Data from: What do we mean with sound semantics, exactly? A survey of taxonomies and ontologies of everyday sounds
Bibliographic record
Abstract
Data from: What do we mean with sound semantics, exactly? A survey of taxonomies and ontologies of everyday sounds (2022, Frontiers in Psychology) Bruno L. Giordano1,*§, Ricardo de Miranda Azevedo2,*, Yenisel Plasencia-Calaña3, Elia Formisano3,4,¶, Michel Dumontier2,3,¶ 1 Institut des Neurosciences de La Timone, CNRS UMR 7289 – Université Aix-Marseille, Marseille, France; 2 Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands 3 BISS institute, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands 4 Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands * co-first author ¶ co-senior author § correspondence: bruno.giordano@univ-amu.fr Abstract: Taxonomies and ontologies for the characterization of everyday sounds have been developed in several research fields, including auditory cognition, soundscape research, artificial hearing, sound design, and medicine. Here, we surveyed thirty-six of such knowledge organization systems, which we identified through a systematic literature search. To evaluate the semantic domains covered by these systems within a homogeneous framework, we introduced a comprehensive set of verbal sound descriptors (sound source properties; attributes of sensation; sound signal descriptors; onomatopoeias; music genres), which we used to manually label the surveyed descriptor classes. We reveal that most taxonomies and ontologies were developed to characterize higher-level semantic relations between sound sources in terms of the sound-generating objects and actions involved (what/how), or in terms of the environmental context (where). This indicates the current lack of a comprehensive ontology of everyday sounds that covers simultaneously all semantic aspects of the relation between sounds. Such an ontology may have a wide range of applications and purposes, ranging from extending our scientific knowledge of auditory processes in the real world, to developing artificial hearing systems. Usage: We include data for 36 taxonomies/ontologies of natural sounds (xlsx file). An owl implementation for each of the taxonomies/ontologies is also provided (zip file).
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.032 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".