Audio Recognition-based Method for RF Transmitters Classification using CNN-LSTM model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a novel approach to Radio Frequency transmitter classification that adapts audio recognition techniques to enhance device identification accuracy. By leveraging Mel-frequency cepstral coefficients (MFCCs) and spectral characteristics traditionally used in audio processing, combined with a hybrid CNN-LSTM deep learning architecture, our method achieves $93.6 \%$ classification accuracy across 8 devices in same-scenario testing. The approach demonstrates strong initial performance comparable to state-of-the-art RF fingerprinting methods, while also providing insights into cross-scenario challenges under varying operational conditions. Our experimental results, conducted using a dataset of commercial RF transmitters, highlight both the potential of audio-inspired features for RF fingerprinting and the persistent challenge of maintaining classification reliability across different operational conditions. This work contributes to the growing field of physicallayer security by demonstrating how cross-domain adaptation of proven audio processing techniques can enhance RF device identification.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it