Security Instrument using Talker Identification and Microphone Arrays in Variable Noisy Environments
Bibliographic record
Abstract
In paper, we propose a robust security instrument that can detect the location and identity of talker in variable noisy environment. This instrument calculates the value of the signal-to-noise (SNR) using a discrete-valued SNR estimator. This SNR value is used to adapt the performance of speech/nonspeech classifier to the surrounding noisy environment. If the detected signal is speech then a novel multi-engine talker identification (TT) will determine the identity of the talker, else an audio classification system will determine the audio type of the detected signal (e.g. widows breaking, and wind sounds). Multi-engine TI utilizes the SNR value to select the TI engine, from a set of six engines (five different SNR environments and a "clean" environment), with the SNR training condition that best matches the surrounding environment; greater TI accuracy should be achieved when training and test environments are similar. The performance of this instrument is evaluated using twelve test environments, with the SNR ranging from -10 dB to clean environment (SNR > 50 dB). The proposed instrument achieves an average classification accuracy of 92% over an SNR range of 10 dB to clean environments; an enhancement of 38% over the instrument trained in a clean environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".