{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T02:05:44Z","timestamp":1773108344020,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T00:00:00Z","timestamp":1597622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours are uncrowded places, where user safety is guaranteed by company overseers. Due to the large size, ensuring adequate surveillance would require many operators to increase the costs of parking fees. To reduce costs, video surveillance systems are used, in which an operator monitors many areas. However, some activities are beyond the control of this technology. In this work, a procedure to identify sound events in an underground garage is developed. The aim of the work is to detect sounds identifying dangerous situations and to activate an automatic alert that draws the attention of surveillance in that area. To do this, the sounds of a parking sector were detected with the use of sound sensors. These sounds were analyzed by a sound detector based on convolutional neural networks. The procedure returned high accuracy in identifying a car crash in an underground parking area.<\/jats:p>","DOI":"10.3390\/bdcc4030020","type":"journal-article","created":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T06:33:20Z","timestamp":1597646000000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2972-0701","authenticated-orcid":false,"given":"Giuseppe","family":"Ciaburro","sequence":"first","affiliation":[{"name":"Department of Architecture and Industrial Design, University of Campania Luigi Vanvitelli, 81031 Aversa, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,17]]},"reference":[{"key":"ref_1","unstructured":"Chrest, A.P., Smith, M.S., Bhuyan, S., Iqbal, M., and Monahan, D.R. (2012). Parking Structures: Planning, Design, Construction, Maintenance and Repair, Springer Science & Business Media."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.trd.2010.07.009","article-title":"A study of an environmental-friendly parking policy","volume":"16","author":"Chu","year":"2011","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_3","first-page":"181","article-title":"Suburban Shopping Center Effects on Highways and Parking","volume":"10","author":"Hoyt","year":"1956","journal-title":"Traffic Q."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s002679900231","article-title":"Utilization of parking lots in Hattiesburg, Mississippi, USA, and impacts on local streams","volume":"24","author":"Albanese","year":"1998","journal-title":"Environ. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/14649357.2017.1416776","article-title":"Cities, Automation, and the Self-parking Elephant in the Room","volume":"19","author":"Guerra","year":"2018","journal-title":"Plan. Theory Pract."},{"key":"ref_6","first-page":"235","article-title":"Automation with RFID technology as an application: Parking lot circulation control","volume":"2","author":"Pala","year":"2008","journal-title":"J. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s00138-008-0153-z","article-title":"IBM smart surveillance system (S3): Event based video surveillance system with an open and extensible framework","volume":"19","author":"Tian","year":"2008","journal-title":"Mach. Vis. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, T., Chowdhery, A., Bahl, P., Jamieson, K., and Banerjee, S. (2015, January 7\u201311). The design and implementation of a wireless video surveillance system. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, Paris, France.","DOI":"10.1145\/2789168.2790123"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TCE.2009.5174427","article-title":"Automatic video-based human motion analyzer for consumer surveillance system","volume":"55","author":"Lao","year":"2009","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_10","unstructured":"Muller-Schneiders, S., Jager, T., Loos, H.S., and Niem, W. (2005, January 15\u201316). Performance evaluation of a real time video surveillance system. Proceedings of the 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Beijing, China."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3846\/16486897.2004.9636819","article-title":"Testing on noise level prevailing at motor vehicle parking lots and numeral simulation of its dispersion","volume":"12","author":"Kazlauskas","year":"2004","journal-title":"J. Environ. Eng. Landsc. Manag."},{"key":"ref_12","first-page":"49","article-title":"Proposal for reducing problems of the air pollution and noise in the urban environment","volume":"5","author":"Mrkajic","year":"2010","journal-title":"Carpathian J. Earth Environ. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S0272-4944(85)80001-3","article-title":"Perceptions of the security and attractiveness of urban parking lots","volume":"5","author":"Shaffer","year":"1985","journal-title":"J. Environ. Psychol."},{"key":"ref_14","first-page":"7","article-title":"Planting in parking lots to improve perceived attractiveness and security","volume":"15","author":"Anderson","year":"1989","journal-title":"J. Arboric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0886-7798(98)00060-1","article-title":"On safety systems for underground car parks","volume":"13","author":"Chow","year":"1998","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hill, J., Rhodes, G., Vollar, S., and Whapples, C. (2005). Car Park Designers\u2032 Handbook, Thomas Telford.","DOI":"10.1680\/cpdh.34389"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101","DOI":"10.3923\/itj.2009.101.113","article-title":"Car park system: A review of smart parking system and its technology","volume":"8","author":"Idris","year":"2009","journal-title":"Inf. Technol. J."