{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T05:25:47Z","timestamp":1769923547717,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["41976185"],"award-info":[{"award-number":["41976185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Region-function combinations are essential for smartphones to be intelligent and context-aware. The prerequisite for providing intelligent services is that the device can recognize the contextual region in which it resides. The existing region recognition schemes are mainly based on indoor positioning, which require pre-installed infrastructures or tedious calibration efforts or memory burden of precise locations. In addition, location classification recognition methods are limited by either their recognition granularity being too large (room-level) or too small (centimeter-level, requiring training data collection at multiple positions within the region), which constrains the applications of providing contextual awareness services based on region function combinations. In this paper, we propose a novel mobile system, called Echo-ID, that enables a phone to identify the region in which it resides without requiring any additional sensors or pre-installed infrastructure. Echo-ID applies Frequency Modulated Continuous Wave (FMCW) acoustic signals as its sensing medium which is transmitted and received by the speaker and microphones already available in common smartphones. The spatial relationships among the surrounding objects and the smartphone are extracted with a signal processing procedure. We further design a deep learning model to achieve accurate region identification, which calculate finer features inside the spatial relations, robust to phone placement uncertainty and environmental variation. Echo-ID requires users only to put their phone at two orthogonal angles for 8.5 s each inside a target region before use. We implement Echo-ID on the Android platform and evaluate it with Xiaomi 12 Pro and Honor-10 smartphones. Our experiments demonstrate that Echo-ID achieves an average accuracy of 94.6% for identifying five typical regions, with an improvement of 35.5% compared to EchoTag. The results confirm Echo-ID\u2019s robustness and effectiveness for region identification.<\/jats:p>","DOI":"10.3390\/s23094302","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T02:18:34Z","timestamp":1682561914000},"page":"4302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Echo-ID: Smartphone Placement Region Identification for Context-Aware Computing"],"prefix":"10.3390","volume":"23","author":[{"given":"Xueting","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongning","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4167-6037","authenticated-orcid":false,"given":"Feng","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"49:1","DOI":"10.1145\/2483669.2483682","article-title":"Semantic trajectories: Mobility data computation and annotation","volume":"4","author":"Yan","year":"2013","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s00779-016-0981-1","article-title":"CondioSense: High-quality context-aware service for audio sensing system via active sonar","volume":"21","author":"Li","year":"2017","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Khanum, A., Lee, C.Y., and Yang, C.S. (2022). Deep-Learning-Based Network for Lane Following in Autonomous Vehicles. Electronics, 11.","DOI":"10.3390\/electronics11193084"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Faisal, M.M., Mohammed, M.S., Abduljabar, A.M., Abdulhussain, S.H., Mahmmod, B.M., Khan, W., and Hussain, A. (2021, January 7\u201310). Object Detection and Distance Measurement Using AI. Proceedings of the 2021 14th International Conference on Developments in eSystems Engineering (DeSE), Sharjah, United Arab Emirates.","DOI":"10.1109\/DeSE54285.2021.9719469"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100969","DOI":"10.1016\/j.rineng.2023.100969","article-title":"Low-cost autonomous car level 2: Design and implementation for conventional vehicles","volume":"17","author":"Mohammed","year":"2023","journal-title":"Results Eng."},{"key":"ref_6","unstructured":"Mayabrahmma, A., Beesetty, Y., Shadaab, K., and Vineet, K. (2023, March 01). Location-Based Services Market. Available online: https:\/\/www.alliedmarketresearch.com\/location-based-services-market."