{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:08:06Z","timestamp":1779253686268,"version":"3.51.4"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031453915","type":"print"},{"value":"9783031453922","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-45392-2_25","type":"book-chapter","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T20:17:29Z","timestamp":1697055449000},"page":"379-393","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Federated Learning and\u00a0Mel-Spectrograms for\u00a0Physical Violence Detection in\u00a0Audio"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3756-2766","authenticated-orcid":false,"given":"Victor E.","family":"de S. Silva","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1524-6604","authenticated-orcid":false,"given":"Tiago B.","family":"Lacerda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5767-7544","authenticated-orcid":false,"given":"P\u00e9ricles","family":"Miranda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9333-3212","authenticated-orcid":false,"given":"Andr\u00e9","family":"C\u00e2mara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3218-2960","authenticated-orcid":false,"given":"Amerson Riley Cabral","family":"Chagas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5439-5314","authenticated-orcid":false,"given":"Ana Paula C.","family":"Furtado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"25_CR1","unstructured":"Beutel, D.J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., Lane, N.D.: Flower: a friendly federated learning research framework. arXiv preprint arXiv:2007.14390 (2020)"},{"key":"25_CR2","unstructured":"Choi, K., Fazekas, G., Sandler, M.: Automatic tagging using deep convolutional neural networks (2016)"},{"key":"25_CR3","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-030-58356-9_11","volume-title":"Ambient Intelligence \u2013 Software and Applications","author":"D Dur\u00e3es","year":"2021","unstructured":"Dur\u00e3es, D., Marcondes, F.S., Gon\u00e7alves, F., Fonseca, J., Machado, J., Novais, P.: Detection violent behaviors: a survey. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds.) ISAmI 2020. AISC, vol. 1239, pp. 106\u2013116. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-58356-9_11"},{"key":"25_CR4","doi-asserted-by":"publisher","unstructured":"Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675\u2013701 (1937). https:\/\/doi.org\/10.1080\/01621459.1937.10503522. https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/01621459.1937.10503522","DOI":"10.1080\/01621459.1937.10503522"},{"issue":"4","key":"25_CR5","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193\u2013202 (1980). https:\/\/doi.org\/10.1007\/BF00344251","journal-title":"Biol. Cybern."},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNNLS.2021.3072238","volume":"33","author":"B Gu","year":"2021","unstructured":"Gu, B., Xu, A., Huo, Z., Deng, C., Huang, H.: Privacy-preserving asynchronous vertical federated learning algorithms for multiparty collaborative learning. IEEE Trans. Neural Netw. Learn. Syst. 33, 1\u201313 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2021.3072238","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Hard, A., et al.: Training keyword spotting models on non-iid data with federated learning (2020). https:\/\/arxiv.org\/abs\/2005.10406","DOI":"10.21437\/Interspeech.2020-3023"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). http:\/\/arxiv.org\/abs\/1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Hossain, M.S., Muhammad, G.: Emotion recognition using deep learning approach from audio\u2013visual emotional big data. Inf. Fusion 49, 69\u201378 (2019). https:\/\/doi.org\/10.1016\/j.inffus.2018.09.008, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253517307066","DOI":"10.1016\/j.inffus.2018.09.008"},{"key":"25_CR10","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/OJCS.2021.3099108","volume":"2","author":"R Hu","year":"2021","unstructured":"Hu, R., Guo, Y., Gong, Y.: Concentrated differentially private federated learning with performance analysis. IEEE Open J. Comput. Soc. 2, 276\u2013289 (2021). https:\/\/doi.org\/10.1109\/OJCS.2021.3099108","journal-title":"IEEE Open J. Comput. Soc."},{"key":"25_CR11","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1121\/1.1901999","volume":"8","author":"J Volkmann","year":"1937","unstructured":"Volkmann, J., Stevens, S.S., Newman, E.B.: A scale for the measurement of the psychological magnitude pitch. J. Acoust. Soc. Am. 8, 208 (1937). https:\/\/doi.org\/10.1121\/1.1901999","journal-title":"J. Acoust. Soc. Am."},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455\u20135516 (2020). https:\/\/doi.org\/10.1007\/s10462-020-09825-6. http:\/\/arxiv.org\/abs\/1901.06032","DOI":"10.1007\/s10462-020-09825-6"},{"key":"25_CR13","doi-asserted-by":"publisher","unstructured":"Kong, Q., Cao, Y., Iqbal, T., Wang, Y., Wang, W., Plumbley, M.D.: PANNs: large-scale pretrained audio neural networks for audio pattern recognition. IEEE\/ACM Trans. Audio Speech Lang. Process. 28, 2880\u20132894 (2020). https:\/\/doi.org\/10.1109\/TASLP.2020.3030497. https:\/\/ieeexplore.ieee.org\/document\/9229505\/","DOI":"10.1109\/TASLP.2020.3030497"},{"issue":"6","key":"25_CR14","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"key":"25_CR15","unstructured":"Lacerda, T. B., Miranda, P., Camara, A., Furtado, A.P.C.: Deep learning and mel-spectrograms for physical violence detection in audio. In: The 18th National Meeting on Artificial and Computational Intelligence, pp. 268\u2013279 (2021). https:\/\/sol.sbc.org.br\/index.php\/eniac\/article\/view\/18259\/18093"},{"key":"25_CR16","unstructured":"Lee, J., Park, J., Kim, K.L., Nam, J.: Sample-level deep convolutional neural networks for music auto-tagging using raw waveforms (2017)"},{"issue":"3","key":"25_CR17","doi-asserted-by":"publisher","first-page":"2134","DOI":"10.1109\/TII.2019.2942179","volume":"16","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Differentially private asynchronous federated learning for mobile edge computing in urban informatics. IEEE Trans. Ind. Inf. 16(3), 2134\u20132143 (2020). https:\/\/doi.org\/10.1109\/TII.2019.2942179","journal-title":"IEEE Trans. Ind. Inf."