
Listen to examples from AIVA or OpenAI Jukebox. Once familiar with their sound, it becomes easier to recognise similar styles.
10. Think About Where It Came From
Tracks uploaded anonymously to SoundCloud or YouTube with no artist information are often AI experiments. Real artists tend to share some background or identity.
Learning to recognise AI-generated songs helps audiences appreciate real artistry. With all this tech, it’s important never to lose the plot just to abandon all the things that make humans who they are, all for the sake of innovation.
