Soundscape cube: A holistic approach to explore and compare acoustic environments
Bibliographic record
Abstract
Soundscape patterns result from sounds radiated by several sources governed by diverse processes acting at different scales.Acoustic measurements are sampling this multi-scale variability pattern at particular locations and times.To facilitate soundscape analysis, the identification and separation of the different contributors, and soundscape comparisons, an approach, called soundscape cube, is introduced.For any acoustic measurement time-series, a probability of occurrence is estimated for all time-frequency samples of sound pressure levels (SPL) from the cumulative density functions (cdfs) of the sound spectra computed for consecutive time-windows.These spectral cdfs are then stacked along the time axis to generate a 3D block that piles up the time-frequency surfaces of the spectral SPL quantiles.This soundscape cube can then be explored by various mathematical operators to characterize and separate intra-soundscape SPL patterns emerging across the cube.The soundscape cube can also be split into its ambient-noise and structured-signal components, which respond to different forcing and timespace scales.Inter-soundscape cube operators can highlight the differences and similarities among sites, years, and their scales of autocorrelation, recurrence, etc. Application examples of this approach are given for acoustic measurements from the Canadian Arctic, the St. Lawrence Estuary, and the Mediterranean Sea.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".