“Auxauralities” : Ears-on 3-D-printed acoustics metamaterials for sound art
Bibliographic record
Abstract
Due to unique acoustic properties, acoustic metamaterials (AMMs) find many engineering applications in industrial or military sectors. However, as a result of their complex behavior and since they are generally unknown to the non-scientists, AMMs are not integrated into the thinking of daily auditory culture and sound environments. AMMs' potentially speculative effects on sound environments and audio cultures could be communicated and investigated through art. To fully seize this opportunity, there is a need for Arts and Sciences sympoiesis as a way of thinking, creating, designing and experientially testing audible-range AMMs. Since engineering AMMs require considerable expertise in physics and additive manufacturing, it is not easy for non-acousticians to explore such materials. Therefore, an “ears-on” audible experiential approach was developed. To do so, the presented work is a co-creation process for developing tools for sound art. Two approaches were investigated as sound art prototyping tools. First, an auralization process is presented to perform acoustic simulations and virtually hear the results beforehand. A second approach uses modular 3-D-printed resonators and crystals for the rapid improvement of the hands-on and “ears-on” iterative design of a sound art installation: “Auxauralities” [Work supported by Fonds Recherche Québec Audace, 2020-AUDC271071].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".