{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:45:25Z","timestamp":1760060725894,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018928","name":"Instituto Tecnol\u00f3gico Metropolitano","doi-asserted-by":"publisher","award":["PF2410"],"award-info":[{"award-number":["PF2410"]}],"id":[{"id":"10.13039\/501100018928","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Soundscape monitoring has become an increasingly important tool for studying ecological processes and supporting habitat conservation. While many recent advances focus on identifying species through supervised learning, there is growing interest in understanding the soundscape as a whole while considering patterns that extend beyond individual vocalizations. This broader view requires unsupervised approaches capable of capturing meaningful structures related to temporal dynamics, frequency content, spatial distribution, and ecological variability. In this study, we present a fully unsupervised framework for analyzing large-scale soundscape data using deep learning. We applied a convolutional autoencoder (Soundscape-Net) to extract acoustic representations from over 60,000 recordings collected across a grid-based sampling design in the Rey Zamuro Reserve in Colombia. These features were initially compared with other audio characterization methods, showing superior performance in multiclass classification, with accuracies of 0.85 for habitat cover identification and 0.89 for time-of-day classification across 13 days. For the unsupervised study, optimized dimensionality reduction methods (Uniform Manifold Approximation and Projection and Pairwise Controlled Manifold Approximation and Projection) were applied to project the learned features, achieving trustworthiness scores above 0.96. Subsequently, clustering was performed using KMeans and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), with evaluations based on metrics such as the silhouette, where scores above 0.45 were obtained, thus supporting the robustness of the discovered latent acoustic structures. To interpret and validate the resulting clusters, we combined multiple strategies: spatial mapping through interpolation, analysis of acoustic index variance to understand the cluster structure, and graph-based connectivity analysis to identify ecological relationships between the recording sites. Our results demonstrate that this approach can uncover both local and broad-scale patterns in the soundscape, providing a flexible and interpretable pathway for unsupervised ecological monitoring.<\/jats:p>","DOI":"10.3390\/make7040109","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T13:16:11Z","timestamp":1758719771000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Soundscape Characterization and Pattern Analysis Using Low-Dimensional Deep Embeddings on a Large-Scale Dataset"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6046-4622","authenticated-orcid":false,"given":"Daniel Alexis","family":"Nieto Mora","sequence":"first","affiliation":[{"name":"Laboratorio de M\u00e1quinas Inteligentes y Reconocimiento de Patrones MIRP, Instituto Tecnol\u00f3gico Metropolitano\u2014ITM, Medell\u00edn 050034, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7115-3870","authenticated-orcid":false,"given":"Leonardo","family":"Duque-Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Laboratorio de M\u00e1quinas Inteligentes y Reconocimiento de Patrones MIRP, Instituto Tecnol\u00f3gico Metropolitano\u2014ITM, Medell\u00edn 050034, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7037-6925","authenticated-orcid":false,"given":"Juan David","family":"Mart\u00ednez Vargas","sequence":"additional","affiliation":[{"name":"School of Applied Sciences and Engineering, EAFIT University, Medell\u00edn 050022, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109017","DOI":"10.1016\/j.ecolind.2022.109017","article-title":"Automatic acoustic heterogeneity identification in transformed landscapes from Colombian tropical dry forests","volume":"140","author":"Rendon","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Noble, A.E., Jensen, F.H., Jarriel, S.D., Aoki, N., Ferguson, S.R., Hyer, M.D., Apprill, A., and Mooney, T.A. (2024). Unsupervised clustering reveals acoustic diversity and niche differentiation in pulsed calls from a coral reef ecosystem. Front. Remote Sens., 5.","DOI":"10.3389\/frsen.2024.1429227"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e2108","DOI":"10.7717\/peerj.2108","article-title":"A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods","volume":"4","author":"Eldridge","year":"2016","journal-title":"PeerJ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20513","DOI":"10.1007\/s00521-024-10157-7","article-title":"Leveraging time-based acoustic patterns for ecosystem analysis","volume":"36","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17049","DOI":"10.1073\/pnas.2004702117","article-title":"Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set","volume":"117","author":"Sethi","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1121\/10.0022408","article-title":"AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyance","volume":"154","author":"Hou","year":"2023","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Colonna, J.G., Carvalho, J.R., and