"We Musicians Know How to Divide and Conquer": Exploring Multimodal Interactions To Improve Music Reading and Memorization for Blind and Low Vision Learners
Bibliographic record
Abstract
Despite the potential of multimodal assistive technologies (MATs) to convey visual information, such as music notation, to blind or low-vision (BLV) individuals, we do not fully understand how MATs can be used to improve music reading and memorization. Through ideation and co-design workshops, we explored how modalities, such as sound and vibration, can improve music reading and memorization through hands-free timely interactions and reminders. Our design workshops presented a unique opportunity for BLV musicians and learners to collaborate and actively engage in the research and design process informed by their individual perspectives and lived experiences. We classified the complex challenges of reading and memorizing music into intrinsic (related to the cognitive aspects of music understanding) and extraneous (pertaining to external factors such as interaction and access) complexities and found that specific modalities are well suited to tackle particular problems. We conclude by outlining design implications and future research directions aimed at developing MATs that holistically improve music learning for BLV people.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".