{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:06:29Z","timestamp":1761897989996,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad del Sin\u00fa"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Acoustic management is very important for detecting possible events in the context of a smart environment (SE). In previous works, we proposed a reflective middleware for acoustic management (ReM-AM) and its autonomic cycles of data analysis tasks, along with its ontology-driven architecture. In this work, we aim to develop an emotion-recognition system for ReM-AM that uses sound events, rather than speech, as its main focus. The system is based on a sound pattern for emotion recognition and the autonomic cycle of intelligent sound analysis (ISA), defined by three tasks: variable extraction, sound data analysis, and emotion recommendation. We include a case study to test our emotion-recognition system in a simulation of a smart movie theater, with different situations taking place. The implementation and verification of the tasks show a promising performance in the case study, with 80% accuracy in sound recognition, and its general behavior shows that it can contribute to improving the well-being of the people present in the environment.<\/jats:p>","DOI":"10.3390\/info15110677","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T12:25:39Z","timestamp":1730291139000},"page":"677","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Emotion-Recognition System for Smart Environments Using Acoustic Information (ERSSE)"],"prefix":"10.3390","volume":"15","author":[{"given":"Gabriela","family":"Santiago","sequence":"first","affiliation":[{"name":"Centro de Microcomputaci\u00f3n y Sistemas Distribuidos (CEMISID), Department of Computing, Faculty of Engineering, Universidad de Los Andes, M\u00e9rida 5101, Venezuela"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4194-6882","authenticated-orcid":false,"given":"Jose","family":"Aguilar","sequence":"additional","affiliation":[{"name":"Centro de Microcomputaci\u00f3n y Sistemas Distribuidos (CEMISID), Department of Computing, Faculty of Engineering, Universidad de Los Andes, M\u00e9rida 5101, Venezuela"},{"name":"GIDITIC, Universidad EAFIT, Medell\u00edn 050022, Colombia"},{"name":"IMDEA Networks Institute, Legan\u00e9s, 28918 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodrigo","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Universidad del Sin\u00fa, Monter\u00eda 230002, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121692","DOI":"10.1016\/j.eswa.2023.121692","article-title":"Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: A systematic review of recent advancements and future prospects","volume":"237","author":"Zhang","year":"2023","journal-title":"Expert Syst. 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