Bibliographic record
Abstract
Exposure to gunshot sounds can cause hearing impairments. Measuring and analyzing these sounds can improve the design of hearing protectors and can help in enacting safety regulations. Furthermore, analyzing gunshot sounds can help identify the type of gun used. This is important for determining the appropriate public safety actions when a gunshot sound is detected in a public space. In this paper, we collected acoustic data from four different guns. To capture their sound including any non-symmetric sound propagation, 27 high dynamic range pressure microphones were placed around the guns forming a polar grid pattern. Audio signals were captured at 204.8 kHz sampling rate synchronously to preserve the fidelity of the impulse nature of the gunshots. In this study, an image-based analysis method was developed to take advantage of the recent advancement of image recognition techniques. Two spectral analysis methods: Short Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT), were then applied to get the spectrogram of the gunshot audio signal. Machine learning using the k-nearest neighbor and random subspaces was used to classify these spectrograms and identify which gun did the particular gunshot originated from. Under reverberant conditions, the STFT maintained a better identification accuracy than the CWT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".