},{"key":"ref_18","first-page":"43","article-title":"A survey of shape feature extraction techniques","volume":"15","author":"Mingqiang","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nussbaumer, H.J. (1981). The fast Fourier transform. Fast Fourier Transform and Convolution Algorithms, Springer.","DOI":"10.1007\/978-3-662-00551-4"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Van Loan, C. (1992). Computational Frameworks for the Fast Fourier Transform, Society for Industrial and Applied Mathematics.","DOI":"10.1137\/1.9781611970999"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/MSPEC.1967.5217220","article-title":"The Fast Fourier Transform","volume":"4","author":"Brigham","year":"1967","journal-title":"IEEE Spectr."},{"key":"ref_22","first-page":"116","article-title":"Experiments on spectrogram reading","volume":"Volume 4","author":"Zue","year":"1979","journal-title":"Proceedings of the ICASSP\u203279. IEEE International Conference on Acoustics, Speech, and Signal Processing, Washington, DC, USA, 2\u20134 April 1979"},{"key":"ref_23","first-page":"IV-293","article-title":"Multiple window spectrogram and time-frequency distributions","volume":"Volume 4","author":"Fraser","year":"1994","journal-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP\u203294, Adelaide, Australia, 19\u201322 April 1994"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv.","DOI":"10.3115\/v1\/P14-1062"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Mohamed, A.R., Kingsbury, B., and Ramabhadran, B. (2013). Deep convolutional neural networks for LVCSR. Proceedings of the 2013 IEEE international Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 26\u201331 May 2013, IEEE.","DOI":"10.1109\/ICASSP.2013.6639347"},{"key":"ref_26","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_27","unstructured":"Xu, L., Ren, J.S., Liu, C., and Jia, J. (2014). Deep convolutional neural network for image deconvolution. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_28","unstructured":"Sedghi, H., Gupta, V., and Long, P.M. (2018). The singular values of convolutional layers. arXiv."},{"key":"ref_29","unstructured":"He, J., Li, L., Xu, J., and Zheng, C. (2018). ReLU deep neural networks and linear finite elements. arXiv."},{"key":"ref_30","first-page":"23","article-title":"Convolutional Neural Network Model in Machine Learning Methods and Computer Vision for Image Recognition: A Review","volume":"14","author":"Jaapar","year":"2018","journal-title":"J. Appl. Sci. Res."},{"key":"ref_31","unstructured":"Ma, W., and Lu, J. (2017). An equivalence of fully connected layer and convolutional layer. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yuan, B. (2016). Efficient hardware architecture of softmax layer in deep neural network. Proceedings of the 2016 29th IEEE International System-on-Chip Conference (SOCC), Seattle, WA, USA, 6\u20139 September 2016, IEEE.","DOI":"10.1109\/SOCC.2016.7905501"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.artmed.2018.04.008","article-title":"Lung sounds classification using convolutional neural networks","volume":"88","author":"Bardou","year":"2018","journal-title":"Artif. Intell. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","article-title":"Deep convolutional neural networks and data augmentation for environmental sound classification","volume":"24","author":"Salamon","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015). Environmental sound classification with convolutional neural networks. Proceedings of the 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), Boston, MA, USA, 17\u201320 September 2015, IEEE.","DOI":"10.1109\/MLSP.2015.7324337"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TCSVT.2018.2828606","article-title":"A robust moving object detection in multi-scenario big data for video surveillance","volume":"29","author":"Chen","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_37","unstructured":"Xu, H., Caramanis, C., and Sanghavi, S. (2010). Robust PCA via outlier pursuit. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_38","unstructured":"Guan, N., Tao, D., Luo, Z., and Shawe-Taylor, J. (2012). MahNMF: Manhattan non-negative matrix factorization. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107239","DOI":"10.1016\/j.apacoust.2020.107239","article-title":"Modelling sound absorption properties of broom fibers using artificial neural networks","volume":"163","author":"Iannace","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Iannace, G., Ciaburro, G., and Trematerra, A. (2019). Wind turbine noise prediction using random forest regression. Machines, 7.","DOI":"10.3390\/machines7040069"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Iannace, G., Ciaburro, G., and Trematerra, A. (2018). Heating, ventilation, and air conditioning (HVAC) noise detection in open-plan offices using recursive partitioning. Buildings, 8.","DOI":"10.3390\/buildings8120169"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Iannace, G., Ciaburro, G., and Trematerra, A. (2019). Fault diagnosis for UAV blades using artificial neural network. Robotics, 8.","DOI":"10.3390\/robotics8030059"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Junio Guimar\u00e3es, A., Vitor de Campos Souza, P., Jonathan Silva Ara\u00fajo, V., Silva Rezende, T., and Souza Ara\u00fajo, V. (2019). Pruning fuzzy neural network applied to the construction of expert systems to aid in the diagnosis of the treatment of cryotherapy and immunotherapy. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3020022"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/4\/3\/20\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:02:03Z","timestamp":1760176923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/4\/3\/20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,17]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["bdcc4030020"],"URL":"https:\/\/doi.org\/10.3390\/bdcc4030020","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,17]]}}}