A Data-Driven Environmental Sound Classification System with Acoustic Triboelectric Nanogenerators
Bibliographic record
Abstract
The availability of big data has resulted in significant advancements in deep learning, which have led to increased performance, surpassing human capabilities in tasks, for example, audio classification. Moreover, there is a growing need for energy-efficient, self-sustained sensors that can be massively deployed for managing sound events with intelligence. Acoustic Triboelectric Nanogenerators (TENG) show promise in this context, as they are capable of converting mechanical motion from acoustic waves into electrical signals while being cheap to manufacture. However, leveraging TENGs for environmental sound classification systems requires addressing the challenges associated with data collection and model training. This paper presents a design for an environmental sound classification system that utilizes acoustic TENGs and transformer-based models. To achieve this, we design and fabricate a device composed of cascading TENGs and use the device to record the ESC-50 dataset, which is then used to fine-tune transformer-based models for audio classification. A substantial improvement on model performance (by 44%) has been observed compared to that from a model pre-trained on the original ESC-50 dataset. The results provide valuable insights into the quality of TENG recorded audio, serving as a benchmark for future research in building data-driven environmental sound monitoring systems.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".