{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:01:37Z","timestamp":1783008097706,"version":"3.54.5"},"publisher-location":"Cham","reference-count":65,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031163012","type":"print"},{"value":"9783031163029","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16302-9_5","type":"book-chapter","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T23:03:52Z","timestamp":1665011032000},"page":"64-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning in\u00a0Audio Classification"],"prefix":"10.1007","author":[{"given":"Yaqin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Wei-Kocsis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John A.","family":"Springer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric T.","family":"Matson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,6]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Latif, S., Cuay\u00e1huitl, H., Pervez, F., Shamshad, F., Ali, H.S., Cambria, E.: A survey on deep reinforcement learning for audio-based applications. arXiv preprint arXiv:2101.00240 (2021)","DOI":"10.1007\/s10462-022-10224-2"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Sharma, G., Umapathy, K., Krishnan, S.: Trends in audio signal feature extraction methods. Appl. Acoust. 158, 107020 (2020)","DOI":"10.1016\/j.apacoust.2019.107020"},{"issue":"1","key":"5_CR3","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10462-018-09679-z","volume":"52","author":"G Nguyen","year":"2019","unstructured":"Nguyen, G., et al.: Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif. Intell. Rev. 52(1), 77\u2013124 (2019)","journal-title":"Artif. Intell. Rev."},{"issue":"5\u20136","key":"5_CR4","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1080\/713827180","volume":"17","author":"S Zhang","year":"2003","unstructured":"Zhang, S., Zhang, C., Yang, Q.: Data preparation for data mining. Appl. Artif. Intell. 17(5\u20136), 375\u2013381 (2003)","journal-title":"Appl. Artif. Intell."},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Ying, X.: An overview of overfitting and its solutions. In: Journal of Physics: Conference Series, vol. 1168, no. 2, p. 022022. IOP Publishing (2019)","DOI":"10.1088\/1742-6596\/1168\/2\/022022"},{"key":"5_CR6","unstructured":"Bishop, C.M., Nasrabadi, N.M.: Pattern Recognition and Machine Learning, vol. 4, no. 4. Springer, Cham (2006)"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 161\u2013168 (2006)","DOI":"10.1145\/1143844.1143865"},{"key":"5_CR8","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/978-0-387-84858-7_14","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: Unsupervised learning. In: Hastie, T., Tibshirani, R., Friedman, J. (eds.) The Elements of Statistical Learning. SSS, pp. 485\u2013585. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-84858-7_14"},{"issue":"3","key":"5_CR9","first-page":"729","volume":"12","author":"MA Wiering","year":"2012","unstructured":"Wiering, M.A., Van Otterlo, M.: Reinforcement learning. Adapt. Learn. Optim. 12(3), 729 (2012)","journal-title":"Adapt. Learn. Optim."},{"key":"5_CR10","unstructured":"Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016)"},{"key":"5_CR11","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"issue":"8","key":"5_CR12","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","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","journal-title":"Artif. Intell. Rev."},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"issue":"10","key":"5_CR14","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TASLP.2014.2339736","volume":"22","author":"O Abdel-Hamid","year":"2014","unstructured":"Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE\/ACM Trans. Audio Speech Lang. Process. 22(10), 1533\u20131545 (2014)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Dong, M.: Convolutional neural network achieves human-level accuracy in music genre classification. arXiv preprint arXiv:1802.09697 (2018)","DOI":"10.32470\/CCN.2018.1153-0"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Park, S.R., Lee, J.: A fully convolutional neural network for speech enhancement. arXiv preprint arXiv:1609.07132 (2016)","DOI":"10.21437\/Interspeech.2017-1465"},{"key":"5_CR17","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.apacoust.2018.12.019","volume":"148","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Guo, Q., Liang, X., Wang, J., Qian, Y.: Environmental sound classification with dilated convolutions. Appl. Acoust. 148, 123\u2013132 (2019)","journal-title":"Appl. Acoust."},{"key":"5_CR18","unstructured":"Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1109\/RBME.2020.3006860","volume":"14","author":"S Latif","year":"2020","unstructured":"Latif, S., Qadir, J., Qayyum, A., Usama, M., Younis, S.: Speech technology for healthcare: opportunities, challenges, and state of the art. IEEE Rev. Biomed. Eng. 14, 342\u2013356 (2020)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404, 132306 (2020)","DOI":"10.1016\/j.physd.2019.132306"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Li, B.: Modeling time-frequency patterns with LSTM vs. convolutional architectures for LVCSR tasks (2016)","DOI":"10.21437\/Interspeech.2016-84"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Li, J., Mohamed, A., Zweig, G., Gong, Y.: LSTM time and frequency recurrence for automatic speech recognition. