Seeing Sound Dataset V1.0.0
Bibliographic record
Abstract
This is dataset contains the synthesized soundscapes and crowdsourced audio annotations that accompany the paper, M. Cartwright, A. Seals, J. Salamon, A. Williams, S. Mikloska, D. MacConnell, E. Law, J. Bello, and O. Nov. "Seeing sound: Investigating the effects of visualizations and complexity on crowdsourced audio annotations." In Proceedings of the ACM on Human-Computer Interaction, 1(1), 2017. https://doi.org/10.1145/3134664 which investigates the effects of soundscape complexity and sound visualizations on the quality and speed of annotations of sound events (i.e. start time, end time, sound class, and proximity). In this dataset, we varied the soundscape complexity along two dimensions: maximum polyphony (3 levels) and Gini polyphony (2 levels). Maximum polyphony is the maximum number of sound events that occurred simultaneously in the soundscape. Gini polyphony is a measure of the concentration of sound events. For each of the 6 (3 x 2) combinations of complexity levels, we synthesized 10 soundscapes using Scaper, each of which was 10 seconds long, for a total of 60 soundscapes. Each soundscape was annotated by 90 participants from Amazon's Mechanical Turk. Of these 90 participants, 30 were aided by waveform visualization, 30 were aided by a spectrogram visualization, and 30 did not have any visualization aid. For more details on how this data was collected, please refer to the paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.064 |
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".