# Towards Automated Animal Density Estimation with Acoustic Spatial Capture-Recapture

arXiv: https://arxiv.org/abs/2308.12859 · HTML: https://mlsanity.com/paper/2308.12859/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables](https://mlsanity.com/paper/1703.00091/) (2017)
- [EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis](https://mlsanity.com/paper/1509.01509/) (2015)
- [Convolutional Recurrent Neural Networks for Bird Audio Detection](https://mlsanity.com/paper/1703.02317/) (2017)
- [Accommodating false positives within acoustic spatial capture-recapture, with variable source levels, noisy bearings and an inhomogeneous spatial density](https://mlsanity.com/paper/2207.09343/) (2022)

## Built on by (most cited first, 1 of 1)

- [Approximate Maximum Likelihood Inference for Acoustic Spatial Capture-Recapture with Unknown Identities, Using Monte Carlo Expectation Maximization](https://mlsanity.com/paper/2410.04390/) (2024)

## Abstract (arXiv)

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually. Digital recorders allow surveyors to gather large volumes of data at low cost, but identifying target species vocalisations in these data is non-trivial. Machine learning (ML) methods are often used to do the identification. They can process large volumes of data quickly, but they do not detect all vocalisations and they do generate some false positives (vocalisations that are not from the target species). Existing wildlife abundance survey methods have been designed specifically to deal with the first of these mistakes, but current methods of dealing with false positives are not well-developed. They do not take account of features of individual vocalisations, some of which are more likely to be false positives than others. We propose three methods for acoustic spatial capture-recapture inference that integrate individual-level measures of confidence from ML vocalisation identification into the likelihood and hence integrate ML uncertainty into inference. The methods include a mixture model in which species identity is a latent variable. We test the methods by simulation and find that in a scenario based on acoustic data from Hainan gibbons, in which ignoring false positives results in 17% positive bias, our methods give negligible bias and coverage probabilities that are close to the nominal 95% level.