Comparison of Hand-Crafted and Deep Features Towards Explainable AI at the Edge for Analysis of Audio Scenes
Bibliographic record
Abstract
This paper explores explainable Artificial Intelligence (XAI) for pre-trained audio models and audio source counting. An XAI pipeline is proposed that utilizes pretrained audio models as feature extractors to investigate the efficacy of hand-crafted acoustic features in replicating the learned representations of the deep features. The approach involves two phases: evaluating similarity between hand-crafted and deep features using the linear centered kernel alignment algorithm, followed by classification analysis using Multi-Layer Perceptrons and select hand-crafted features. The results show this approach has implications for edge computing and XAI, where we demonstrate a resource-constrained neural network classifier, under 10 KB in size, that uses interpretable hand-crafted features, exhibits less than a 4% decrease in accuracy compared to a benchmark model <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$136\times$</tex> larger.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".