Combate à caça ilegal de mamíferos usando SED

  • Davi F. Henrique Universidade La Salle
  • Mariana M. Blume Universidade La Salle
  • Aline Duarte Riva Universidade La Salle

Resumo


Para o combate às atividades ilegais de caça, existem abordagens que se utilizam de machine learning para formular melhores estratégias de patrulha. Como forma complementar, propomos um modelo utilizando redes neurais para a detecção de atividades potencialmente ilegais, através do processamento do som capturado em regiões onde existe fauna habitante.
Palavras-chave: Aprendizagem de Máquina, Caça Ilegal, Processamento de Som

Referências

Antunes, A. P., Fewster, R. M., Venticinque, E. M., Peres, C. A., Levi, T., Rohe, F., and Shepard, G. H. (2016). Empty forest or empty rivers? A century of commercial hunting in Amazonia. Science Advances, 2(10):e1600936.

Ardila, R., Branson, M., Davis, K., Henretty, M., Kohler, M., Meyer, J., Morais, R., Saunders, L., Tyers, F. M., and Weber, G. (2020). Common voice: A massively multilingual speech corpus.

Chan, T. K. and Chin, C. S. (2020). A comprehensive review of polyphonic sound event detection. IEEE Access, 8:103339–103373.

Dogan, S. (2021). A new fractal h-tree pattern based gun model identification method using gunshot audios. Applied Acoustics, 177:107916.

Drossos, K., Mimilakis, S. I., Gharib, S., Li, Y., and Virtanen, T. (2020). Sound Event Detection with Depthwise Separable and Dilated Convolutions. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–7, Glasgow, United Kingdom. IEEE.

Ferraro, A., Bogdanov, D., Jeon, J. H., Yoon, J., and Serra, X. (2019). Music auto-tagging using cnns and mel-spectrograms with reduced frequency and time resolution. CoRR, abs/1911.04824.

Fulzele, V., Kulkarni, Y., and Aras, S. (2020). Conservation of wildlife from poaching by using sound detection and machine learning. INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY.

Henrique, D. (2021). Human animal hunting audio dataset.

Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P.-A., and Petitjean, F. (2020). Inception- time: Finding alexnet for time series classification. Data Mining and Knowledge Discovery, 34(6):1936–1962.

Liang, J., Aronson, J. D., and Hauptmann, A. (2019). Technical Report of the Video Event Reconstruction and Analysis (VERA) System – Shooter Localization, Models, Interface, and Beyond. arXiv:1905.13313 [cs]. arXiv: 1905.13313.

Lilien, R. (2018). Development of computational methods for the audio analysis of gunshots.

Piczak, K. J. (2015). ESC: Dataset for Environmental Sound Classification. In Proceedings of the 23rd Annual ACM Conference on Multimedia, pages 1015– 1018. ACM Press.

Purwins, H., Li, B., Virtanen, T., Schl ̈uter, J., Chang, S., and Sainath, T. N. (2019). Deep learning for audio signal processing. CoRR, abs/1905.00078.

Salamon, J., Jacoby, C., and Bello, J. P. (2014). A dataset and taxonomy for urban sound research. In 22nd ACM International Conference on Multimedia (ACMMM’14), pages 1041–1044, Orlando, FL, USA.

Xu, L., Bondi, E., Fang, F., Perrault, A., Wang, K., and Tambe, M. (2020a). Dual- Mandate Patrols: Multi-Armed Bandits for Green Security. arXiv:2009.06560 [cs, stat]. arXiv: 2009.06560.

Xu, L., Gholami, S., McCarthy, S., Dilkina, B., Plumptre, A., Tambe, M., Singh, R., Nsubuga, M., Mabonga, J., Driciru, M., Wanyama, F., Rwetsiba, A., Okello, T., and Enyel, E. (2020b). Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (Short Version). In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pages 1898–1901, Dallas, TX, USA. IEEE.
Publicado
01/06/2022
Como Citar

Selecione um Formato
HENRIQUE, Davi F.; BLUME, Mariana M.; RIVA, Aline Duarte. Combate à caça ilegal de mamíferos usando SED. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO RIO GRANDE DO SUL, 2. , 2022, Canoas. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 56-63. DOI: https://doi.org/10.5753/ercomprs.2022.20406.