{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:42:55Z","timestamp":1747190575777,"version":"3.40.5"},"reference-count":20,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T00:00:00Z","timestamp":1627862400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2018YFC0830602"],"award-info":[{"award-number":["2018YFC0830602"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,8,2]]},"abstract":"<jats:p>Using fake audio to spoof the audio devices in the Internet of Things has become an important problem in modern network security. Aiming at the problem of lack of robust features in fake audio detection, an audio streams\u2019 hidden feature extraction method based on a heuristic mask for empirical mode decomposition (HM-EMD) is proposed in this paper. First, using HM-EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). Then, on the basis of IMFs, basic features and hidden information features HCFs of audio streams are constructed, respectively. Finally, a machine learning method is used to classify audio streams based on these features. The experimental results show that hidden information features of audio streams based on HM-EMD can effectively supplement the nonlinear and nonstationary information that traditional features such as mel cepstrum features cannot express and can better realize the representation of hidden acoustic events, which provide a new research idea for fake audio detection.<\/jats:p>","DOI":"10.1155\/2021\/5566347","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T22:25:33Z","timestamp":1627943133000},"page":"1-12","source":"Crossref","is-referenced-by-count":5,"title":["Feature Extraction Method for Hidden Information in Audio Streams Based on HM-EMD"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5496-7194","authenticated-orcid":true,"given":"Jiu","family":"Lou","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China"}]},{"given":"Zhongliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8386-2858","authenticated-orcid":true,"given":"Decheng","family":"Zuo","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9215-7173","authenticated-orcid":true,"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2021.101199"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.13328\/j.cnki.jos.006140"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.21437\/odyssey.2020-19"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2019-1887"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2019-2249"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.21437\/odyssey.2016-41"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/asru46091.2019.9003845"},{"author":"Z. Z. Wu","key":"8","article-title":"A study on spoofing attack in state-of-the-art speaker verification: the telephone speech case"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2019-2212"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2019-2892"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2019-1698"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34872-4_44"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-020-00216-0"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2005.1416051"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2017.03.019"},{"key":"16","unstructured":"MesarosA.HeittolaT.VirtanenT.A multi-device dataset for urban acoustic scene classi\ufb01cation2018Tampere, FinlandTampere University of TechnologyTechnical report"},{"key":"17","unstructured":"WangX.JunichiY. S.MassimilianoT.ASVspoof 2019: a large-scale public database of synthesized, converted and replayed speech2020Ithaca, NY, USACornell UniversityTechnical Report"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.3819968"},{"article-title":"Audio tagging and deep architectures for acoustic scene classification: Uos submission for the DCASE 2020 challenge","year":"2020","author":"H. J. 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