Systematic Review on Machine Learning Applied to Bioacoustics using the PRISMA Method
Abstract
Using the most appropriate technique is crucial in any Machine Learning process. In bioacoustics, due to the complexity of the data, many techniques have been applied and developed. In this context, this paper presents the systematic review carried out using the PRISMA methodology for Bioacoustics in environmental monitoring through bird vocalization. The review demonstrates that the Spiking Neural Network, Convolutional Neural Network and Residual Neural Network techniques have gained prominence in recent years.
Keywords:
bioacoustic, neural networks, machine learning, classification, bird monitoring
References
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Tivarekar, R. P., Chavan, V. D., Shete, S. A., and Vartak, A. (2018). Audio based bird species recognition using naÏve bayes algorithm. International Journal of modern Trends in Engineering and Research (IJMTER).
Weerasena, H., Jayawardhana, M., Egodage, D., Fernando, H., Sooriyaarachchi, S., Gamage, C., and Kottege, N. (2018). Continuous automatic bioacoustics monitoring of bird calls with local processing on node level. In TENCON 2018 - 2018 IEEE Region 10 Conference, pages 0235–0239.
Xiao, H., Liu, D., Chen, K., and Zhu, M. (2022). Amresnet: An automatic recognition model of bird sounds in real environment. Applied Acoustics, 201:109121.
Xie, J., Yang, J., Ding, C., and Li, W. (2020). High accuracy individual identification model of crested ibis (nipponia nippon) based on autoencoder with self-attention. IEEE Access, 8:41062–41070.
Yang, F., Jiang, Y., and Xu, Y. (2022). Design of bird sound recognition model based on lightweight. IEEE Access, 10:85189–85198.
Zhao, Z., hua Zhang, S., yong Xu, Z., Bellisario, K., hua Dai, N., Omrani, H., and Pijanowski, B. C. (2017). Automated bird acoustic event detection and robust species classification. Ecological Informatics, 39:99–108.
B. T. Padovese, L. R. P. (2019). Machine learning for identifying an endangered brazilian psittacidae species. Journal of Environmental Informatics Letters.
de Oliveira, A. G., Ventura, T. M., Ganchev, T. D., Silva, L. N., Marques, M. I., and Schuchmann, K.-L. (2020). Speeding up training of automated bird recognizers by data reduction of audio features. PeerJ, 8:e8407.
Ferreira, A. (2011). Assessment of heavy metals in egretta thula: case study: Coroa grande mangrove, sepetiba bay, rio de janeiro, brazil. Brazilian Journal of Biology, 71(1):77–82.
Hidayat, A. A., Cenggoro, T. W., and Pardamean, B. (2021). Convolutional neural networks for scops owl sound classification. Procedia Computer Science, 179:81–87. 5th International Conference on Computer Science and Computational Intelligence 2020.
Hu, S., Chu, Y., Wen, Z., Zhou, G., Sun, Y., and Chen, A. (2023). Deep learning bird song recognition based on mff-scsenet. Ecological Indicators, 154:110844.
Jing, L., Liu, B., Choi, J., Janin, A., Bernd, J., Mahoney, M. W., and Friedland, G. (2016). A discriminative and compact audio representation for event detection. In Proceedings of the 24th ACM International Conference on Multimedia, MM ’16, page 57–61, New York, NY, USA. Association for Computing Machinery.
Kvsn, R. R., Montgomery, J., Garg, S., and Charleston, M. (2020). Bioacoustics data analysis – a taxonomy, survey and open challenges. IEEE Access, 8:57684–57708.
Maegawa, Y., Ushigome, Y., Suzuki, M., Taguchi, K., Kobayashi, K., Haga, C., and Matsui, T. (2021). A new survey method using convolutional neural networks for automatic classification of bird calls. Ecological Informatics, 61:101164.
Mohanty, R., Bhuyan, H. K., Pani, S. K., Ravi, V., and Krichen, M. (2023). Bird species recognition using spiking neural network along with distance based fuzzy co-clustering. International Journal of Speech Technology, pages 1–14.
Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. BMJ, 339.
Tivarekar, R. P., Chavan, V. D., Shete, S. A., and Vartak, A. (2018). Audio based bird species recognition using naÏve bayes algorithm. International Journal of modern Trends in Engineering and Research (IJMTER).
Weerasena, H., Jayawardhana, M., Egodage, D., Fernando, H., Sooriyaarachchi, S., Gamage, C., and Kottege, N. (2018). Continuous automatic bioacoustics monitoring of bird calls with local processing on node level. In TENCON 2018 - 2018 IEEE Region 10 Conference, pages 0235–0239.
Xiao, H., Liu, D., Chen, K., and Zhu, M. (2022). Amresnet: An automatic recognition model of bird sounds in real environment. Applied Acoustics, 201:109121.
Xie, J., Yang, J., Ding, C., and Li, W. (2020). High accuracy individual identification model of crested ibis (nipponia nippon) based on autoencoder with self-attention. IEEE Access, 8:41062–41070.
Yang, F., Jiang, Y., and Xu, Y. (2022). Design of bird sound recognition model based on lightweight. IEEE Access, 10:85189–85198.
Zhao, Z., hua Zhang, S., yong Xu, Z., Bellisario, K., hua Dai, N., Omrani, H., and Pijanowski, B. C. (2017). Automated bird acoustic event detection and robust species classification. Ecological Informatics, 39:99–108.
Published
2023-11-28
How to Cite
MARTINS, Luiz E. R.; DOS SANTOS, Virgínia A.; DE OLIVEIRA, Allan G.; VENTURA, Thiago M.; SIMÕES, Nielsen Cassiano.
Systematic Review on Machine Learning Applied to Bioacoustics using the PRISMA Method. In: REGIONAL SCHOOL ON INFORMATICS OF MATO GROSSO (ERI-MT), 12. , 2023, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 251-255.
ISSN 2447-5386.
DOI: https://doi.org/10.5753/eri-mt.2023.236622.
