HOWLish : a CNN for automated wolf howl detection

Campos, Rafael (avtor), Krofel, Miha (avtor), Rio-Maior, Helena (avtor), Renna, Francesco (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: bioacoustics, deep learning, howling, monitoring, wolf
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: BF - Biotehniška fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: Str. 58-70
• Številčenje: Vol. 12, iss. 1
• PID: 20.500.12556/RUL-180425
• UDK: [599.744.111.1:591.582]:004.85
• ISSN pri članku: 2056-3485
• DOI: 10.1002/rse2.70024
• COBISS.SI-ID: 250212611
• Datum objave v RUL: 09.03.2026

Gradivo je del revije

• Naslov: Remote sensing in ecology and conservation
• Skrajšan naslov: Remote sens. ecol. conserv.
• Založnik: Zoological Society of London
• ISSN: 2056-3485
• COBISS.SI-ID: 525560345

Licence

• Licenca: CC BY-NC 4.0, Creative Commons Priznanje avtorstva-Nekomercialno 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc/4.0/deed.sl
• Opis: Licenca Creative Commons, ki prepoveduje komercialno uporabo, vendar uporabniki ne rabijo upravljati materialnih avtorskih pravic na izpeljanih delih z enako licenco.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: bioakustika, strojno učenje, oglašanje, monitoring, volk, Canis lupus