{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:02:10Z","timestamp":1772323330110,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981779","type":"print"},{"value":"9789819981786","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8178-6_4","type":"book-chapter","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T10:02:54Z","timestamp":1701252174000},"page":"48-60","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MDAM: Multi-Dimensional Attention Module for Anomalous Sound Detection"],"prefix":"10.1007","author":[{"given":"Shengbing","family":"Chen","sequence":"first","affiliation":[]},{"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiajun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhiqi","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/342009.335388"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"4_CR3","unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)"},{"key":"4_CR4","unstructured":"Dohi, K., et al.: Description and discussion on DCASE 2022 challenge task 2: unsupervised anomalous sound detection for machine condition monitoring applying domain generalization techniques. arXiv preprint arXiv:2206.05876 (2022)"},{"key":"4_CR5","unstructured":"Dohi, K., et al.: MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task. arXiv preprint arXiv:2205.13879 (2022)"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Harada, N., Niizumi, D., Ohishi, Y., Takeuchi, D., Yasuda, M.: First-shot anomaly sound detection for machine condition monitoring: a domain generalization baseline. arXiv preprint arXiv:2303.00455 (2023)","DOI":"10.23919\/EUSIPCO58844.2023.10289721"},{"key":"4_CR7","unstructured":"Harada, N., Niizumi, D., Takeuchi, D., Ohishi, Y., Yasuda, M., Saito, S.: Toyadmos2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions. arXiv preprint arXiv:2106.02369 (2021)"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4_CR9","unstructured":"Inoue, T., et al.: Detection of anomalous sounds for machine condition monitoring using classification confidence. In: DCASE, pp. 66\u201370 (2020)"},{"key":"4_CR10","unstructured":"Jiang, A., et al.: Thuee system for first-shot unsupervised anomalous sound detection for machine condition monitoring. Technical report, DCASE2023 Challenge (2023)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, A., Zhang, W.Q., Deng, Y., Fan, P., Liu, J.: Unsupervised anomaly detection and localization of machine audio: a GAN-based approach. In: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023, pp. 1\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10096813"},{"key":"4_CR12","unstructured":"Jie, J.: Anomalous sound detection based on self-supervised learning. Technical report, DCASE2023 Challenge (2023)"},{"key":"4_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"4_CR14","unstructured":"Kuroyanagi, I., Hayashi, T., Takeda, K., Toda, T.: Two-stage anomalous sound detection systems using domain generalization and specialization techniques. Technical report, DCASE2022 Challenge, Technical report (2022)"},{"key":"4_CR15","unstructured":"Lv, Z., Han, B., Chen, Z., Qian, Y., Ding, J., Liu, J.: Unsupervised anomalous detection based on unsupervised pretrained models. Technical report, DCASE2023 Challenge (2023)"},{"key":"4_CR16","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using T-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"issue":"1","key":"4_CR17","doi-asserted-by":"publisher","first-page":"21552","DOI":"10.1038\/s41598-021-01045-4","volume":"11","author":"W Mu","year":"2021","unstructured":"Mu, W., Yin, B., Huang, X., Xu, J., Du, Z.: Environmental sound classification using temporal-frequency attention based convolutional neural network. Sci. Rep. 11(1), 21552 (2021)","journal-title":"Sci. Rep."},{"key":"4_CR18","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Reynolds, D.A., et al.: Gaussian mixture models. Encycl. Biometrics 741(659\u2013663) (2009)","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"issue":"23","key":"4_CR21","doi-asserted-by":"publisher","first-page":"11128","DOI":"10.3390\/app112311128","volume":"11","author":"Y Wang","year":"2021","unstructured":"Wang, Y., et al.: Unsupervised anomalous sound detection for machine condition monitoring using classification-based methods. Appl. Sci. 11(23), 11128 (2021)","journal-title":"Appl. Sci."},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Wilkinghoff, K.: Sub-cluster AdaCos: learning representations for anomalous sound detection. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9534290"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Wilkinghoff, K.: Design choices for learning embeddings from auxiliary tasks for domain generalization in anomalous sound detection. In: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023, pp. 1\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10097176"},{"key":"4_CR24","unstructured":"Wilkinghoff, K.: Fraunhofer FKIE submission for task 2: first-shot unsupervised anomalous sound detection for machine condition monitoring. Technical report, DCASE2023 Challenge (2023)"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4_CR26","unstructured":"Xiao, F., Liu, Y., Wei, Y., Guan, J., Zhu, Q., Zheng, T., Han, J.: The dcase2022 challenge task 2 system: Anomalous sound detection with self-supervised attribute classification and GMM-based clustering. Challenge Technical report (2022)"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8178-6_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:27:19Z","timestamp":1709828839000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8178-6_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9789819981779","9789819981786"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8178-6_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]},"assertion":[{"value":"30 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}