Meta-Learning with Pretrained Audio Representations Enables One-Shot Acoustic Signal Classification
Bibliographic record
Abstract
Few-shot acoustic signal classification remains a challenging problem due to the high diversity and variability of acoustic data and limited availability of labeled samples. While pretrained audio classification models have proven effective for various acoustic signal classification tasks, fine-tuning them can still lead to overfitting in low-resource settings. In this work, we proposes an attention-based meta-learning framework that operates on the hidden states of a pretrained audio classification model. Specifically, we introduce a trainable hierarchical additive attention module to extract meaningful features from the hidden states of a large-scale pre-trained Audio Spectrogram Transformer (AST). The attention mechanism is trained with a simple meta-learning paradigm, enabling effective adaptation to one-shot learning tasks. We evaluates the proposed model on multiple acoustic signal classification tasks, including acoustic scene classification, sound event recognition and underwater vessel noise classification. Experimental results demonstrate that our proposed framework substantially outperforms the existing methods such as CNN-based prototypical networks in terms of one-shot classification accuracy. This research not only provides an efficient solution for low data resource acoustic pattern recognition tasks but also demonstrate the strong potential of pre-trained audio classification models when combined with metalearning framework for few-shot learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".