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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Music and Audio Processing
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,031 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,031 works in the cohort · of 4,299,418page 4 of 21

Labels cover 2 of 1,031 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,031 of 1,031 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Toward Sound-Assisted Intrusion Detection Systems
Lei Qi, Miguel Vargas Martín, Bill Kapralos, Mark Green, Miguel Á. García-Ruiz
2007· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
fundno affunlabeled
The Networked Environment for Music Analysis (NEMA)
Kris West, Amit Kumar, Andrew J. Shirk, Guojun Zhu, J. Stephen Downie, Andreas F. Ehmann +1 more
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
Adaptive environment classification system for hearing aids
Luc Lamarche, Christian Giguère, Wail Gueaieb, T. Aboulnasr, Hisham Othman
2010· article· en· The Journal of the Acoustical Society of America· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
The Present, Past, and Future of Timbre Research
Kai Siedenburg, Charalampos Saitis, Stephen McAdams
2019· book-chapter· en· Springer handbook of auditory research· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
afffundno abstractunlabeled
Latent Timbre Synthesis
Kıvanç Tatar, Daniel Bisig, Philippe Pasquier
2020· article· en· Neural Computing and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
fundno affunlabeled
Antipattern Discovery in Folk Tunes
Darrell Conklin
2013· article· en· Journal of New Music Research· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
aboutno affunlabeled
A Novel Classification Method with Cubic Spline Interpolation
Husam Ali Abdulmohsin, Hala Bahjat Abdul Wahab, Abdul Mohssen Jaber Abdul Hossen
2021· article· en· Intelligent Automation & Soft Computing· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
ETS System for AV+EC 2015 Challenge
Patrick Cardinal, Najim Dehak, Alessandro L. Koerich, Jahangir Alam, Patrice Boucher
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Probabilistic models for melodic prediction
Jean-François Paiement, Samy Bengio, Douglas Eck
2009· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
The Music Listening Histories Dataset.
Gabriel Vigliensoni, Ichiro Fujinaga
2017· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Assistive music browsing using self-organizing maps
George Tzanetakis, Manjinder Singh Benning, Steven R. Ness, Darren Minifie, Nigel J. Livingston
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Artificial Neural Networks that Classify Musical Chords
Vanessa Yaremchuk, Michael R. Dawson
2008· article· en· International Journal of Cognitive Informatics and Natural Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Music-driven character animation
Danielle Sauer, Yee‐Hong Yang
2009· article· en· ACM Transactions on Multimedia Computing Communications and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations

How this was built: Screen · Findings · About