Rosso, O.A. (2020). Estimating ecoacoustic activity in the Amazon rainforest through information theory quantifiers. PLoS ONE, 15.","DOI":"10.1101\/2020.02.09.940916"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101236","DOI":"10.1016\/j.ecoinf.2021.101236","article-title":"BirdNET: A deep learning solution for avian diversity monitoring","volume":"61","author":"Kahl","year":"2021","journal-title":"Ecol. Informatics"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sharma, S., Sato, K., and Gautam, B.P. (2023). A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques. Sustainability, 15.","DOI":"10.3390\/su15097128"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1038\/s41467-022-27980-y","article-title":"Perspectives in machine learning for wildlife conservation","volume":"13","author":"Tuia","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e20275","DOI":"10.1016\/j.heliyon.2023.e20275","article-title":"Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring","volume":"9","year":"2023","journal-title":"Heliyon"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gibb, K.A., Eldridge, A., Sandom, C.J., and Simpson, I.J. (2023). Towards interpretable learned representations for ecoacoustics using variational auto-encoding. bioRxiv.","DOI":"10.1101\/2023.09.07.556690"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ecolind.2015.05.057","article-title":"Connecting soundscape to landscape: Which acoustic index best describes landscape configuration?","volume":"58","author":"Fuller","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106373","DOI":"10.1016\/j.envsoft.2025.106373","article-title":"Letting ecosystems speak for themselves: An unsupervised methodology for mapping landscape acoustic heterogeneity","volume":"187","author":"Rendon","year":"2025","journal-title":"Environ. Model. Softw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1111\/2041-210X.70041","article-title":"Graphical representation of landscape heterogeneity identification through unsupervised acoustic analysis","volume":"16","author":"Guerrero","year":"2025","journal-title":"Methods Ecol. Evol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2216","DOI":"10.7717\/peerj-cs.2216","article-title":"piRNA-disease association prediction based on multi-channel graph variational autoencoder","volume":"10","author":"Sun","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e327","DOI":"10.7717\/peerj-cs.327","article-title":"Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: A comparative evaluation","volume":"6","author":"Vaiyapuri","year":"2020","journal-title":"PeerJ Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e1214","DOI":"10.7717\/peerj-cs.1214","article-title":"Anomaly detection for blueberry data using sparse autoencoder-support vector machine","volume":"9","author":"Wei","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1111\/2041-210X.13901","article-title":"Deep learning as a tool for ecology and evolution","volume":"13","author":"Borowiec","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1111\/2041-210X.13827","article-title":"A deep Generative Artificial Intelligence system to predict species coexistence patterns","volume":"13","author":"Hirn","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1111\/2041-210X.14239","article-title":"ECOGEN: Bird sounds generation using deep learning","volume":"15","author":"Guei","year":"2024","journal-title":"Methods Ecol. Evol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101237","DOI":"10.1016\/j.ecoinf.2021.101237","article-title":"Acoustic auto-encoders for biodiversity assessment","volume":"62","author":"Rowe","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_23","first-page":"218","article-title":"Insights from Deep Learning in Feature Extraction for Non-supervised Multi-species Identification in Soundscapes","volume":"13788","author":"Guerrero","year":"2022","journal-title":"Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.)"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"348","DOI":"10.15446\/abc.v22n3.63561","article-title":"Seed dispersal by bats over successional gradients in the Colombian orinoquia (San Martin, Meta, Colombia)","volume":"22","year":"2017","journal-title":"Acta Biol\u00f3gica Colomb."},{"key":"ref_25","unstructured":"Ram\u00edrez B, H., Mej\u00eda, W., and Barrera Zambrano, V.A. (2023). Flora al Interior del \u00c1rea 1 del Banco de H\u00e1bitat del Meta de Terrasos. v2.9, SiB Colombia."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nieto-Mora, D.A., Ferreira de Oliveira, M.C., Sanchez-Giraldo, C., Duque-Mu\u00f1oz, L., Isaza-Narv\u00e1ez, C., and Mart\u00ednez-Vargas, J.D. (2024). Soundscape Characterization Using Autoencoders and Unsupervised Learning. Sensors, 24.","DOI":"10.3390\/s24082597"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102449","DOI":"10.1016\/j.ecoinf.2023.102449","article-title":"Towards interpretable learned representations for ecoacoustics using variational auto-encoding","volume":"80","author":"Gibb","year":"2024","journal-title":"Ecol. