{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T15:58:58Z","timestamp":1775663938416,"version":"3.50.1"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,6]]},"DOI":"10.1109\/icassp39728.2021.9414662","type":"proceedings-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T19:53:45Z","timestamp":1620935625000},"page":"336-340","source":"Crossref","is-referenced-by-count":71,"title":["Flow-Based Self-Supervised Density Estimation for Anomalous Sound Detection"],"prefix":"10.1109","author":[{"given":"Kota","family":"Dohi","sequence":"first","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd.,Tokyo,Japan,185-8601"}]},{"given":"Takashi","family":"Endo","sequence":"additional","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd.,Tokyo,Japan,185-8601"}]},{"given":"Harsh","family":"Purohit","sequence":"additional","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd.,Tokyo,Japan,185-8601"}]},{"given":"Ryo","family":"Tanabe","sequence":"additional","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd.,Tokyo,Japan,185-8601"}]},{"given":"Yohei","family":"Kawaguchi","sequence":"additional","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd.,Tokyo,Japan,185-8601"}]}],"member":"263","reference":[{"key":"ref10","first-page":"2338","article-title":"Masked autoregressive flow for density estimation","author":"papamakarios","year":"2017","journal-title":"NeurIPS"},{"key":"ref11","article-title":"Description and discussion on DCASE2020 Challenge task2: Unsupervised anomalous sound detection for machine condition monitoring","author":"koizumi","year":"2020","journal-title":"DCAS Workshop"},{"key":"ref12","first-page":"14707","article-title":"Likelihood ratios for out-of-distribution detection","author":"ren","year":"2019","journal-title":"NeurIPS"},{"key":"ref13","article-title":"Deep anomaly detection with outlier exposure","author":"hendrycks","year":"2019","journal-title":"ICLRE"},{"key":"ref14","article-title":"Why normalizing flows fail to detect out-of-distribution data","author":"kirichenko","year":"2020","journal-title":"ICML workshop on Invertible Neural Networks and Normalizing Flows"},{"key":"ref15","article-title":"Reframing unsupervised machine condition monitoring as a supervised classification task with outlier-exposed classifiers","author":"primus","year":"2020"},{"key":"ref16","article-title":"Normalizing flows for novelty detection in industrial time series data","author":"schmidt","year":"2019","journal-title":"ICML workshop on Invertible Neural Networks and Normalizing Flows"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683072"},{"key":"ref18","article-title":"Normalizing flows for deep anomaly detection","author":"ryzhikov","year":"2019"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206939"},{"key":"ref4","article-title":"NICE: Non-linear independent components estimation","author":"dinh","year":"2015"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.21423"},{"key":"ref6","article-title":"WAIC, but why? generative ensembles for robust anomaly detection","author":"choi","year":"2019"},{"key":"ref5","article-title":"Do deep generative models know what they don&#x2019;t know?","author":"nalisnick","year":"2019","journal-title":"ICLRE"},{"key":"ref8","article-title":"Unsupervised anomalous sound detection using self-supervised classification and group masked autoencoder for density estimation","author":"giri","year":"2020"},{"key":"ref7","article-title":"Input complexity and out-of-distribution detection with likelihood-based generative models","author":"serr\u00e0","year":"2020","journal-title":"ICLRE"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2017.8081297"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054344"},{"key":"ref9","first-page":"10215","article-title":"Glow: Generative flow with invertible 1x1 convolutions","author":"kingma","year":"2018","journal-title":"NeurIPS"},{"key":"ref20","article-title":"Anomalous sound detection with masked autoregressive flows and machine type dependent postprocessing","author":"haunschmid","year":"2020"},{"key":"ref22","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"ICLRE"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref23","article-title":"Understanding and mitigating exploding inverses in invertible neural networks","author":"behrmann","year":"2020"}],"event":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Toronto, ON, Canada","start":{"date-parts":[[2021,6,6]]},"end":{"date-parts":[[2021,6,11]]}},"container-title":["ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9413349\/9413350\/09414662.pdf?arnumber=9414662","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:19:09Z","timestamp":1659485949000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9414662\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,6]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/icassp39728.2021.9414662","relation":{},"subject":[],"published":{"date-parts":[[2021,6,6]]}}}