<scp>ECOGEN</scp> : Bird sounds generation using deep learning
Bibliographic record
Abstract
Abstract Large‐scale acoustic projects generate vast amounts of data that can now be efficiently processed using deep learning tools. However, these tools often face limitations due to sound labeling and imbalanced sampling. Data augmentation can help overcome such challenges, particularly through the generation of synthetic and lifelike sounds. Synthetic samples can be valuable not only for deep learning but also for species with limited available data. Despite advancements in computer power, sound generation remains a time‐consuming process, even requiring a substantial number of samples. We present ECOGEN, a novel deep learning approach designed to generate realistic bird songs for biologists and ecologists. The primary objective of ECOGEN is to enhance the number of samples in under‐represented bird song classes, thereby improving the performance and robustness of classifiers in ecological research.The ECOGEN framework employs spectrograms as a representation of bird songs and leverages proven image generation techniques to create new spectrograms, subsequently converted back to digital audio signals. As a class‐agnostic tool, ECOGEN is applicable to a wide range of biophonic sounds, including mammal and insect calls. We show that adding samples generated by ECOGEN to a bird song classifier improved the classification accuracy by 12% on average and improved results compared with classic data augmentation techniques 80% of the time. Our approach is both fast and efficient, enabling the generation of synthetic bird songs on standard computing resources. By facilitating the creation of synthetic bird songs, ECOGEN can contribute to the conservation of endangered bird species, while providing valuable insights into their vocalizations, behaviours and habitat preferences. Future development of ECOGEN can be easily implemented and could focus on incorporating additional configurable parameters during the generation phase for increased control over the output, catering to the specific needs of biologists.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".