{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T08:04:18Z","timestamp":1771661058386,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030801281","type":"print"},{"value":"9783030801298","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-80129-8_71","type":"book-chapter","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T09:04:22Z","timestamp":1625475862000},"page":"1083-1091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Entropy Based Feature Pooling in Speech Command Classification"],"prefix":"10.1007","author":[{"given":"Christoforos","family":"Nalmpantis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lazaros","family":"Vrysis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danai","family":"Vlachava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lefteris","family":"Papageorgiou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris","family":"Vrakas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"71_CR1","doi-asserted-by":"crossref","unstructured":"Bountourakis, V., Vrysis, L., Konstantoudakis, K., Vryzas, N.: An enhanced temporal feature integration method for environmental sound recognition. In: Acoustics, vol.\u00a01, pp. 410\u2013422. Multidisciplinary Digital Publishing Institute (2019)","DOI":"10.3390\/acoustics1020023"},{"key":"71_CR2","unstructured":"Boureau, Y.L., Ponce, J., LeCun, Y.: A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 111\u2013118 (2010)"},{"key":"71_CR3","doi-asserted-by":"crossref","unstructured":"Coucke, A., Chlieh, M., Gisselbrecht, T., Leroy, D., Poumeyrol, M., Lavril, T.: Efficient keyword spotting using dilated convolutions and gating. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6351\u20136355 (2019)","DOI":"10.1109\/ICASSP.2019.8683474"},{"key":"71_CR4","doi-asserted-by":"crossref","unstructured":"Fayyad, J., Jaradat, M.A., Gruyer, D., Najjaran, H.: Deep learning sensor fusion for autonomous vehicle perception and localization: a review. Sensors 20(15), 4220 (2020)","DOI":"10.3390\/s20154220"},{"key":"71_CR5","doi-asserted-by":"crossref","unstructured":"Han, W., et al.: Contextnet: improving convolutional neural networks for automatic speech recognition with global context. arXiv preprintarXiv:2005.03191 (2020)","DOI":"10.21437\/Interspeech.2020-2059"},{"key":"71_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"71_CR7","unstructured":"Kusupati, A., Singh, M., Bhatia, K., Kumar, A., Jain, P., Varma, M.: Fastgrnn: a fast, accurate, stable and tiny kilobyte sized gated recurrent neural network. In: Advances in Neural Information Processing Systems, pp. 9017\u20139028 (2018)"},{"key":"71_CR8","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1007\/s10462-019-09724-5","volume":"53","author":"A Lentzas","year":"2020","unstructured":"Lentzas, A., Vrakas, D.: Non-intrusive human activity recognition and abnormal behavior detection on elderly people: a review. Artif. Intell. Rev. 53, 1975\u20132021 (2020). https:\/\/doi.org\/10.1007\/s10462-019-09724-5","journal-title":"Artif. Intell. Rev."},{"key":"71_CR9","doi-asserted-by":"crossref","unstructured":"McGraw, I., et\u00a0al.: Personalized speech recognition on mobile devices. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5955\u20135959. IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7472820"},{"key":"71_CR10","doi-asserted-by":"crossref","unstructured":"Nalmpantis, C., Lentzas, A., Vrakas, D.: A theoretical analysis of pooling operation using information theory. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1729\u20131733. IEEE (2019)","DOI":"10.1109\/ICTAI.2019.00256"},{"key":"71_CR11","doi-asserted-by":"publisher","unstructured":"Nalmpantis, C., Vrakas, D.: On time series representations for multi-label NILM. Neural Comput. Appl. 32, 17275\u201317290 (2020). https:\/\/doi.org\/10.1007\/s00521-020-04916-5","DOI":"10.1007\/s00521-020-04916-5"},{"key":"71_CR12","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprintarXiv:1409.1556 (2014)"},{"key":"71_CR13","doi-asserted-by":"crossref","unstructured":"Solovyev, R.A., et al.: Deep learning approaches for understanding simple speech commands. In: 2020 IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO), pp. 688\u2013693. IEEE (2020)","DOI":"10.1109\/ELNANO50318.2020.9088863"},{"key":"71_CR14","unstructured":"Tsipas, N., Vrysis, L., Dimoulas, C., Papanikolaou, G.: Mirex 2015: Methods for speech\/music detection and classification. In Processing, Music information retrieval evaluation eXchange (MIREX) (2015)"},{"key":"71_CR15","doi-asserted-by":"crossref","unstructured":"Viswanathan, J., Saranya, N., Inbamani, A.: Deep learning applications in medical imaging: Introduction to deep learning-based intelligent systems for medical applications. In: Deep Learning Applications in Medical Imaging, pp. 156\u2013177. IGI Global (2021)","DOI":"10.4018\/978-1-7998-5071-7.ch007"},{"key":"71_CR16","unstructured":"Vrysis, L., Thoidis, I., Dimoulas, C., Papanikolaou, G.: Experimenting with 1d CNN architectures for generic audio classification. In: Audio Engineering Society Convention 148. Audio Engineering Society (2020)"},{"key":"71_CR17","doi-asserted-by":"crossref","unstructured":"Vrysis, L., Tsipas, N., Thoidis, I., Dimoulas, C.: 1d\/2d deep cnns vs. temporal feature integration for general audio classification. J. Audio Eng. Soc. 68(1\/2), 66\u201377 (2020)","DOI":"10.17743\/jaes.2019.0058"},{"key":"71_CR18","unstructured":"Warden, P.: Speech commands: A dataset for limited-vocabulary speech recognition. arXiv preprintarXiv:1804.03209 (2018)"},{"key":"71_CR19","doi-asserted-by":"publisher","first-page":"10767","DOI":"10.1109\/ACCESS.2019.2891838","volume":"7","author":"M Zeng","year":"2019","unstructured":"Zeng, M., Xiao, N.: Effective combination of densenet and bilstm for keyword spotting. IEEE Access 7, 10767\u201310775 (2019)","journal-title":"IEEE Access"},{"key":"71_CR20","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Geiger, J., Pohjalainen, J., Mousa, Amr, E.D., Jin, W., Schuller, B.: Deep learning for environmentally robust speech recognition: an overview of recent developments. ACM Trans. Intell. Syst. Technol. 9(5), 28 p. (2018). https:\/\/doi.org\/10.1145\/3178115. Article 49","DOI":"10.1145\/3178115"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80129-8_71","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:13:28Z","timestamp":1771658008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80129-8_71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030801281","9783030801298"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80129-8_71","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}