Papers › Evaluation of CNN-based Automatic Music Tagging Models

Evaluation of CNN-based Automatic Music Tagging Models

1 Jun 2020arXiv:2006.00751links table onlyarchive 2025-07-28

Minz Won, Andres Ferraro, Dmitry Bogdanov, Xavier Serra

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Recent advances in deep learning accelerated the development of content-based automatic music tagging systems. Music information retrieval (MIR) researchers proposed various architecture designs, mainly based on convolutional neural networks (CNNs), that achieve state-of-the-art results in this multi-label binary classification task. However, due to the differences in experimental setups followed by researchers, such as using different dataset splits and software versions for evaluation, it is difficult to compare the proposed architectures directly with each other. To facilitate further research, in this paper we conduct a consistent evaluation of different music tagging models on three datasets (MagnaTagATune, Million Song Dataset, and MTG-Jamendo) and provide reference results using common evaluation metrics (ROC-AUC and PR-AUC). Furthermore, all the models are evaluated with perturbed inputs to investigate the generalization capabilities concerning time stretch, pitch shift, dynamic range compression, and addition of white noise. For reproducibility, we provide the PyTorch implementations with the pre-trained models.

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minzwon/sota-music-tagging-models officialmentioned in papermentioned on GitHubpytorchMIT report
Dohppak/Music_DeepEmbedding_Extractor mentioned on GitHubpytorch report
HephaestusProject/pytorch-FCN mentioned on GitHubpytorch report
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minzwon/tag-based-music-retrieval mentioned on GitHubpytorchMIT report
pxaris/ccml mentioned on GitHubpytorchApache-2.0 report

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Music Auto-Tagging MagnaTagATune (clean) Short-chunk CNN + Res PR-AUC 46.14 #1 of 3 Archive leaderboard report
Music Auto-Tagging MagnaTagATune (clean) Short-chunk CNN + Res ROC-AUC 91.29 #1 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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