Automatic music transcription models still struggle with real-world pop music—the best model only achieves 38% Onset F1—suggesting this dataset will be valuable for developing better transcription systems.
MulTTiPop is a dataset of 572 pop music segments (3.5 hours) paired with multitrack MIDI transcriptions, spanning from the 1930s to 2000s. The authors created it by matching audio from existing datasets, manually aligning beats, and using tempo warping. They benchmark state-of-the-art transcription models and show significant room for improvement.