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Hybrid Soundscape: Human and non-human sounds interactions for a collective installation

2022· article· en· W4312297784 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueeCAADe proceedings · 2022
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSoundscapeComputer scienceDialog boxHuman–computer interactionEmulationEvent (particle physics)Sound (geography)AcousticsWorld Wide Web

Abstract

fetched live from OpenAlex

The paper describes a site-specific architectural soundscape installation created during a workshop in August 2021 at the Domaine de Boisbuchet in France. Far from urban noise, participants were attuned to natural, artificial, and human sound spheres, placing them in dialog and interweaving them through emulation, voice recording, and electro-acoustic devices including piezoceramic sensors, small motors, speakers, and embedded electronics. This expository paper includes qualitative descriptions of the spatial sound compositions, the technology that supported them, and the performance into which they were integrated. The results of this event were described by participants as trance-like, with phasing of multiple periodically organized emergent sound phenomena creating a deeply immersive distributed environment. In describing in detail, the tools, processes, outcomes and implications of the workshop, this paper offers an example of a design approach and model that can contribute immersive distributed architectural soundscape design through human and non-human sound interaction.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.316
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it