Edge-Based Data Sensing and Processing Platform for Urban Noise Classification
Bibliographic record
Abstract
The increasing population in urban areas contributes to noise pollution. So, cities look to locate areas with high noise levels to regulate them and improve urban well-being. However, dispatching personnel for noise data collection is time-consuming and expensive. Therefore, we propose a low-cost Internet of Things (IoT)-based urban noise classification platform to address noise collection and processing challenges in urban environments. We designed a prototype sound-sensing setup consisting of an STM32 NUCLEO-64 board with an attached X-NUCLEO-CCA02M1 expansion board as the digital MEMS microphone connected to a Raspberry Pi for collecting and classifying urban sound. Then, it sends the results via WiFi to the cloud server for noise analysis. We examined our design's feasibility with experiments evaluating its classification accuracy and power consumption. We further examined its latency when processing is at the edge or cloud. The experimental results suggest our platform's potential in noise analysis for urban environments.
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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 | 0.001 | 0.001 |
| 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 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".