Low-SNR Northern Right Whale Upcall Detection and Classification Using Passive Acoustic Monitoring to Reduce Adverse Human–Whale Interactions
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
Marine mammal vocalizations, such as those of the Northern Right Whale (NARW), are often masked by underwater acoustic noise. The acoustic vocalization signals are characterized by features such as their amplitude, timing, modulation, duration, and spectral content, which cannot be robustly captured by a single feature extraction method. These complex signals pose additional detection challenges beyond their low SNR. Consequently, this study proposes a novel low-SNR NARW classifier for passive acoustic monitoring (PAM). This approach employs an ideal binary mask with a bidirectional long short-term memory highway network (IBM-BHN) to effectively detect and classify NARW upcalls in challenging conditions. To enhance model performance, the reported literature limitations were addressed by employing a hybrid feature extraction method and leveraging the BiLSTM to capture and learn temporal dependencies. Furthermore, the integration of a highway network improves information flow, enabling near-real-time classification and superior model performance. Experimental results show the IBM-BHN method outperformed five considered state-of-the-art baseline models. Specifically, the IBM-BHN achieved an accuracy of 98%, surpassing ResNet (94%), CNN (85%), LSTM (83%), ANN (82%), and SVM (67%). These findings highlight the practical potential of IBM-BHN to support near-real-time monitoring and inform evidence-based, adaptive policy enforcement critical for NARW conservation.
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 | 0.001 | 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