Classification of Bird Sound Using High-and Low-Complexity Convolutional Neural Networks
Bibliographic record
Abstract
Birds are a reflection of environmental health as pollution and climate change affect biodiversity.Experts in ecology and machine learning stand to benefit the most from largescale monitoring of biodiversity.Today, convolutional neural networks (CNNs) are the preferred choice for species recognition as their performance has consistently outperformed humans.However, CNNs are disadvantaged by their high computational complexity and the need to provide vast amounts of training data.This paper compares the performance versus the complexity of two widely used CNNs, namely ResNet-50 and MobileNetV1.ResNet-50 is a high-complexity CNN while MobilenetV1 is a low-complexity CNN targeted for mobile applications.We used spectrogram images of Brazilian bird sounds as inputs to both networks.These birds were chosen due to their abundance of samples in the Xeno-canto bird sound repository.Short-Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficient (MFCC) algorithms are used to extracting spectrogram images.To validate the precision of the classifier, 1,000 spectrogram images of each of ten bird species are produced and fed into both classifiers.The findings indicate that the accuracy of MobileNetV1 is close to that of ResNet-50, with MFCC which is 85.73 and 90.56 respectively.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".