Una reinterpretación tímbrica del espacio eco-acústico: Improvisación guiada a través del análisis del paisaje sonoro
Bibliographic record
Abstract
This work exposes a case study that uses the soundscape as an interpretive and analytical guide of space in the context of musical composition and improvisation. The article explores a methodology that allows organizing and interacting space as a compositional element. Different musicians participated in developing an intuitive analysis focused on the perception of space or sound events with moving trajectories. It was possible by using ambisonics technology, which allows it a more accurate appreciation of the acoustic characteristics of the space, as well as different moving acoustic events that occur during the recording. Artists like Barry Truax (Canada, 1947) and David Dunn (United States, 1953), who have included space in their works as an element to interact through sound. Both artists helped us as a reference in developing different strategies to interact with the space, using diverse sounds projected in the space. This methodology developed an approach to study soundscape, as well as showing the results obtained by the different members of the study. All this in order to incorporate the acoustic space as a musical element to study.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".