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Supervised Wolf Chorus Counting

To our knowledge, there are no attempts at automated acoustic counting of overlapping signals, though there are several approaches that may be promising. We are training models (e.g. LSTM-CRF) to predict the number of overlapping spectral elements at fine timescales using open-source data. Assess the model’s ability to generalize to new datasets. Train a model to predict the number of wolves in a chorus based on human annotation of the number of wolves vocalizing concurrently.​

Unsupervised Wolf Source Separation

Using previous work in source separation and emphasizing the unsupervised MixIT training algorithm used to separate overlapping birdsong mixtures, we can attempt to separate wolf choruses into predictions for the individuals present in the chorus. Though not functionally limited in the number of sources it can handle, it is unclear how the model will perform as the number of concurrently vocalizing wolves increases.

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Unsupervised Meaning Discovery in Wolf Vocalizations

The CETI project has produced machine learning models, with little or no understanding of a species vocal repertoire, can be used to reveal meaningful units in the sounds. The approach in this paper, APPROACHING AN UNKNOWN COMMUNICATION SYSTEM BY LATENT SPACE EXPLORATION AND CAUSAL INFERENCE, with modification for wolf vocalizations, is promising.​​

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Conservation Value

1. Non-invasive wolf population monitoring: occupancy, abundance, population trends

2. Assessing wolf pack structure and social dynamics: reproduction, pack identity, individual identity, changes in pack membership, aid in understanding effects of hunting, poaching, or environmental changes on pack dynamics

3. Tracking wolf movement and territory: monitor habitat use, overturn in territory, index of habitat quality, influence of humans on territory use.

4. Understanding responses to environmental and human disturbances: target areas of protection or corridors; mitigate human impacts

5. Monitoring reintroduction and conservation success in other places where wolves are returning

6. Conservation of cultural and ecosystem roles of wolves 

7. Supporting law enforcement efforts and human-livestock conflicts: monitoring and responding to potential illegal activity such as poaching and gunshots inside protected lands; developing potential tools to mitigate depredations with livestock. 

8. Technological advances to aid in species monitoring: developing reliable, long-lasting, cost-effective advanced camera traps with acoustic recorders; develop AI models for processing data

9. Education and outreach about wolves, animal communication, ecosystem processes, and natural soundscapesUmbrella research: recording soundscapes for wolves yields data to aid with conservation and monitoring of other species (e.g., bird) and natural soundscape.