Doppler-Based Sound Localization and Its Application in AI-empowered Traffic Warning for Deafness
Why this work is in the frame
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Bibliographic record
Abstract
Hearing is essential for information processing and our safety, especially in traffic. For patients with hearing impairments, sound localization devices help identify threatening sound sources and reduce traffic safety risk. Existing methods typically rely on measuring time delay using microphone arrays. Unfortunately, this method faces critical challenges for dynamic sound sources, posing major threats. Addressing this challenge, we propose a wearable sound localization system that combines interaural time differences and Doppler frequency shifts to determine the position, direction, and speed of moving sound sources. The method's feasibility is first evaluated through a combination of theoretical calculations and experimental verifications. A proof-of-concept setup was established by engaging three microphones as the receiver and a toy car emitting a constant tone as the sound source. An AI algorithm based on artificial neural network was further trained using the received sound signal when the source moved at different locations and directions. The results demonstrated accurate detection of Doppler shifts, with classification accuracies of 100% for front/back, 87.5% for distance, and 62.5% for left/right. Finally, the system was integrated into a wristband with feedback motors, providing vibrational alerts based on the detected motion and proximity of sound sources. These results validate the feasibility of using Doppler shifts and machine learning for motion detection in real time. The proposed system offers a portable solution to enhance the awareness of the environment for individuals with hearing impairments and lays the groundwork for future warning devices in traffic safety applications.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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