End-to-End Environmental Sound Classification using a 1D Convolutional\n Neural Network
Bibliographic record
Abstract
In this paper, we present an end-to-end approach for environmental sound\nclassification based on a 1D Convolution Neural Network (CNN) that learns a\nrepresentation directly from the audio signal. Several convolutional layers are\nused to capture the signal's fine time structure and learn diverse filters that\nare relevant to the classification task. The proposed approach can deal with\naudio signals of any length as it splits the signal into overlapped frames\nusing a sliding window. Different architectures considering several input sizes\nare evaluated, including the initialization of the first convolutional layer\nwith a Gammatone filterbank that models the human auditory filter response in\nthe cochlea. The performance of the proposed end-to-end approach in classifying\nenvironmental sounds was assessed on the UrbanSound8k dataset and the\nexperimental results have shown that it achieves 89% of mean accuracy.\nTherefore, the propose approach outperforms most of the state-of-the-art\napproaches that use handcrafted features or 2D representations as input.\nFurthermore, the proposed approach has a small number of parameters compared to\nother architectures found in the literature, which reduces the amount of data\nrequired for training.\n
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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) | 0.002 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".