Evaluation Of Algorithms Using Games: The Case Of Music Tagging.
Open accessAbstract
Search by keyword is an extremely popular method for retrieving music. To support this, novel algorithms that automatically tag music are being developed. The conventional way to evaluate audio tagging algorithms is to compute measures of agreement between the output and the ground truth set. In this work, we introduce a new method for evaluating audio tagging algorithms on a large scale by collecting set-level judgments from players of a human computation game called TagATune. We present the design and preliminary results of an experiment comparing five algorithms using this new evaluation metric, and contrast the results with those obtained by applying several conventional agreement-based evaluation metrics. 1.
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The models are trained with the MagnaTagATune dataset (19k training songs) and the Million Song Dataset (200k training songs).
“The MTT models are trained with the MagnaTagATune dataset, and the MSD models are trained with the Million Song Dataset.”
From musicnn: Pre-trained convolutional neural networks for music audio tagging (2019) · cites this paper as [3]
Section 2.1.1, “The Dataset” (p. 388), says the participating algorithms were trained on a subset of the TagATune dataset. It reports 16,289 training music clips, not 19,000 songs, and does not address models trained on the Million Song Dataset or a 200,000-song training set. (LLM, conf=0.87)
Experimental — accuracy not yet measuredMTT consists of 26k music clips from 5,223 unique songs including genre, instrument, vocal, mood, perceptual tempo, origin, and sonority features.
“MTT [31] consists of 26k music clips from 5,223 unique songs including genre, instrument, vocal, mood, perceptual tempo, origin, and sonority features.”
From LP-MusicCaps: LLM-Based Pseudo Music Captioning (2023) · cites this paper as [31]
Section 2.1.1, “The Dataset” (p. 388), describes a subset of the TagATune dataset, not MTT. Searches for “MTT,” “5,223,” and the listed feature categories found no passage in our copy establishing MTT’s size or contents. (LLM, conf=0.76)
Experimental — accuracy not yet measuredPrevailing methodologies for contemporary musical descriptions typically lean on discriminative learning.
“In the realm of Music Information Retrieval (MIR), prevailing methodologies for contemporary musical descriptions typically lean on discriminative learning.”
From MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response (2024) · cites this paper as Law et al., 2009
The Introduction (p. 387) describes music-tagging algorithms and conventional agreement-based evaluation, but does not say that prevailing methods rely on discriminative learning. Searches for “discriminative learning” and related descriptions of classification methods found no passage establishing that claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Hou, S., Liu, S., Yuan, R., Xue, W., Shan, Y., Zhao, M., & Zhang, C. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890309 · Cited by 6 · see citing works
Combining Sources of Description for Approximating Music Similarity Ratings
Wolff, D., & Weyde, T. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37425-8_9 · Cited by 6 · see citing works
Alonso-Jiménez, P., Favory, X., Aarabi, H. F., Bourdalas, G., Serra, X., Lidy, T., & Bogdanov, D. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095058 · Cited by 6 · see citing works
Learning contextualized semantics from co-occurring terms via a Siamese architecture
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Zero-shot Learning and Knowledge Transfer in Music Classification and TaggingPreprint
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Joint Music and Language Attention Models for Zero-Shot Music Tagging
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Time Signature Detection: A Survey
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Exploring Hierarchical Auditory Representation via a Neural Encoding Model
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- Authors
- Law, E., West, K., Mandel, M. I., Bay, M., & Downie, J. S.
- Affiliations
- Carnegie Mellon University (US)
- University of Illinois Urbana-Champaign (US)
- Columbia University (US)
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2009-10-26
- Pages
- 387-392
- OpenAlex
- W2127870748
- Cited by
- 168
- References
- 3
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 20 · 2023: 15 · 2022: 17 · 2021: 15 · 2020: 22 · 2019: 13 · 2018: 11 · 2017: 9 · 2016: 8 · 2015: 1 · 2014: 4 · 2013: 6 · 2012: 3
- FWCI
- 8.40
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- other-oa
- Locations
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (other-oa)
- International Symposium/Conference on Music Information Retrieval
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Music Technology and Sound Studies
- Keywords
- music tagging, algorithm evaluation, crowdsourcing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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