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, L., Xu, Z., and Zhao, Z. (2022). Biotic sound SNR influence analysis on acoustic indices. Front. Remote Sens., 3.","DOI":"10.3389\/frsen.2022.1079223"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Omprakash, A., Balakrishnan, R., Ewers, R., and Sethi, S. (2024). Interpretable and Robust Machine Learning for Exploring and Classifying Soundscape Data. bioRxiv.","DOI":"10.1101\/2024.11.07.622465"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1111\/2041-210X.14361","article-title":"Time series methods for the analysis of soundscapes and other cyclical ecological data","volume":"15","author":"Yoh","year":"2024","journal-title":"Methods Ecol. Evol."},{"key":"ref_31","first-page":"1","article-title":"Improving the integration of artificial intelligence into existing ecological inference workflows","volume":"2024","author":"Cowans","year":"2024","journal-title":"Methods Ecol. Evol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2334","DOI":"10.1111\/2041-210X.13711","article-title":"scikit-maad: An open-source and modular toolbox for quantitative soundscape analysis in Python","volume":"12","author":"Ulloa","year":"2021","journal-title":"Methods Ecol. Evol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., and Ritter, M. (2017, January 5\u20139). Audio Set: An ontology and human-labeled dataset for audio events. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Castro-Ospina, A.E., Solarte-Sanchez, M.A., Vega-Escobar, L.S., Isaza, C., and Mart\u00ednez-Vargas, J.D. (2024). Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks. Sensors, 24.","DOI":"10.3390\/s24072106"},{"key":"ref_35","unstructured":"McInnes, L., Healy, J., and Melville, J. (2020). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv."},{"key":"ref_36","first-page":"1","article-title":"Understanding How Dimension Reduction Tools Work : An","volume":"22","author":"Wang","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","first-page":"485","article-title":"Neighborhood preservation in nonlinear projection methods: An experimental study","volume":"2130","author":"Venna","year":"2001","journal-title":"Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.)"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.ecolind.2016.12.018","article-title":"Automatic identification of rainfall in acoustic recordings","volume":"75","author":"Bedoya","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102258","DOI":"10.1016\/j.ecoinf.2023.102258","article-title":"Learning to detect an animal sound from five examples","volume":"77","author":"Nolasco","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s12304-016-9266-3","article-title":"The Application of the Acoustic Complexity Indices (ACI) to Ecoacoustic Event Detection and Identification (EEDI) Modeling","volume":"9","author":"Farina","year":"2016","journal-title":"Biosemiotics"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ulloa, J.S., Aubin, T., Llusia, D., Courtois, \u00c9.A., Fouquet, A., Gaucher, P., Pavoine, S., and Sueur, J. (2019). Explosive breeding in tropical anurans: Environmental triggers, community composition and acoustic structure. BMC Ecol., 19.","DOI":"10.1186\/s12898-019-0243-y"},{"key":"ref_42","first-page":"61","article-title":"Acoustic indices as proxies for biodiversity in certified and non-certified cocoa plantations in Indonesia","volume":"197","author":"Budi","year":"2025","journal-title":"Environ. Monit. Assess."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1038\/s41467-020-15351-4","article-title":"Dimensionality reduction by UMAP to visualize physical and genetic interactions","volume":"11","author":"Dorrity","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Poblete, V., Espejo, D., Vargas, V., Otondo, F., and Huijse, P. (2021). Characterization of sonic events present in natural-urban hybrid habitats using umap and sednet: The case of the urban wetlands. Appl. Sci., 11.","DOI":"10.3390\/app11178175"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1111\/1365-2656.13754","article-title":"A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations","volume":"91","author":"Thomas","year":"2022","journal-title":"J. Anim. Ecol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s40543-025-00480-6","article-title":"Advancing dimensionality reduction for enhanced visualization and clustering in single-cell transcriptomics","volume":"16","author":"Sanju","year":"2025","journal-title":"J. Anal. Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s12304-015-9248-x","article-title":"Ecoacoustics: The Ecological Investigation and Interpretation of Environmental Sound","volume":"8","author":"Sueur","year":"2015","journal-title":"Biosemiotics"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ye, Z., Du, Y., Mao, Y., Liu, Y., Wu, Z., and Wang, J. (2022, January 13\u201316). AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density. Proceedings of the 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), Shenzhen, China.","DOI":"10.1109\/DSAA54385.2022.10032412"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"121860","DOI":"10.1016\/j.eswa.2023.121860","article-title":"Density peak clustering algorithms: A review on the decade 2014\u20132023","volume":"238","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1111\/cobi.13119","article-title":"Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity","volume":"32","author":"Buxton","year":"2018","journal-title":"Conserv. