Automatic bird sound detection: logistic regression based acoustic occupancy model
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
Avian bioacoustics research was greatly assisted by the introduction of autonomous recording units, which not only allow remote monitoring but also make large-scale studies possible. However, manual inspection of acoustic recordings becomes more challenging with increasingly larger datasets. In this study, we developed a logistic model to predict the probability of bird presence in audio recordings using sound frequency percentiles. The acoustic recordings covered bird songs and calls in a wide range of environments (e.g. grassland, forest, urban areas) along with the presence of noise due to weather, traffic, insects, and human speech. Based on leave-one-out cross-validation, our final logistic model resulted in a 75% overall accuracy and a 16% false negative rate using the optimal cut-off of 0.35 (i.e. probability ≥ 0.35 indicates the presence of birds). Compared with a convolutional neural network model using the same dataset, the logistic model was about seven times faster in terms of the processing time, but achieved slightly lower overall accuracy. This bird sound detection model using sound frequency percentiles in a logistic model opens up promising approaches to aid in automatic, accurate, and efficient analyses of large audio datasets for monitoring wildlife communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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