Vehicle Sound Recognition Assistance in IoT Systems for Hearing-Impaired Drivers
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
Hearing-impaired drivers face significant challenges in detecting critical auditory cues, such as emergency vehicle sirens, essential for safe driving. This article presents an advanced IoT-based sound recognition system designed to enhance situational awareness for these drivers. Audible signals are recognized and transformed into alerts displayed in the dashboard. Our approach involves preprocessing audio data to extract 23 features. We normalize these features and evaluate multiple Machine Learning and Deep Learning models for their classification performance. The top five models, selected based on their performance metrics, are then combined into an ensemble model using majority voting to improve accuracy and robustness. Our dataset comprising 1500 audio samples enabled us to achieve a final accuracy of 94.2% with the ensemble voting approach. These results demonstrate a significant performance in sound classification accuracy compared to individual models, indicating the effectiveness of our ensemble approach. This research provides a valuable step towards developing more accessible and safer driving assistance systems for individuals with hearing impairments.
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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 | 0.000 | 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 | 0.000 |
| 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