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.landurbplan.2017.01.014","article-title":"Do acoustic indices reflect the characteristics of bird communities in the savannas of Central Brazil?","volume":"162","author":"Machado","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1111\/2041-210X.13254","article-title":"Guidelines for the use of acoustic indices in environmental research","volume":"10","author":"Gardner","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_53","first-page":"319","article-title":"Environmental sound as a mirror of landscape ecological integrity in monitoring programs","volume":"19","author":"Daza","year":"2021","journal-title":"Perspect. Ecol. Conserv."},{"key":"ref_54","first-page":"2","article-title":"What do insects, anurans, birds, and mammals have to say about soundscape indices in a tropical savanna","volume":"2","author":"Ferreira","year":"2018","journal-title":"J. Ecoacoust."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1111\/2041-210X.13042","article-title":"Active learning for classifying long-duration audio recordings of the environment","volume":"9","author":"Kholghi","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_56","first-page":"1040","article-title":"The Acoustic Index User\u2019s Guide: A practical manual for defining, generating and understanding current and future acoustic indices","volume":"16","author":"Duthie","year":"2024","journal-title":"Methods Ecol. Evol."},{"key":"ref_57","unstructured":"Towsey, M.W. (2025, September 18). Noise Removal from Wave-Forms and Spectrograms Derived from Natural Recordings of the Environment. Available online: http:\/\/eprints.qut.edu.au\/61399\/."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.ecoinf.2013.11.007","article-title":"The use of acoustic indices to determine avian species richness in audio-recordings of the environment","volume":"21","author":"Towsey","year":"2014","journal-title":"Ecol. Inform."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/j.1654-1103.2005.tb02393.x","article-title":"Rao\u2019s quadratic entropy as a measure of functional diversity based on multiple traits","volume":"16","year":"2005","journal-title":"J. Veg. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"112954","DOI":"10.1016\/j.ecolind.2024.112954","article-title":"The efficacy of acoustic indices for monitoring abundance and diversity in soil soundscapes","volume":"169","author":"Metcalf","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1016\/j.ecolind.2010.11.005","article-title":"A new methodology to infer the singing activity of an avian community: The Acoustic Complexity Index (ACI)","volume":"11","author":"Pieretti","year":"2011","journal-title":"Ecol. Indic."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ecoinf.2012.08.001","article-title":"The remote environmental assessment laboratory\u2019s acoustic library: An archive for studying soundscape ecology","volume":"12","author":"Kasten","year":"2012","journal-title":"Ecol. Inform."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1525\/bio.2011.61.3.6","article-title":"Soundscape ecology: The science of sound in the landscape","volume":"61","author":"Pijanowski","year":"2011","journal-title":"BioScience"},{"key":"ref_64","unstructured":"Rudnick, D., Ryan, S.J., Beier, P., Cushman, S.A., Dieffenbach, F., and Trombulak, S.C. (2012). The Role of Landscape Connectivity in Planning and Implementing Conservation and Restoration Priorities, Ecological Society of America. Issues in Ecology."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1146\/annurev-ecolsys-102209-144718","article-title":"From graphs to spatial graphs","volume":"41","author":"Dale","year":"2010","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"25002","DOI":"10.1088\/2752-664X\/ad4bec","article-title":"Soundscape mapping: Understanding regional spatial and temporal patterns of soundscapes incorporating remotely-sensed predictors and wildfire disturbance","volume":"3","author":"Quinn","year":"2024","journal-title":"Environ. Res. Ecol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"201309","DOI":"10.1098\/rsos.201309","article-title":"Dynamic spatio-temporal patterns of metapopulation occupancy in patchy habitats","volume":"8","author":"Bertassello","year":"2021","journal-title":"R. Soc. Open Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"120089","DOI":"10.1016\/j.eswa.2023.120089","article-title":"Explainable automated anuran sound classification using improved one-dimensional local binary pattern and Tunable Q Wavelet Transform techniques","volume":"225","author":"Akbal","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e02056","DOI":"10.1002\/eap.2056","article-title":"Modeling avian full annual cycle distribution and population trends with citizen science data","volume":"30","author":"Fink","year":"2020","journal-title":"Ecol. Appl."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:49:01Z","timestamp":1760035741000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,24]]},"references-count":69,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040109"],"URL":"https:\/\/doi.org\/10.3390\/make7040109","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2025,9,24]]}}}