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David Mech, L. & Boitani, L. Wolves: Behavior, ecology, and conservation (University of Chicago Press, 2010).
Poyarkov, A. D., Korablev, M. P., Bragina, E. & Hernandez-Blanco, J. A. Overview of current research on wolves in Russia. Front. Ecol. Evol.10, 869161 (2022).
Smith, D. W. & Bangs, E. E. Reintroduction of wolves to Yellowstone National Park: history, values and ecosystem restoration. Reintroduction of top-order predators 92–125 (2009).
Hoag, D. et al. Economic consequences of the wolf comeback in the western United States (2022). Preprint at https://digitalcommons.unl.edu/icwdm_usdanwrc/2584/.
Rode, J., Flinzberger, L., Karutz, R., Berghöfer, A. & Schröter-Schlaack, C. Why so negative? Exploring the socio-economic impacts of large carnivores from a European perspective. Biol. Conserv. 255, 108918 (2021).
Di Bernardi, C. et al. Continuing recovery of wolves in Europe. PLOS sustainability and transformation4, e0000158 (2025).
Raynor, J. L., Grainger, C. A. & Parker, D. P. Wolves make roadways safer, generating large economic returns to predator conservation. Proc. Natl. Acad. Sci. U. S. A. 118, e2023251118 (2021).
Thomsen, B. Wolf ecotourism: A posthumanist approach to wildlife ecotourism. In Routledge handbook of ecotourism, 117–131 (Routledge, 2021).
Hernandez-Blanco, J., Poyarkov, A. & Krutova, V. Wolf (Canis lupus lupus) pack organization at the Voronezh Biosphere Reserve. Zool. Zhurnal 84, 80–93 (2005).
Nikolskii, A. & Frommolt, K. Zvukovaya aktivnost volka (Izdatelstvo Moskovskogo Universiteta, 1989).
Mech, L. D. & Boitani, L. Wolves: behavior, ecology, and conservation (University of Chicago Press, 2019).
Schassburger, R. M. Wolf vocalization: An integrated model of structure, motivation and ontogeny. In Man and wolf : Advances, issues and problems in captive wolf research, 313–347 (1987).
Coscia, E. M., Phillips, D. P. & Fentress, J. C. Spectral analysis of neonatal wolf vocalizations. Bioacoustics3, 275–293 (1991).
Larsen, H. L. et al. Bioacoustic detection of wolves: Identifying subspecies and individuals by howls. Animals12, 631 (2022).
Sadhukhan, S., Root-Gutteridge, H. & Habib, B. Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method. Sci. Reports 11, 7309 (2021).
Harrington, F. H. & Mech, L. D. Wolf howling and its role in territory maintenance. Behaviour 68, 207–249 (1979).
Harrington, F. H. & Mech, L. D. An analysis of howling response parameters useful for wolf pack censusing. J. Wildl. Manage.https://doi.org/10.2307/3808560 (1982).
Zaccaroni, M. et al. Group specific vocal signature in free-ranging wolf packs. Ethol. Ecol. Evol.24, 322–331 (2012).
Palacios, V., Font, E. & Márquez, R. Iberian wolf howls: Acoustic structure, individual variation, and a comparison with North American populations. J. Mammal.88, 606–613 (2007).
Root-Gutteridge, H. et al. Improving individual identification in captive Eastern grey wolves (Canis lupus lycaon) using the time course of howl amplitudes. Bioacoustics23, 39–53 (2014).
Papin, M., Pichenot, J. & Germain, E. La bioacoustique: un outil prometteur pour l’estimation des effectifs de loups gris. In 11e Rencontres Bourgogne-Nature et du 37e Colloque francophone de Mammalogie, Les Mammifères sauvages-Recolonisation et réémergence, Revue Scientifique Bourgogne. Nature, vol. 21, 256–65 (2015).
Ross, S.R.-J. et al. Passive acoustic monitoring provides a fresh perspective on fundamental ecological questions. Funct. Ecol.37, 959–975 (2023).
Barber-Meyer, S. M., Palacios, V., Marti-Domken, B. & Schmidt, L. J. Testing a new passive acoustic recording unit to monitor wolves. Wildlife Soc. Bull.44, 590–598 (2020).
Sossover, D., Burrows, K., Kahl, S. & Wood, C. M. Using the birdnet algorithm to identify wolves, coyotes, and potentially their interactions in a large audio dataset. Mamm. Res.69, 159–165 (2024).
Joslin, P. W. Movements and home sites of timber wolves in al?onquin park. Am. Zool. 7, 279–288 (1967).
