{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:47:04Z","timestamp":1767923224179,"version":"3.49.0"},"reference-count":19,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Traditional methods used to identify and monitor insect species are time-consuming, costly, and fully dependent on the observer\u2019s ability. This paper presents a deep learning-based cicada species recognition system using acoustic signals to classify the cicada species. The sound recordings of cicada species were collected from different online sources and pre-processed using denoising algorithms. An improved H\u00e4rm\u00e4 syllable segmentation method is introduced to segment the audio signals into syllables since the syllables play a key role in identifying the cicada species. After that, a visual representation of the audio signal was obtained using a spectrogram, which was fed to a convolutional neural network (CNN) to perform classification. The experimental results validated the robustness of the proposed method by achieving accuracies ranging from 66.67% to 100%.<\/jats:p>","DOI":"10.3390\/a15100358","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T20:58:47Z","timestamp":1664398727000},"page":"358","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Cicada Species Recognition Based on Acoustic Signals"],"prefix":"10.3390","volume":"15","author":[{"given":"Wan Teng","family":"Tey","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0901-3831","authenticated-orcid":false,"given":"Tee","family":"Connie","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2848-0291","authenticated-orcid":false,"given":"Kan Yeep","family":"Choo","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia"}]},{"given":"Michael Kah Ong","family":"Goh","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Noda, J.J., Travieso-Gonz\u00e1lez, C.M., S\u00e1nchez-Rodr\u00edguez, D., and Alonso-Hern\u00e1ndez, J.B. 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