Bibliographic record
Abstract
SASS-E is an audio dataset containing 13,313 one-hit audio samples from three distinct steelpans. Citation This dataset is part of a paper published in the NIME 2023 proceedings. It is available here. If you use this dataset in a research project, we request that you cite this paper: C. Malloy and G. Tzanetakis, “Steelpan-specific pitch detection: a dataset and deep learning model”, in Proceedings of the International Conference on New Interfaces for Musical Expression (NIME), May 2023, pp. 428--435. doi: 10.5281/zenodo.11189232. Data curator Colin Malloy Contact You can contact Colin regarding this dataset at malloyc@uvic.ca. About SASS-E The SASS-E dataset is an audio dataset curated as part of Colin's PhD research. It consists of over 13,000 one-hit audio samples from three tenor steelpans totaling over 9 hours and 25 minutes of audio. The samples were recorded in a professional quality recording studio at 48 kHz/ 24-bit depth. Approximately 50 strikes were recorded per note per instrument at a wide variety of dynamic levels and beating areas. This allows for comprehensive coverage of minute details and fluctuations in timbre. The audio samples are pre-split into training, validation, and test sets with 7,931 samples in the training set, 2,680 samples in the validation set, and 2,702 samples in the test set. The audio files have filenames in the following format: _ _ _sample_ .wav. They are each labeled with the MIDI note number for the given note struck. The instrument label is in the format "ctenor-0x" where x is the number assigned to the instrument. Future of SASS-E In the future, we plan to add samples from a wider variety of steelpans. The goal is to create a comprehensive dataset that covers all major varieties of steelpans from the soprano to bass voices of the family. This will include other kinds of tenor steelpans (such as low D tenors, Invader tenors, and 3rds/4ths tenors). License All of the samples in SASS-E are released under Creative Commons (CC) CC-BY-NC. This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. Specifically, these samples are not to be used in a commercial sample library (such as a virtual instrument).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.058 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".