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Published on: September 27, 2024
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Propagating variational model uncertainty for bioacoustic call label smoothing
Georgios Rizos1, Jenna Lawson2, Simon Mitchell3
1GLAM - Group on Language, Audio, & Music, Department of Computing, Imperial College London, London SW7 2RH, UK.
Patterns (New York, N.Y.)
|March 15, 2024
Summary
Bayesian neural networks now quantify prediction uncertainty, improving wildlife call detection. Sample-free methods and uncertainty-aware label smoothing enhance accuracy in passive acoustic monitoring.
Area of Science:
- Artificial Intelligence
- Bioacoustics
- Machine Learning
Background:
- Bayesian neural networks (BNNs) propagate uncertainty alongside predictions.
- Variational Bayesian methods offer sample-free approaches to uncertainty estimation.
- Passive acoustic monitoring (PAM) is crucial for wildlife research.
Purpose of the Study:
- To apply sample-free BNN methods for wildlife call detection using PAM data.
- To introduce uncertainty-aware label smoothing to improve model training.
- To evaluate the proposed methods on a challenging bioacoustic dataset.
Main Methods:
- Utilized sample-free variational Bayesian neural networks.
- Developed an uncertainty-aware label smoothing technique.
- Introduced a new bioacoustic dataset from Malaysian Borneo with overlapping calls from 30 species.
Main Results:
- Achieved an absolute percentage improvement of 1.5 points in AU-ROC.
- Demonstrated a 13-point improvement in F1 score.
- Showcased a 19.5-point reduction in expected calibration error (ECE).
Conclusions:
- Sample-free BNNs with uncertainty-aware label smoothing significantly improve wildlife call detection accuracy.
- The proposed methods offer a robust approach for analyzing complex bioacoustic data.
- This work advances the application of AI in ecological monitoring.
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