{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T20:57:23Z","timestamp":1774990643630,"version":"3.50.1"},"reference-count":20,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2025,10,1]]},"DOI":"10.1587\/transinf.2024edl8085","type":"journal-article","created":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T18:06:47Z","timestamp":1744567607000},"page":"1250-1254","source":"Crossref","is-referenced-by-count":2,"title":["TEFFDConv: An Improved Approach to Enhance Temporal Localization in Sound Event Detection"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Xichang","family":"CAI","sequence":"first","affiliation":[{"name":"Department of Electronic and Communication Engineering, North China University of Technology"}]},{"given":"Jingxuan","family":"CHEN","sequence":"additional","affiliation":[{"name":"Department of Electronic and Communication Engineering, North China University of Technology"}]},{"given":"Ziyi","family":"LIU","sequence":"additional","affiliation":[{"name":"Department of Electronic and Communication Engineering, North China University of Technology"}]},{"given":"Menglong","family":"WU","sequence":"additional","affiliation":[{"name":"Department of Electronic and Communication Engineering, North China University of Technology"}]},{"given":"HongYang","family":"GUO","sequence":"additional","affiliation":[{"name":"Beijing TimeTuring Technology Company"}]},{"given":"Xuejing","family":"SUN","sequence":"additional","affiliation":[{"name":"Beijing TimeTuring Technology Company"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] M. Neri, F. Battisti, A. Neri, and M. Carli, \u201cSound event detection for human safety and security in noisy environments,\u201d IEEE Access, vol.10, pp.134230-134240, 2022. doi: 10.1109\/ACCESS.2022.3231681 10.1109\/access.2022.3231681","DOI":"10.1109\/ACCESS.2022.3231681"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] M.K. Nandwana and T. Hasan, \u201cTowards Smart-Cars That Can Listen: Abnormal Acoustic Event Detection on the Road,\u201d INTERSPEECH, pp.2968-2971, 2016. 10.21437\/interspeech.2016-1366","DOI":"10.21437\/Interspeech.2016-1366"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] A. Vafeiadis, K. Votis, D. Giakoumis, D. Tzovaras, L. Chen, and R. Hamzaoui, \u201cAudio content analysis for unobtrusive event detection in smart homes,\u201d Engineering Applications of Artificial Intelligence, vol.89, p.103226, 2020. doi: 10.1016\/j.engappai.2019.08.020 10.1016\/j.engappai.2019.08.020","DOI":"10.1016\/j.engappai.2019.08.020"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] E. Cak\u0131r, G. Parascandolo, T. Heittola, H. Huttunen, and T. Virtanen,\u201cConvolutional recurrent neural networks for polyphonic sound event detection,\u201d IEEE\/ACM Trans. Audio, Speech, Language Process., vol.25, no. 6, pp.1291-1303, 2017. doi: 10.1109\/TASLP.2017.2690575 10.1109\/taslp.2017.2690575","DOI":"10.1109\/TASLP.2017.2690575"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] H. Nam, S.-H. Kim, B.-Y. Ko, and Y.-H. Park, \u201cFrequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection,\u201d INTERSPEECH 2022, ISCA, pp.2763-2767, 2022. 10.21437\/interspeech.2022-10127","DOI":"10.21437\/Interspeech.2022-10127"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] S. Xiao, X. Zhang, and P. Zhang, \u201cMulti-dimensional frequency dynamic convolution with confident mean teacher for sound event detection,\u201d ICASSP, IEEE, pp.1-5, 2023. 10.1109\/icassp49357.2023.10096306","DOI":"10.1109\/ICASSP49357.2023.10096306"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. Zhou, V. Jampani, Z. Pi, Q. Liu, and M.-H. Yang, \u201cDecoupled dynamic filter networks,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.6647-6656, 2021. 10.1109\/cvpr46437.2021.00658","DOI":"10.1109\/CVPR46437.2021.00658"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] H. Yue, Z. Zhang, D. Mu, Y. Dang, J. Yin, and J. Tang, \u201cFull-frequency dynamic convolution: a physical frequency-dependent convolution for sound event detection,\u201d International Conference on Pattern Recognition, Springer, pp.260-272, 2025. 