MulTTiPop: A Multitrack Transcription Dataset for Pop Music
A dataset to test whether AI can accurately transcribe pop music into notes
Researchers created MulTTiPop, a collection of 572 pop music segments with matching digital note-by-note transcriptions, to measure how well AI systems can automatically convert recorded music into written musical notation. When tested on the best existing AI models, the results showed significant room for improvement—the top performer only correctly identified 38% of note onsets, the moment each note begins.
Automatic music transcription is a key step toward AI that can analyze, remix, and understand recorded music. This dataset gives researchers a reliable way to measure real progress on the problem. Better transcription systems could speed up music production, help musicians learn songs by ear, and improve music search and recommendation tools.