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 187\u2013191. IEEE (2015)","DOI":"10.1109\/ASRU.2015.7404793"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Ghosal, D., Kolekar, M.H.: Music genre recognition using deep neural networks and transfer learning. In: Interspeech, pp. 2087\u20132091 (2018)","DOI":"10.21437\/Interspeech.2018-2045"},{"issue":"12","key":"5_CR25","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1109\/TASLP.2016.2602884","volume":"24","author":"Y Qian","year":"2016","unstructured":"Qian, Y., Bi, M., Tan, T., Yu, K.: Very deep convolutional neural networks for noise robust speech recognition. IEEE\/ACM Trans. Audio Speech Lang. Process. 24(12), 2263\u20132276 (2016)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Sun, T.-W.: End-to-end speech emotion recognition with gender information. IEEE Access 8, 152 423\u2013152 438 (2020)","DOI":"10.1109\/ACCESS.2020.3017462"},{"key":"5_CR27","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"issue":"11","key":"5_CR28","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"IEEE Trans. Signal Process."},{"key":"5_CR29","unstructured":"Raffel, C., Luong, M.-T., Liu, P.J., Weiss, R.J., Eck, D.: Online and linear-time attention by enforcing monotonic alignments. In: International Conference on Machine Learning, pp. 2837\u20132846. PMLR (2017)"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Graves, A.: Sequence transduction with recurrent neural networks. arXiv preprint arXiv:1211.3711 (2012)","DOI":"10.1007\/978-3-642-24797-2"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Pham, N.-Q., Nguyen, T.-S., Niehues, J., M\u00fcller, M., St\u00fcker, S., Waibel, A.: Very deep self-attention networks for end-to-end speech recognition. arXiv preprint arXiv:1904.13377 (2019)","DOI":"10.21437\/Interspeech.2019-2702"},{"key":"5_CR32","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"issue":"3","key":"5_CR33","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1109\/TASL.2012.2227740","volume":"21","author":"M Shannon","year":"2012","unstructured":"Shannon, M., Zen, H., Byrne, W.: Autoregressive models for statistical parametric speech synthesis. IEEE Trans. Audio Speech Lang. Process. 21(3), 587\u2013597 (2012)","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"5_CR34","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"5_CR35","unstructured":"Sutton, R.S., Barto, A.G., et al.: Introduction to reinforcement learning (1998)"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Fran\u00e7ois-Lavet, V., Henderson, P., Islam, R., Bellemare, M.G., Pineau, J.: An introduction to deep reinforcement learning. arXiv preprint arXiv:1811.12560 (2018)","DOI":"10.1561\/9781680835397"},{"key":"5_CR37","unstructured":"Kaiser, L., et al.: Model-based reinforcement learning for Atari. arXiv preprint arXiv:1903.00374 (2019)"},{"key":"5_CR38","unstructured":"Whiteson, S.: TreeQN and ATeeC: differentiable tree planning for deep reinforcement learning (2018)"},{"key":"5_CR39","doi-asserted-by":"crossref","unstructured":"Kala, T., Shinozaki, T.: Reinforcement learning of speech recognition system based on policy gradient and hypothesis selection. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5759\u20135763. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8462656"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Tjandra, A., Sakti, S., Nakamura, S.: Sequence-to-sequence ASR optimization via reinforcement learning. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5829\u20135833. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8461705"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Chung, H., Jeon, H.-B., Park, J.G.: Semi-supervised training for sequence-to-sequence speech recognition using reinforcement learning. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207023"},{"key":"5_CR42","unstructured":"Fakoor, R., He, X., Tashev, I., Zarar, S.: Reinforcement learning to adapt speech enhancement to instantaneous input signal quality. arXiv preprint arXiv:1711.10791 (2017)"},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Alamdari, N., Lobarinas, E., Kehtarnavaz, N.: Personalization of hearing aid compression by human-in-the-loop deep reinforcement learning. IEEE Access 8, 203 503\u2013203 515 (2020)","DOI":"10.1109\/ACCESS.2020.3035728"},{"key":"5_CR44","unstructured":"Kotecha, N.: Bach2Bach: generating music using a deep reinforcement learning approach. arXiv preprint arXiv:1812.01060 (2018)"},{"key":"5_CR45","unstructured":"Jaques, N., Gu, S., Turner, R.E., Eck, D.: Generating music by fine-tuning recurrent neural networks with reinforcement learning (2016)"},{"key":"5_CR46","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.ecoinf.2019.05.007","volume":"52","author":"J Xie","year":"2019","unstructured":"Xie, J., Zhu, M.: Handcrafted features and late fusion with deep learning for bird sound classification. Eco. Inform. 