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Today, machine learning methods have become a powerful tool to help curb the effects of global warming by solving ecological questions. In particular, the Colombian Tropical Dry Forest (TDF) is an important ecosystem that is currently under threat due to deforestation generated by cattle, mining, and urban development since colonial times. One of the urgent challenges in this area is to understand the threatened ecosystems landscape transformation and forest degradation. Traditionally, environmental conservation experts measure these changes using transformation levels (high, medium, low). These levels have been obtained through direct observation, counting species, and measures of spatial variation through the time. Therefore, these methods are invasive to the study landscapes and require large amounts of time analysis. A proficient alternative to classical methods is the passive acoustic monitoring, as they are less invasive to the environment, avoid seeing the difficulty of species from isolated individuals, and help reduce the time of researchers at the sites. Even though too much data is generated, and computational tools have been required for their analysis. This paper proposes a new method to automatically identify the transformation in the Colombian TDF. The method is based on Gaussian Mixture Models (GMM) and Universal Background Model (UBM). In addition, it includes an acoustic indices analysis to select the most informative variables. The GMM proposal was tested in two local sites (La Guajira and Bolivar regions) and achieved an accuracy of 93% and 89% for each one, and it was obtained 84% with the general UBM model.
 V. Hutson, and K. Schmitt, “Permanence and the Dynamics of Biological Systems,” Mathematical biosciences, vol. 111 (1), pp. 1-71, 1992. https://doi.org/10.1016/0025-5564(92)90078-b
 S. L. Pim, S. Bergl, R. Dehgan, A. Giri, C. Jewell, Z. Joppa, L. Kays, and S. Loarie, “Emerging Technologies to Conserve Biodiversity,” Trends in Ecology and Evolution, vol. 30 (11), pp. 685-696, 2015. https://doi.org/10.1016/j.tree.2015.08.008
 R. D. Gregory, and A. Van Strien, “Wild Bird Indicators: Using Composite Population Trends of Birds as Measures of Environmental Health,” Ornithological Science, vol. 9 (1), pp. 3-22, 2010. https://doi.org/10.2326/osj.9.3
 J. Sueur, and A. Farina, “Ecoacoustics: The Ecological Investigation and Interpretation of Environmental Sound,” Biosemiotics, vol. 8 (3), pp. 493-502, 2015. https://doi.org/10.1007/s12304-015-9248-x
 B. C. Pijanowski, B. M. Napoletano, N. G. Pieretti, B. L. Krause, L. Bernie, L. J. Villanueva, S. L. Dumyahn, and A. Farina, “Soundscape Ecology: The Science of Sound in the Landscape,” Bioscience, vol. 61 (3), pp. 203-216, 2011. https://doi.org/10.1525/bio.2011.61.3.6
 J. L. Deichmann O. Acevedo‐Charry, L. Barclay, Z. Burivalova, M. Campos‐Cerqueira, F. d'Horta, E. T. Game, B. L. Gottesman, P. J. Hart, A. K. Kalan, S. Linke, L. Do Nascimento, B. Pijanowski, E. Staaterman, and T. Mitchell Aide, “It’s time to listen: there is much to be learned from the sounds of tropical ecosystems,” BioTropica, vol. 50 (5), pp. 713-718, 2018. https://doi.org/10.1111/btp.12593
 J. Sueur, S. Pavoine, O. Hamerlynck, and S. Duvail, “Rapid acoustic survey for biodiversity appraisal,” PLoS One, vol. 3 (12), e4065, 2008. https://doi.org/10.1371/journal.pone.0004065
 M. Towsey, L. Zhang, M. Cottman-Fields, J. Wimmer, J. Zhang, and P. Roe, “Visualization of long-duration acoustic recordings of the environment,” Procedia Computer Science, vol. 29, pp. 703-712, 2014. https://doi.org/10.1016/j.procs.2014.05.063
 W. E. Gómez, C. V. Isaza, and J. M. Daza, “Identifying disturbed habitats: A new method from acoustic indices,” Ecological Information, vol. 45, pp. 16-25, 2018. https://doi.org/10.1016/j.ecoinf.2018.03.001
 D. C. Duque-Montoya, and C. Isaza, “Automatic Ecosystem Identification Using Psychoacoustical Features,” In Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence, pp. 1-4, 2018. https://doi.org/10.1145/3243250.3243251
 D. C. Duque-Montoya, “Methodology for Ecosystem Change Assessing using Ecoacoustics Analysis,” Master Thesis, Universidad de Antioquia, Colombia, 2018. http://hdl.handle.net/10495/13302
 C. Bedoya, C. Isaza, J. M. Daza, and J. D. López, “Automatic identification of rainfall in acoustic recordings,” Ecological Indicators, vol. 75, pp. 95-100, 2017. https://doi.org/10.1016/j.ecolind.2016.12.018
 W. H. Busby, and W. R. Brecheisen, “Chorusing Phenology and Habitat Associations of the Crawfish Frog, Rana areolata (Anura: Ranidae), in Kansas,” The Southwestern Naturalist, vol. 42 (2), pp. 210-217, 1997.
 D. Saenz, L. Fitzgerald, K. Baum, and R. Conner, “Abiotic correlates of anuran calling phenology: The importance of rain, temperature, and season,” Herpetological Monographs, vol. 20 (1), pp. 64-82, 2006. https://doi.org/10.1655/0733-1347(2007)20[64:acoacp]2.0.co;2.
 P. D. Welch, “The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms,” IEEE Transactions on Audio and Electroacoustics, vol. 15 (2), pp. 70-73, 1967. https://doi.org/10.1109/TAU.1967.1161901.1967
 B. C. Pijanowski, E. M. Brinley Buckley, M. Harner, A. Caven, B. Gottesman, and M. Forsberg, “Assessing biological and environmental effects of a total solar eclipse with passive multimodal technologies,” Ecological Indicators, vol. 95, pp. 353-369, 2018. https://doi.org/10.1016/j.ecolind.2018.07.017
 P. Virtanen, “SciPy 1.0: fundamental algorithms for scientific computing in Python,” Nat. Methods, vol. 17 (3), pp. 261-272, 2020.
 N. N. Kulkarni, and V. K. Bairagi, “Extracting Salient Features for EEG-based Diagnosis of Alzheimer’s Disease Using Support Vector Machine Classifier,” IETE Journal of Research, vol. 63 (1), pp. 11-22, 2017. https://doi.org/10.1080/03772063.2016.1241164
 D. Ellis, “librosa: Audio and Music Signal Analysis in Python,” In Proceedings 14th Python Sci. Conf., pp. 18-24, 2015. https://doi.org/10.25080/majora-7b98e3ed-003
 G. Varoquaux, L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa, and A. Mueller, “Scikit-learn: Machine Learning Without Learning the Machinery,” GetMobile: Mobile Computing and Communications, vol. 19 (1), pp. 29-33, 2015. https://doi.org/10.1145/2786984.2786995
 D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, “Speaker verification using adapted Gaussian mixture models,” Digital Signal Processing., vol. 10 (1-3), pp. 19-41, 2000. https://doi.org/10.1006/dspr.1999.0361
 B. De Coensel, Introducing the temporal aspect in environmental soundscape research, 2007.
 E. P. Kasten, S. H. Gage, J. Fox, and W. Joo, “The remote environmental assessment laboratory’s acoustic library: An archive for studying soundscape ecology,” Ecological Informatics, vol. 12, pp. 50-67, 2012. https://doi.org/10.1016/j.ecoinf.2012.08.001