Combining audio and non-audio inputs in evolved neural networks for Ovenbird classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 it