Bibliographic record
Abstract
Speech, music and environmental sound refer to three specialized areas of acoustic communication and its study, and these areas can be regarded as forming a continuum of human aural experience. Although these areas have traditionally been studied separately, there are many factors today pointing to their overlap and interaction. Most obviously, contemporary audio technology has blurred their distinctions by making reproduced speech and music into common environmental sounds, often structured by media and individuals as accompaniment environments. However, the relatively intense affective responses that we regard as the expressions of emotions through speech and music have been studied extensively, but separately, with any equivalent role of environmental sounds largely ignored. Recent advances in brain functioning have begun to suggest that there are underlying mechanisms, related to specific parts of the brain, which can be linked to known psychological responses to both music and speech. One important clue to their relationship is that the emotional (and other) aspects of speech are conveyed by paralanguage (i.e. the nonverbal aspects of vocalization), the parameters for which are closely related to the musical parameters of melody. This paper wishes to extend this current line of research to the neglected area of environmental sound as it is perceived by individuals in context, namely as the soundscape. Soundscape competence, it is argued, co-evolved with the specialized areas of speech and music, and today with the widespread phenomenon of music-as-environment, as well as other media practices, it is useful to re-connect old arguments about music and emotion with contemporary soundscape experience. The paper will be illustrated with both environmental recordings and excerpts of the author’s soundscape compositions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".