10.1007\/978-3-031-78498-9_18","DOI":"10.1007\/978-3-031-78498-9_18"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] Y.-H. Shen, K.-X. He, and W.-Q. Zhang, \u201cLearning How to Listen: A Temporal-Frequential Attention Model for Sound Event Detection,\u201d Interspeech 2019, 2019. doi: 10.21437\/Interspeech.2019-2045 10.21437\/interspeech.2019-2045","DOI":"10.21437\/Interspeech.2019-2045"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] Y. Zhang, Y. Liang, S. Weng, H. Lin, L. Chen, and S. Zheng, \u201cHierarchical Temporal Attention and Competent Teacher Network for Sound Event Detection,\u201d ICME, IEEE, pp.1-6, 2024. 10.1109\/icme57554.2024.10688275","DOI":"10.1109\/ICME57554.2024.10688275"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Q. Hou, D. Zhou, and J. Feng, \u201cCoordinate attention for efficient mobile network design,\u201d Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp.13713-13722, 2021. 10.1109\/cvpr46437.2021.01350","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] J. Hu, L. Shen, and G. Sun, \u201cSqueeze-and-excitation networks,\u201d Proceedings of the IEEE conference on computer vision and pattern recognition, pp.7132-7141, 2018. 10.1109\/cvpr.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] R. Serizel, N. Turpault, A. Shah, and J. Salamon, \u201cSound event detection in synthetic domestic environments,\u201d ICASSP, IEEE, pp.86-90, 2020. doi: 10.1109\/ICASSP40776.2020.9054478 10.1109\/icassp40776.2020.9054478","DOI":"10.1109\/ICASSP40776.2020.9054478"},{"key":"14","unstructured":"[14] A. Tarvainen and H. Valpola, \u201cMean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,\u201d Advances in Neural Information Processing Systems, vol.30, 2017."},{"key":"15","unstructured":"[15] H. Zhang, M. Cisse, Y.N. Dauphin, and D. Lopez-Paz, \u201cmixup: Beyond empirical risk minimization,\u201d ICLR 2018, 2018."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] H. Nam, S.-H. Kim, and Y.-H. Park, \u201cFilteraugment: An acoustic environmental data augmentation method,\u201d ICASSP, IEEE, pp.4308-4312, 2022. doi: 10.1109\/ICASSP43922.2022.9747680 10.1109\/icassp43922.2022.9747680","DOI":"10.1109\/ICASSP43922.2022.9747680"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] D.S. Park, W. Chan, Y. Zhang, C.-C. Chiu, B. Zoph, E.D. Cubuk, and Q.V. Le, \u201cSpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition,\u201d Interspeech 2019, 2019. doi: 10.21437\/Interspeech.2019-2680 10.21437\/interspeech.2019-2680","DOI":"10.21437\/Interspeech.2019-2680"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] G. Ferroni, N. Turpault, J. Azcarreta, F. Tuveri, R. Serizel, C. Bilen, and S. Krstulovic, \u201cImproving sound event detection metrics: insights from dcase 2020,\u201d ICASSP, IEEE, pp.631-635, 2021. doi: 10.1109\/ICASSP39728.2021.9414711 10.1109\/icassp39728.2021.9414711","DOI":"10.1109\/ICASSP39728.2021.9414711"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] J. Ebbers, R. Haeb-Umbach, and R. Serizel, \u201cThreshold independent evaluation of sound event detection scores,\u201d ICASSP, IEEE, pp.1021-1025, 2022. doi: 10.1109\/ICASSP43922.2022.9747556 10.1109\/icassp43922.2022.9747556","DOI":"10.1109\/ICASSP43922.2022.9747556"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] S.-H. Kim, H. Nam, and Y.-H. Park, \u201cTemporal dynamic convolutional neural network for text-independent speaker verification and phonemic analysis,\u201d ICASSP, IEEE, pp.6742-6746, 2022. doi: 10.1109\/ICASSP43922.2022.9747421 10.1109\/icassp43922.2022.9747421","DOI":"10.1109\/ICASSP43922.2022.9747421"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/10\/E108.D_2024EDL8085\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T03:27:28Z","timestamp":1759548448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/10\/E108.D_2024EDL8085\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"references-count":20,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024edl8085","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"article-number":"2024EDL8085"}}