A framework for computer-assisted sound design systems supported by modelling affective and perceptual properties of soundscape
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.
Bibliographic record
Abstract
Autonomously generating artificial soundscapes for video games, virtual reality, and sound art presents several non-trivial challenges. We outline a system called Audio Metaphor that is built upon the notion that sound design for soundscape compositions is emotionally informed. We first define the problem space of generating soundscape compositions referencing the sound design and soundscape literature. Next, we survey the state-of-the-art soundscape generation systems and establish the characteristics and challenges for evaluating these types of systems. We then describe the Audio Metaphor system that aims to model the soundscape generation problem using a method of soundscape emotion recognition and segmentation based on perceptual classes, and an autonomous mixing engine utilising optimisation and prediction algorithms to generate a soundscape composition. We evaluate the soundscape compositions generated by Audio Metaphor by comparing them with those created by a human expert and also those generated randomly. Our analysis of the evaluation study reveals that the proposed soundscape generation model is human-competitive regarding semantic and emotion-based indicators.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it