ENST-Drums: an extensive audio-visual database for drum signals processing
Bibliographic record
Abstract
The <strong>ENST-Drums database</strong> is a large and varied research database for automatic drum transcription and processing: Three professional drummers specialized in different music genres were recorded. Total duration of audio material recorded per drummer is around 75 minutes. Each drummer played his own drum kit. Each sequence used either sticks, rods, brushes or mallets to increase the diversity of drum sounds. The drum kits themselves are varied, ranging from a small, portable, kit with two toms and 2 cymbals, suitable for jazz and latin music ; to a larger rock drum set with 4 toms and 5 cymbals. Each sequence is recorded on 8 individual audio channels, is filmed from two angles, and is fully annotated A large part of ENST-Drums is publicly available <strong>under some conditions</strong>. These conditions include: The use and exploitation of the database should be limited to <strong>research</strong> purposes. No commercial use is possible. The database is distributed under the licence "Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)" Any document describing a research work where ENST-Drums was used should include a reference to ENST-Drums and to the paper <em>Olivier Gillet and Gaël Richard. ENST-Drums: an extensive audio-visual database for drum signals processing, In Proc of ISMIR'06, Victoria, Canada, 2006.</em> <strong>Acknowledgements</strong> We would like to thank: The 3 drummers: Louis Cavé, Bertrand Clouard and Frédéric Rottier. E. Thiévon (author) and Play Music Publishing (publisher) for the background accompaniment sequences. The authors wish to acknowledge the support of the French ministry of research (ACI-MusicDiscover project) and of the European Commission under the FP6-027026-K-SPACE contract.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".