Learning Music Blind: Understanding the Application of Technology to Support BLV Music Learning
Bibliographic record
Abstract
Learning to play a musical instrument and engaging in musical activities have enabled blind and/or low vision people to develop self-identity, find community and pursue music as a career. However, blind and/or low vision music learners face complex obstacles to learn music. They are highly reliant on their learning environment and music teachers for accommodations and flexibility. Prior research has identified the challenges faced by blind and/or low vision musicians and recognized the importance of touch for music reading and physical guidance. However, limited research has addressed these challenges through the development of assistive technology. The development of music computer technologies with haptics and the affordances of wearable technologies provides encouraging opportunities to develop haptic wearable devices to support blind and/or low vision music learning. I identify three unexplored research questions: (1) what design considerations must be addressed in future assistive technologies for BLV music learning, (2) how can wearable technologies with vibrotactile feedback support BLV student-teacher interactions, and (3) what are the long-term benefits and limitations of the use of assistive technologies for BLV music learning? I outline my research to date and highlight my findings.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".