A Hierarchical Visual Feature-Based Approach For Image Sonification
Bibliographic record
Abstract
This paper presents a new image sonification system that strives to help visually impaired users access visual information via an audio (easily decodable) signal that is generated in real time when the users explore the image on a touch screen or with a pointer. The sonified signal, which is generated for each position within the image, tries to capture the most useful and discriminant local information about the image content at different levels of abstraction, ranging from low-level (at the pixel level) to high-level (segmentation) and combining low-level (color edges and texture), mid-level and high-level (gradient or color distribution for each region of the image) features. The proposed system mainly uses musical notes at several octaves, the notion of timbre, and loudness but also uses pitch, rhythm and the distortion effect in an intuitive way to sonify the image content both locally and globally. To this end, we use perceptually meaningful mappings, in which the properties of an image are directly reflected in the audio domain, in a very predictable way. The listener can then draw simple and reliable conclusions about the image by quickly decoding the sonified result.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".