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/2.940013","article-title":"Implementing a Sentient Computing System","volume":"34","author":"Addlesee","year":"2001","journal-title":"Computer"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1145\/128756.128759","article-title":"The active badge location system","volume":"10","author":"Want","year":"1992","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Azizyan, M., Constandache, I., and Choudhury, R.R. (2009, January 20\u201325). SurroundSense: Mobile phone localization via ambience fingerprinting. Proceedings of the ACM\/IEEE International Conference on Mobile Computing and Networking, Beijing, China.","DOI":"10.1145\/1614320.1614350"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Borriello, G., Liu, A.L., Offer, T., Palistrant, C., and Sharp, R. (2005, January 25\u201330). WALRUS: Wireless acoustic location with room-level resolution using ultrasound. Proceedings of the ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, Singapore.","DOI":"10.1145\/1067170.1067191"},{"key":"ref_11","unstructured":"Tarzia, S.P., Dinda, P.A., Dick, R.P., and Memik, G. (July, January 28). Indoor localization without infrastructure using the acoustic background spectrum. Proceedings of the ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, Bethesda, MD, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hightower, J., Consolvo, S., LaMarca, A., Smith, I.E., and Hughes, J. (2005, January 6\u20139). Learning and Recognizing the Places We Go. Proceedings of the Ubiquitous Computing, Nagasaki, Japan.","DOI":"10.1007\/11551201_10"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tung, Y.C., and Shin, K.G. (2015, January 7\u201311). EchoTag: Accurate Infrastructure-Free Indoor Location Tagging with Smartphones. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, Paris, France.","DOI":"10.1145\/2789168.2790102"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tung, Y.C., Bui, D.V., and Shin, K.G. (2018, January 10\u201315). Cross-Platform Support for Rapid Development of Mobile Acoustic Sensing Applications. Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, Munich, Germany.","DOI":"10.1145\/3210240.3210312"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cheng, H., and Lou, W. (2021, January 10\u201313). Push the Limit of Device-Free Acoustic Sensing on Commercial Mobile Devices. Proceedings of the IEEE INFOCOM 2021\u2014IEEE Conference on Computer Communications, Vancouver, BC, Canada.","DOI":"10.1109\/INFOCOM42981.2021.9488703"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kunze, K., and Lukowicz, P. (2007, January 16\u201319). Symbolic Object Localization Through Active Sampling of Acceleration and Sound Signatures. Proceedings of the Ubiquitous Computing, Innsbruck, Austria.","DOI":"10.1007\/978-3-540-74853-3_10"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Diao, W., Liu, X., and Zhang, K. (2014, January 3\u20137). Acoustic Fingerprinting Revisited: Generate Stable Device ID Stealthily with Inaudible Sound. Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Scottsdale, AZ, USA.","DOI":"10.1145\/2660267.2660300"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"161989:1","DOI":"10.1155\/2014\/161989","article-title":"Chirp Signal Transform and Its Properties","volume":"2014","author":"Horai","year":"2014","journal-title":"J. Appl. Math."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/JIOT.2019.2953713","article-title":"Door-Monitor: Counting In-and-Out Visitors with COTS WiFi Devices","volume":"7","author":"Yang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e7311","DOI":"10.1002\/cpe.7311","article-title":"Fast and accurate computation of high-order Tchebichef polynomials","volume":"34","author":"Abdulhussain","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s10710-017-9314-z","article-title":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning","volume":"19","author":"Heaton","year":"2017","journal-title":"Genet. Program. Evolvable Mach."},{"key":"ref_22","unstructured":"Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., and Cai, J. (2015). Recent advances in convolutional neural networks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, \u00c0., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning Deep Features for Discriminative Localization. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_26","unstructured":"(2023, January 20). GPS: The Global Positioning System, Available online: https:\/\/www.gps.gov\/."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Y., Lymberopoulos, D., Liu, J., and Priyantha, B. (2012, January 25\u201329). FM-based indoor localization. Proceedings of the ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, Low Wood Bay Lake District, UK.","DOI":"10.1145\/2307636.2307653"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1109\/TMC.2015.2451628","article-title":"Guoguo: Enabling Fine-Grained Smartphone Localization via Acoustic Anchors","volume":"15","author":"Liu","year":"2016","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Haeberlen, A., Flannery, E., Ladd, A.M., Rudys, A., Wallach, D.S., and Kavraki, L.E. (2004, January 15\u201318). Practical robust localization over large-scale 802.11 wireless networks. Proceedings of the ACM\/IEEE International Conference on Mobile Computing and Networking, Philadelphia, PA, USA.","DOI":"10.1145\/1023720.1023728"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Youssef, M., and Agrawala, A.K. (2005, January 6\u20138). The Horus WLAN location determination system. Proceedings of the ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, Seattle, WA, USA.","DOI":"10.1145\/1067170.1067193"},{"key":"ref_31","unstructured":"Vasisht, D., Kumar, S., and