HOWLish : a CNN for automated wolf howl detection

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

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.

• Language: English
• Keywords: bioacoustics, deep learning, howling, monitoring, wolf
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: BF - Biotechnical Faculty
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: Str. 58-70
• Numbering: Vol. 12, iss. 1
• PID: 20.500.12556/RUL-180425
• UDC: [599.744.111.1:591.582]:004.85
• ISSN on article: 2056-3485
• DOI: 10.1002/rse2.70024
• COBISS.SI-ID: 250212611
• Publication date in RUL: 09.03.2026

Record is a part of a journal

• Title: Remote sensing in ecology and conservation
• Shortened title: Remote sens. ecol. conserv.
• Publisher: Zoological Society of London
• ISSN: 2056-3485
• COBISS.SI-ID: 525560345

Licences

• License: CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International
• Link: http://creativecommons.org/licenses/by-nc/4.0/
• Description: A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.

Secondary language

• Language: Slovenian
• Keywords: bioakustika, strojno učenje, oglašanje, monitoring, volk, Canis lupus