Root-Gutteridge, H. et al. Identifying individual wild eastern grey wolves (Canis lupus lycaon) using fundamental frequency and amplitude of howls. Bioacoustics23, 55–66 (2014).
Whytock, R. C. & Christie, J. Solo: An open source, customizable and inexpensive audio recorder for bioacoustic research. Methods Ecol. Evol.8, 308–312 (2017).
Sadhukhan, S., Root-Gutteridge, H. & Habib, B. Identifying unknown Indian wolves by their distinctive howls: Its potential as a non-invasive survey method. Sci. Rep.11, 7309 (2021).
Papin, M., Pichenot, J., Guérold, F. & Germain, E. Acoustic localization at large scales: A promising method for grey wolf monitoring. Front. Zool.15, 1–10 (2018).
Suter, S. M., Giordano, M., Nietlispach, S., Apollonio, M. & Passilongo, D. Non-invasive acoustic detection of wolves. Bioacoustics 26, 237–248 (2017).
Garland, L., Crosby, A., Hedley, R., Boutin, S. & Bayne, E. Acoustic vs. photographic monitoring of gray wolves (Canis lupus): A methodological comparison of two passive monitoring techniques. Can. J. Zool.98, 219–228 (2020).
Passilongo, D., Mattioli, L., Bassi, E., Szabó, L. & Apollonio, M. Visualizing sound: Counting wolves by using a spectral view of the chorus howling. Front. Zool.12, 1–10 (2015).
Hennelly, L., Habib, B., Root-Gutteridge, H., Palacios, V. & Passilongo, D. Howl variation across Himalayan, North African, Indian, and Holarctic wolf clades: tracing divergence in the world s oldest wolf lineages using acoustics. Current zoology 63, 341–348 (2017).
Benesty, J., Sondhi, M. M., Huang, Y. et al. Springer handbook of speech processing, vol. 1 (Springer, 2008).
Kheddar, H., Hemis, M. & Himeur, Y. Automatic speech recognition using advanced deep learning approaches: A survey. Inf. Fusionhttps://doi.org/10.1016/j.inffus.2024.102422 (2024).
Savchenko, A. V. & Savchenko, L. V. Towards the creation of reliable voice control system based on a fuzzy approach. Pattern Recogn. Lett.65, 145–151 (2015).
Savchenko, V. & Savchenko, A. Information-theoretic analysis of efficiency of the phonetic encoding-decoding method in automatic speech recognition. J. Commun. Technol. Electron.61, 430–435 (2016).
Savchenko, A. V., Savchenko, V. V. & Savchenko, L. V. Gain-optimized spectral distortions for pronunciation training. Optim. Lett.16, 2095–2113 (2022).
Kaneko, T., Tanaka, K., Kameoka, H. & Seki, S. ISTFTNET: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform. ICASSP 2022 – 2022 IEEE Int. Conf. on Acoust. Speech and Signal Processing (ICASSP) 6207–6211 (2022).
Zhang, T., Feng, G., Liang, J. & An, T. Acoustic scene classification based on Mel spectrogram decomposition and model merging. Appl. Acoust.182, 108258. https://doi.org/10.1016/j.apacoust.2021.108258 (2021).
Yost, W. Fundamentals of Hearing: An Introduction (Brill, 2013).
Tawaqal, B. & Suyanto, S. Recognizing five major dialects in Indonesia based on MFCC and DRNN. J. Phys. Conf. Ser.https://doi.org/10.1088/1742-6596/1844/1/012003 (2021).
Cai, D., Qin, X. & Li, M. Multi-Channel Training for End-to-End Speaker Recognition Under Reverberant and Noisy Environment. In Interspeech (2019).
He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. 2016 IEEE Conf. on Comput. Vis. Pattern Recognit. (CVPR) 770–778 (2015).
Tan, M. Efficientnet: Rethinking model scaling for convolutional neural networks. Preprint at https://arxiv.org/abs/1905.11946 (2019).
Liu, Z. et al. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, 10012–10022 (2021).
Dosovitskiy, A. et al. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. Preprint at https://arxiv.org/abs/2010.11929 (2020).
Yang, Z., Qiu, Z. & Xie, H. An image classification method based on self-attention ConvNeXt. In International Conference on Computer Engineering and Networks, 657–666 (Springer, 2022).
Gong, Y., Chung, Y.-A. & Glass, J. AST: Audio Spectrogram Transformer. Preprint at https://arxiv.org/abs/2104.01778 (2021).