52, 74\u201381 (2019)","journal-title":"Eco. Inform."},{"issue":"3","key":"5_CR47","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","volume":"24","author":"J Salamon","year":"2017","unstructured":"Salamon, J., Bello, J.P.: Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process. Lett. 24(3), 279\u2013283 (2017)","journal-title":"IEEE Signal Process. Lett."},{"issue":"1","key":"5_CR48","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MSP.2018.2874383","volume":"36","author":"J Nam","year":"2018","unstructured":"Nam, J., Choi, K., Lee, J., Chou, S.-Y., Yang, Y.-H.: Deep learning for audio-based music classification and tagging: teaching computers to distinguish rock from bach. IEEE Signal Process. Mag. 36(1), 41\u201351 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"5_CR49","unstructured":"Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Advances in Neural Information Processing Systems, vol. 12 (1999)"},{"key":"5_CR50","unstructured":"Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928\u20131937. PMLR (2016)"},{"issue":"7540","key":"5_CR51","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"5_CR52","unstructured":"Seno, T.: Welcome to deep reinforcement learning part 1: DQN (2017). https:\/\/towardsdatascience.com\/welcome-to-deep-reinforcement-learning-part-1-dqn-c3cab4d41b6b"},{"key":"5_CR53","doi-asserted-by":"crossref","unstructured":"Van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1 (2016)","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"5_CR54","unstructured":"Schaul, T., Quan, J., Antonoglou, I., Silver, D.: Prioritized experience replay. arXiv preprint arXiv:1511.05952 (2015)"},{"key":"5_CR55","doi-asserted-by":"crossref","unstructured":"Abe\u00dfer, J.: A review of deep learning based methods for acoustic scene classification. Appl. Sci. 10(6) (2020)","DOI":"10.3390\/app10062020"},{"key":"5_CR56","unstructured":"Seo, H., Park, J., Park, Y.: Acoustic scene classification using various pre-processed features and convolutional neural networks. In: Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA, pp. 25\u201326 (2019)"},{"issue":"1","key":"5_CR57","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/LSP.2018.2878620","volume":"26","author":"V Lostanlen","year":"2018","unstructured":"Lostanlen, V., et al.: Per-channel energy normalization: why and how. IEEE Signal Process. Lett. 26(1), 39\u201343 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"5_CR58","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lee, T.: Enhancing sound texture in CNN-based acoustic scene classification. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2019, pp. 815\u2013819. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683490"},{"key":"5_CR59","unstructured":"Mariotti, O., Cord, M., Schwander, O.: Exploring deep vision models for acoustic scene classification. In: Proceedings of the DCASE, pp. 103\u2013107 (2018)"},{"key":"5_CR60","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"5_CR61","doi-asserted-by":"crossref","unstructured":"Gemmeke, J.F., et al.: Audio set: an ontology and human-labeled dataset for audio events. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776\u2013780. IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"5_CR62","doi-asserted-by":"crossref","unstructured":"Koutini, K., Eghbal-zadeh, H., Widmer, G.: Receptive-field-regularized CNN variants for acoustic scene classification. arXiv preprint arXiv:1909.02859 (2019)","DOI":"10.33682\/cjd9-kc43"},{"key":"5_CR63","doi-asserted-by":"crossref","unstructured":"Park, D.S., et al.: SpecAugment: a simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779 (2019)","DOI":"10.21437\/Interspeech.2019-2680"},{"key":"5_CR64","unstructured":"Lasseck, M.: Acoustic bird detection with deep convolutional neural networks. In: Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018), pp. 143\u2013147 (2018)"},{"key":"5_CR65","unstructured":"Li, J., Deng, L., Haeb-Umbach, R., Gong, Y.: Robust automatic speech recognition: a bridge to practical applications (2015)"}],"container-title":["Communications in Computer and Information Science","Information and Software Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16302-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T23:23:11Z","timestamp":1665012191000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16302-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031163012","9783031163029"],"references-count":65,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16302-9_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information and Software Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icist2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icist.ktu.edu\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66","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":"23","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":"3","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":"35% - 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":"3","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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}