Summary
MulTTiPop is a new dataset of pop music segments paired with multitrack MIDI recordings, designed for evaluating automatic music transcription (AMT) models. It contains 572 segments totaling 3.5 hours of audio, spanning diverse genres and decades from the 1930s to the 2000s. The dataset was built by performing metadata-based matching on song segments from the Lakh MIDI and TheoryTab datasets, manually identifying an anchor beat between audio and MIDI, then applying beat tracking to the audio and warping the MIDI to match its tempo and timing. Researchers evaluated state-of-the-art AMT models on MulTTiPop and found substantial room for improvement, with the best model achieving only 38% Onset F1. The paper was posted to arXiv (2507.08753) in July 2025 by Nathan Pruyne, Benjamin Stoler, and William Chen.
Paper Overview
Field: Music AI
Authors: Nathan Pruyne, Benjamin Stoler, William Chen
Published: 2025-07-12
arXiv: 2507.08753
Abstract
We present MulTTiPop, a dataset of pop music segments and their associated multitrack MIDI recordings for the evaluation of automatic music transcription models. MulTTiPop contains 572 segments of popular music totaling 3.5 hours of audio, and contains songs from diverse genres and decades from the 1930s to 2000s.
To collect this dataset, we perform metadata-based matching on song segments from the Lakh MIDI and TheoryTab datasets, manually identify an anchor beat between the audio and MIDI, then use beat tracking on the audio and warp the MIDI to match its tempo and timing.
We evaluate state-of-the-art automatic music transcription models on MulTTiPop and find substantial room for improvement, with the best model achieving 38% Onset F1.
More details and sound examples of MulTTiPop are available on the project page linked from the paper.
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