Gemmeke, J. F. et al. Audio Set: An ontology and human-labeled dataset for audio events. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 776–780, https://doi.org/10.1109/ICASSP.2017.7952261 (2017).
Radford, A. et al. Robust speech recognition via large-scale weak supervision. In International conference on machine learning, 28492–28518 (PMLR, 2023).
Schneider, S., Baevski, A., Collobert, R. & Auli, M. wav2vec: Unsupervised Pre-training for Speech Recognition. In Interspeech (2019).
Baevski, A., Zhou, Y., Mohamed, A. & Auli, M. wav2vec 2.0: A framework for self-supervised learning of speech representations. Adv. neural information processing systems 33, 12449–12460 (2020).
Piczak, K. J. ESC: Dataset for Environmental Sound Classification. In Proceedings of the 23rd ACM International Conference on Multimedia, MM ’15, 1015 1018, https://doi.org/10.1145/2733373.2806390 (Association for Computing Machinery, 2015).
Bandara, M., Jayasundara, R., Ariyarathne, I., Meedeniya, D. & Perera, C. Forest sound classification dataset: FSC22. Sensorshttps://doi.org/10.3390/s23042032 (2023).
Kahl, S. et al. Overview of BirdCLEF 2022: Endangered bird species recognition in soundscape recordings. In CLEF 2022 – Working Notes of the 23rd Conference and Labs of the Evaluation Forum, vol. 3180 of CEUR Workshop Proceedings, 1929–1939 (2022).
Park, D. S. et al. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. In Interspeech 2019 (ISCA, 2019).
Savchenko, A. V. & Belova, N. S. Statistical testing of segment homogeneity in classification of piecewise-regular objects. Int. J. Appl. Math. Comput. Sci. 25 (2015).
Oikarinen, T. et al. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. J. Acoust. Soc. Am.145, 654 (2019).
Chen, X., Zhao, J., Chen, Y.-H., Zhou, W. & Hughes, A. C. Automatic standardized processing and identification of tropical bat calls using deep learning approaches. Biol. Conserv.241, 108269 (2020).
Kim, C.-I., Cho, Y., Jung, S., Rew, J. & Hwang, E. Animal sounds classification scheme based on multi-feature network with mixed datasets. KSII Trans. Internet Inf. Syst. 14 (2020).
Fang, Z., Yin, B., Du, Z. & Huang, X. Fast environmental sound classification based on resource adaptive convolutional neural network. Sci. Rep.12, 6599 (2022).
Sun, Y., Midori Maeda, T., Solís-Lemus, C., Pimentel-Alarcón, D. & Buřivalová, Z. Classification of animal sounds in a hyperdiverse rainforest using convolutional neural networks with data augmentation. Ecol. Indic.145, 109621. https://doi.org/10.1016/j.ecolind.2022.109621 (2022).
Wu, B., Takamichi, S., Sakti, S. & Nakamura, S. A Transformer Framework for Simultaneous Segmentation, Classification, and Caller Identification of Marmoset Vocalization. Preprint at https://arxiv.org/abs/2410.23279 (2024).
Curless, D. et al. Classification of wolf call types using remote sensor technology. J. Acoust. Soc. Am.121, 3106. https://doi.org/10.1121/1.4782030 (2007).
Singh, N. Classification of animal sound using Convolutional neural network. Preprint at https://arrow.tudublin.ie/scschcomdis/203/ (2020).
Stahli, O., Ost, T. & Studer, T. Development of an AI-based bioacoustic wolf monitoring system. The Int. FLAIRS Conf. Proc. 35 (2022).
Sossover, D., Burrows, K., Kahl, S. & Wood, C. M. Using the BirdNET algorithm to identify wolves, coyotes, and potentially their interactions in a large audio dataset. Mamm. Res.69, 159–165 (2024).
Salamon, J., Jacoby, C. & Bello, J. P. A dataset and taxonomy for urban sound research. In Proceedings of the 22nd ACM international conference on Multimedia, 1041–1044 (2014).
Turpault, N., Serizel, R., Shah, A. P. & Salamon, J. Sound event detection in domestic environments with weakly labeled data and soundscape synthesis. In Workshop on Detection and Classification of Acoustic Scenes and Events (2019).
Fonseca, E., Favory, X., Pons, J., Font, F. & Serra, X. FSD50K: An open dataset of human-labeled sound events. IEEE/ACM Trans. Audio Speech Lang. Process.30, 829–852 (2021).
Kershenbaum, A. et al. Disentangling canid howls across multiple species and subspecies: Structure in a complex communication channel. Behav. Processes124, 149–157 (2016).
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