Combining audio and non-audio inputs in evolved neural networks for Ovenbird classification
Bibliographic record
Abstract
In the last several years, the use of neural networks as tools to automate species classification from digital data has increased. This has been due in part to the high classification accuracy of image classification through Convolutional Neural Networks (CNN). In the case of audio data, CNN-based recognisers are used to automate the classification of species in audio recordings by using information from sound visualisation (i.e. spectrograms). It is common for these recognisers to use the spectrogram as their sole input. However, researchers have other, non-audio data, such as habitat preferences of a species, phenology, and range information, which could improve species classification. We present how a single-species recogniser neural network’s accuracy can be improved by using non-audio data as inputs in addition to spectrogram information. We analyse the cause of the improvements: are they a result of having a neural network with a higher number of parameters or is it due to the use of the two inputs? We find that networks that use the two different inputs have a higher classification accuracy. This suggests that the accuracy of classifiers can be improved by giving them non-audio information about the location and conditions where the recordings were obtained.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".