Quantitative Soundscape Analysis to Understand Multidimensional Features
Bibliographic record
Abstract
A methodology for the analysis of soundscapes was developed in an attempt to facilitate efficient and accurate soundscape comparisons across time and space. The methodology consists of a collection of traditional soundscape metrics, statistical measures, and acoustic indices that were selected to quantify several salient properties of marine soundscapes: amplitude, impulsiveness, periodicity, and uniformity. The metrics were calculated over approximately 30 h of semi-continuous passive acoustic data gathered in seven unique acoustic environments. The resultant metric values were compared toa prioridescriptions and cross-examined statistically to determine which combination most effectively captured the characteristics of the representative soundscapes. The best measures of amplitude, impulsiveness, periodicity, and uniformity were determined to be SPLrms and SPLpk for amplitude, kurtosis for impulsiveness, an autocorrelation based metric for periodicity, and the Dissimilarity index for uniformity. The metrics were combined to form the proposed “Soundscape Code,” which allows for rapid multidimensional and direct comparisons of salient soundscape properties across time and space. This initial characterization will aid in directing further analyses and guiding subsequent assessments to understand soundscape dynamics.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".