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Smaragdis, \u201cAudio analysis for surveillance applications,\u201d Proc. 2005 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp.158-161, 2005. 10.1109\/aspaa.2005.1540194"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] S. Ntalampiras, I. Potamitis, and N. Fakotakis, \u201cOn acoustic surveillance of hazardous situations,\u201d Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.165-168, 2009. 10.1109\/icassp.2009.4959546","DOI":"10.1109\/ICASSP.2009.4959546"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] S. Chandrakala and S.L. Jayalakshmi, \u201cEnvironmental audio scene and sound event recognition for autonomous surveillance: A survey and comparative studies,\u201d ACM Computing Surveys (CSUR), vol.52, no.3, Article No.63, 2019. 10.1145\/3322240","DOI":"10.1145\/3322240"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] Y. Koizumi, S. Saito, H. Uematsu, Y. Kawachi, and N. Harada, \u201cUnsupervised detection of anomalous sound based on deep learning and the Neyman-Pearson lemma,\u201d IEEE\/ACM Trans. Audio Speech Lang. Process., vol.27, no.1, pp.212-224, 2019. 10.1109\/taslp.2018.2877258","DOI":"10.1109\/TASLP.2018.2877258"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Y. Kawaguchi, R. Tanabe, T. Endo, K. Ichige, and K. Hamada, \u201cAnomaly detection based on an ensemble of dereverberation and anomalous sound extraction,\u201d Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.865-869, 2019. 10.1109\/icassp.2019.8683702","DOI":"10.1109\/ICASSP.2019.8683702"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] Q. Jin, P.F. Schulam, S. Rawat, S. Burger, D. Ding, and F. Metze, \u201cEvent-based video retrieval using audio,\u201d Proc. INTERSPEECH, 2012.","DOI":"10.21437\/Interspeech.2012-556"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] A. Dessein, A. Cont, and G. Lemaitre, \u201cReal-time detection of overlapping sound events with non-negative matrix factorization,\u201d Matrix Information Geometry, pp.341-371, Springer, 2013. 10.1007\/978-3-642-30232-9_14","DOI":"10.1007\/978-3-642-30232-9_14"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] T. Komatsu, T. Toizumi, R. Kondo, and Y. Senda, \u201cAcoustic event detection method using semi-supervised non-negative matrix factorization with mixtures of local dictionaries,\u201d Proc. Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), pp.45-49, 2016.","DOI":"10.1109\/ICASSP.2016.7472079"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] S. Hershey, S. Chaudhuri, D.P.W. Ellis, J.F. Gemmeke, A. Jansen, R.C. Moore, M. Plakal, D. Platt, R.A. Saurous, B. Seybold, M. Slaney, R.J. Weiss, and K. Wilson, \u201cCNN architectures for large-scale audio classification,\u201d Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.131-135, 2017. 10.1109\/icassp.2017.7952132","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"13","unstructured":"[13] I.Y. Jeong, S. Lee, Y. Han, and K. Lee, \u201cAudio event detection using multiple-input convolutional neural network,\u201d Proc. Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), pp.51-54, 2017."},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] E. \u00c7akir, G. Parascandolo, T. Heittola, H. Huttunen, and T. Virtanen, \u201cConvolutional recurrent neural networks for polyphonic sound event detection,\u201d IEEE\/ACM Trans. Audio Speech Lang. Process., vol.25, no.6, pp.1291-1303, 2017. 10.1109\/taslp.2017.2690575","DOI":"10.1109\/TASLP.2017.2690575"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] T. Hayashi, S. Watanabe, T. Toda, T. Hori, J. Le Roux, and K. Takeda, \u201cDuration-controlled LSTM for polyphonic sound event detection,\u201d IEEE\/ACM Trans. Audio Speech Lang. Process., vol.25, no.11, pp.2059-2070, 2017. 10.1109\/taslp.2017.2740002","DOI":"10.1109\/TASLP.2017.2740002"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] S. Kothinti, K. Imoto, D. Chakrabarty, G. Sell, S. Watanabe, and M. Elhilali, \u201cJoint acoustic and class inference for weakly supervised sound event detection,\u201d Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), pp.36-40, 2019. 10.1109\/icassp.2019.8682772","DOI":"10.1109\/ICASSP.2019.8682772"},{"key":"17","unstructured":"[17] A. Mesaros, T. Heittola, and A. Klapuri, \u201cLatent semantic analysis in sound event detection,\u201d Proc. European Signal Processing Conference (EUSIPCO), pp.1307-1311, 2011."},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] K. Imoto and N. 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Schwenk, and Y. Bengio, \u201cLearning phrase representations using RNN encoder-decoder for statistical machine translation,\u201d Proc. 2014 Conf. Empir. Methods Nat. Lang. Process. (EMNLP), pp.1724-1734, 2014. 10.3115\/v1\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] P.J. Werbos, \u201cBackpropagation through time: What it does and how to do it,\u201d Proc. IEEE, vol.78, no.10, pp.1550-1560, 1990. 10.1109\/5.58337","DOI":"10.1109\/5.58337"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] D.I. Shuman, S.K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, \u201cThe emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,\u201d IEEE Signal Process. Mag., vol.30, no.3, pp.83-98, 2013. 10.1109\/msp.2012.2235192","DOI":"10.1109\/MSP.2012.2235192"},{"key":"24","unstructured":"[24] D.P. Kingma and J.L. 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