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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.

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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 1 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
Multimodal fusion for multimedia analysis: a survey
Pradeep K. Atrey, M. Anwar Hossain, Abdulmotaleb El Saddik, Mohan Kankanhalli
2010· article· en· Multimedia Systems· Computer Science
machine prediction:candidate · noneconsensus · none
1,226
citations
affunlabeled
How to Construct Deep Recurrent Neural Networks
Razvan Pascanu, Çağlar Gülçehre, Kyunghyun Cho, Yoshua Bengio
2014· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
582
citations
afffundno abstractunlabeled
Trends in audio signal feature extraction methods
Garima Sharma, Kartikeyan Umapathy, Sridhar Krishnan
2019· article· en· Applied Acoustics· Computer Science
machine prediction:candidate · noneconsensus · none
467
citations
affunlabeled
Audio Chord Recognition With Recurrent Neural Networks.
Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent
2013· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
145
citations
affno abstractunlabeled
Musical Timbre Perception
Stephen McAdams
2012· book-chapter· en· Elsevier eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
101
citations
affunlabeled
High-dimensional sequence transduction
Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
96
citations
affvenueunlabeled
Neural Network Music Genre Classification
Nikki Pelchat, Craig Gelowitz
2020· article· en· Canadian Journal of Electrical and Computer Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
affunlabeled
Counterpoint By Convolution.
Cheng-Zhi Anna Huang, Tim Cooijmans, Adam P. Roberts, Aaron Courville, Douglas Eck
2017· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
80
citations
affunlabeled
Steerable, Progressive Multidimensional Scaling
Matt Williams, Tamara Munzner
2005· article· en· IEEE Symposium on Information Visualization· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
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