Form Follows Sound: Designing For Sound Awareness
Bibliographic record
Abstract
Sound is a prominent feature of our urban environment, affecting us in many ways. Studies show that sound levels in cities are increasing every year. Considering the fact that our well-being is closely related to everyday sound, there is an apparent interconnection between them. Active listening to the surrounding acoustic environment offers the ability to appreciate sounds and articulate auditory needs having an impact on our well-being. Being aware of sound and expressing what we want to hear can lead to tangible transformation and change in our urban acoustic environment and space.\nThis thesis explores the potential of sound visualization and the representation of auditory information as a means of enhancing perception about urban sounds in our daily interactions. Through practice-based design research, studies in sound and music visualization with a focus on perception of shapes and semiotics, the main body of this work intends to gain insights into sound perception and its potential relationship with visual form. This research led to the creation of ‘Right Hear’, a map depicting the evolving soundscapes of Vancouver. ‘Right Hear’ aims to invite users to explore their own acoustic sense of place and become aware of the urban sounds by offering the ability to simultaneously listen and see sounds on a visual map. In parallel with this, the body of this work led to a series of exercises in the notation of sound and translation of graphic scores that looks into the ways that people from diverse fields of practice perceive, translate and respond to abstract shapes.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".