<scp>HOWLish</scp> : a <scp>CNN</scp> for automated wolf howl detection

Abstract Automated sound‐event detection is crucial for large‐scale passive acoustic monitoring of wildlife, but the availability of ready‐to‐use tools is narrow across taxa. Machine learning is currently the state‐of‐the‐art framework for developing sound‐event detection tools tailored to specific wildlife calls. Gray wolves ( Canis lupus ), a species with intricate management necessities, howl spontaneously for long‐distance intra‐ and inter‐pack communication, which makes them a prime target for passive acoustic monitoring. Yet, there is currently no pre‐trained, open‐access tool that allows reliable automated detection of wolf howls in recorded soundscapes. We collected 50 137 h of soundscape data, where we manually labeled 841 unique howling events. We used this dataset to fine‐tune VGGish—a convolutional neural network trained for audio classification—effectively retraining it for wolf howl detection. HOWLish correctly classified 77% of the wolf howling examples present on our test set, with a false positive rate of 1.74%; still, precision was low (0.006) granted extreme class imbalance (7124:1). During field tests, HOWLish retrieved 81.3% of the observed howling events while offering a 15‐fold reduction in operator time when compared to fully manual detection. This work establishes the baseline for open‐access automated wolf howl detection. HOWLish facilitates remote sensing of wild wolf populations, offering new opportunities in non‐invasive large‐scale monitoring and communication research of wolves. The knowledge gap we addressed here spans across many soniferous taxa, to which our approach also tallies.