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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 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]
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“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]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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
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Wang, L., Liu, H., Zhang, X., Zhao, S., Guo, L., Han, J., & Hu, X. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.843988 · Cited by 5
Metadata
- 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
- Social Sciences › Arts and Humanities › Music › 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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