Low-Frequency Acoustic Source Detection and Localization
Bibliographic record
Abstract
In aviation, clear-air turbulence (CAT) is a major cause of in-flight injuries. It occurs in cloudless skies and cannot be detected by the onboard weather radar. Studies have predicted the extent of CAT to increase substantially in the next few decades, thus \nnecessitating a method for detecting CAT. \n \nWith CAT known to generate low-frequency and infrasonic acoustic emissions, acoustic-based methods can potentially be deployed for detection and localization. This thesis studies low-frequency acoustic source detection and localization in the context of CAT. \n \nLocalizing low-frequency acoustic sources is challenging for acoustic beamforming which suffers from poor resolution at low source frequencies. A deep learning-based method is adopted as an alternative. Deep learning models for two-dimensional and three-dimensional acoustic source localization (ASL) have been built using synthetic data and computationally inexpensive neural network architectures. These models are necessary to prove the viability of deep learning for low-frequency ASL. The thesis then explores the potential of a deep learning-enabled, acoustics-based method for CAT detection in the future through a large-scale, virtual flight case, set up for the detection of a representative CAT source. The flight case tries to predict what an in-flight microphone will detect around a CAT source through a technique known as auralization which simulates the acoustic field of a source by modeling the sound propagation and determining what a receiver would hear. \n \nThe deep learning models yield promising qualitative and quantitative results that prove the feasibility of using deep learning for low-frequency ASL. Combined with the results from auralization, it can be concluded that there exists considerable scope for deep learning-enabled, acoustics-based detection and localization of CAT. The future work involves expanding the current scale of research with deeper network architectures to process real, in-flight acoustic data.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".