},{"key":"25_CR18","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.y.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 54, pp. 1273\u20131282. PMLR (2017). https:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"key":"25_CR19","unstructured":"Nations, U.: Policy brief: the impact of covid-19 on women (2020). https:\/\/www.un.org\/sexualviolenceinconflict\/wp-content\/uploads\/2020\/06\/report\/policy-brief-the-impact-of-covid-19-on-women\/policy-brief-the-impact-of-covid-19-on-women-en-1.pdf"},{"key":"25_CR20","doi-asserted-by":"publisher","unstructured":"Nayyar, R.K., Nair, S., Patil, O., Pawar, R., Lolage, A.: Content-based auto-tagging of audios using deep learning. In: 2017 International Conference on Big Data, IoT and Data Science (BID), pp. 30\u201336 (2017). https:\/\/doi.org\/10.1109\/BID.2017.8336569","DOI":"10.1109\/BID.2017.8336569"},{"key":"25_CR21","unstructured":"Organization, W.H.: Violence against women (2021). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/violence-against-women"},{"key":"25_CR22","unstructured":"Organization, W.H.: Violence against women prevalence estimates, 2018: global, regional and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women (2021). https:\/\/www.who.int\/publications\/i\/item\/9789240022256"},{"key":"25_CR23","doi-asserted-by":"publisher","unstructured":"Paul, S., Sengupta, P., Mishra, S.: Flaps: Federated learning and privately scaling. In: 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 13\u201319 (2020). https:\/\/doi.org\/10.1109\/MASS50613.2020.00011","DOI":"10.1109\/MASS50613.2020.00011"},{"issue":"2","key":"25_CR24","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/jstsp.2019.2908700","volume":"13","author":"H Purwins","year":"2019","unstructured":"Purwins, H., Li, B., Virtanen, T., Schluter, J., Chang, S.Y., Sainath, T.: Deep learning for audio signal processing. IEEE J. Sel. Topics Signal Process. 13(2), 206\u2013219 (2019). https:\/\/doi.org\/10.1109\/jstsp.2019.2908700","journal-title":"IEEE J. Sel. Topics Signal Process."},{"key":"25_CR25","doi-asserted-by":"publisher","first-page":"107560","DOI":"10.1109\/ACCESS.2019.2932114","volume":"7","author":"M Ramzan","year":"2019","unstructured":"Ramzan, M., et al.: A review on state-of-the-art violence detection techniques. IEEE Access 7, 107560\u2013107575 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2932114","journal-title":"IEEE Access"},{"key":"25_CR26","doi-asserted-by":"publisher","unstructured":"Rouas, J.L., Louradour, J., Ambellouis, S.: Audio events detection in public transport vehicle. In: 2006 IEEE Intelligent Transportation Systems Conference, pp. 733\u2013738. IEEE (2006). https:\/\/doi.org\/10.1109\/ITSC.2006.1706829. http:\/\/ieeexplore.ieee.org\/document\/1706829\/","DOI":"10.1109\/ITSC.2006.1706829"},{"key":"25_CR27","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520. IEEE (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00474. https:\/\/ieeexplore.ieee.org\/document\/8578572\/","DOI":"10.1109\/CVPR.2018.00474"},{"key":"25_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-030-91608-4_43","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2021","author":"F Santos","year":"2021","unstructured":"Santos, F.: In-car violence detection based on the audio signal. In: Yin, H., et al. (eds.) IDEAL 2021. LNCS, vol. 13113, pp. 437\u2013445. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91608-4_43"},{"key":"25_CR29","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015). https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"25_CR30","doi-asserted-by":"publisher","unstructured":"Souto, H., Mello, R., Furtado, A.: An acoustic scene classification approach involving domestic violence using machine learning. In: Anais do ENIAC, pp. 705\u2013716 (2019). https:\/\/doi.org\/10.5753\/eniac.2019.9327. https:\/\/sol.sbc.org.br\/index.php\/eniac\/article\/view\/9327","DOI":"10.5753\/eniac.2019.9327"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions (2014)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"25_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision (2015). http:\/\/arxiv.org\/abs\/1512.00567","DOI":"10.1109\/CVPR.2016.308"},{"key":"25_CR33","doi-asserted-by":"publisher","unstructured":"Triastcyn, A., Faltings, B.: Federated learning with bayesian differential privacy. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 2587\u20132596 (2019). https:\/\/doi.org\/10.1109\/BigData47090.2019.9005465","DOI":"10.1109\/BigData47090.2019.9005465"},{"key":"25_CR34","doi-asserted-by":"publisher","unstructured":"Tripathi, G., Singh, K.V.D.K.: Violence recognition using convolutional neural network: a survey. J. Intell. Fuzzy Syst. 39, 7931\u20137952 (2020). https:\/\/doi.org\/10.3233\/JIFS-201400. https:\/\/content.iospress.com\/articles\/journal-of-intelligent-and-fuzzy-systems\/ifs201400","DOI":"10.3233\/JIFS-201400"},{"key":"25_CR35","doi-asserted-by":"crossref","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. Biometr. Bull. 1(6), 80\u201383 (1945). http:\/\/www.jstor.org\/stable\/3001968","DOI":"10.2307\/3001968"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45392-2_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:51:40Z","timestamp":1710348700000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45392-2_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453915","9783031453922"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45392-2_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belo Horizonte","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bracis.dcc.ufmg.br","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"242","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"90","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}