Katabi, D. (2016, January 16\u201318). Decimeter-Level Localization with a Single WiFi Access Point. Proceedings of the Symposium on Networked Systems Design and Implementation, Santa Clara, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"28405","DOI":"10.1007\/s11042-022-12481-3","article-title":"Indoor localization system using deep learning based scene recognition","volume":"81","author":"Labinghisa","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"51:1","DOI":"10.1145\/3397320","article-title":"VocalLock: Sensing Vocal Tract for Passphrase-Independent User Authentication Leveraging Acoustic Signals on Smartphones","volume":"4","author":"Lu","year":"2020","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, M., Wang, Q., Yang, J., Li, Q., Xiao, F., Wang, Z., and Chen, X. (2018, January 15\u201319). PatternListener: Cracking Android Pattern Lock Using Acoustic Signals. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, Toronto, ON, Canada.","DOI":"10.1145\/3243734.3243777"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rathore, A.S., Zhu, W., Daiyan, A., Xu, C., Wang, K., Lin, F., Ren, K., and Xu, W. (2020, January 15\u201319). SonicPrint: A generally adoptable and secure fingerprint biometrics in smart devices. Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, Toronto, ON, Canada.","DOI":"10.1145\/3386901.3388939"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dai, H., Wang, W., Liu, A.X., Ling, K., and Sun, J. (2019, January 10\u201313). Speech Based Human Authentication on Smartphones. Proceedings of the 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Boston, MA, USA.","DOI":"10.1109\/SAHCN.2019.8824958"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tan, J., Nguyen, C.T., and Wang, X. (2017, January 1\u20134). SilentTalk: Lip reading through ultrasonic sensing on mobile phones. Proceedings of the IEEE INFOCOM 2017\u2014IEEE Conference on Computer Communications, Atlanta, GA, USA.","DOI":"10.1109\/INFOCOM.2017.8057099"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TNET.2019.2891733","article-title":"Lip Reading-Based User Authentication Through Acoustic Sensing on Smartphones","volume":"27","author":"Lu","year":"2019","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"36:1","DOI":"10.1145\/3191768","article-title":"SilentKey: A New Authentication Framework through Ultrasonic-based Lip Reading","volume":"2","author":"Tan","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_40","unstructured":"Zhou, B., Lohokare, J., Gao, R., and Ye, F. (November, January 29). EchoPrint: Two-factor Authentication using Acoustics and Vision on Smartphones. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, New Delhi, India."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, R., and Mao, S. (August, January 31). SonarBeat: Sonar Phase for Breathing Beat Monitoring with Smartphones. Proceedings of the 2017 26th International Conference on Computer Communication and Networks (ICCCN), Vancouver, BC, Canada.","DOI":"10.1109\/ICCCN.2017.8038412"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3161188","article-title":"C-FMCW Based Contactless Respiration Detection Using Acoustic Signal","volume":"1","author":"Wang","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Qian, K., Wu, C., Xiao, F., Zheng, Y., Zhang, Y., Yang, Z., and Liu, Y. (2018, January 16\u201319). Acousticcardiogram: Monitoring Heartbeats using Acoustic Signals on Smart Devices. Proceedings of the IEEE INFOCOM 2018\u2014IEEE Conference on Computer Communications, Honolulu, HI, USA.","DOI":"10.1109\/INFOCOM.2018.8485978"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, X., Yu, J., Chen, Y., Zhu, Y., Kong, L., and Li, M. (2019, January 17\u201321). BreathListener: Fine-grained Breathing Monitoring in Driving Environments Utilizing Acoustic Signals. Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, Seoul, Republic of Korea.","DOI":"10.1145\/3307334.3326074"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/2867070.2867078","article-title":"Contactless Sleep Apnea Detection on Smartphones","volume":"19","author":"Nandakumar","year":"2015","journal-title":"GetMobile Mob. Comput. Commun."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Du, H., Li, P., Zhou, H., Gong, W., Luo, G., and Yang, P. (2018, January 16\u201319). WordRecorder: Accurate Acoustic-based Handwriting Recognition Using Deep Learning. Proceedings of the IEEE INFOCOM 2018\u2014IEEE Conference on Computer Communications, Honolulu, HI, USA.","DOI":"10.1109\/INFOCOM.2018.8486285"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kim, H., Byanjankar, A., Liu, Y., Shu, Y., and Shin, I. (2018, January 4\u20137). UbiTap: Leveraging Acoustic Dispersion for Ubiquitous Touch Interface on Solid Surfaces. Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, Shenzhen, China.","DOI":"10.1145\/3274783.3274848"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, M., Lin, J., Zou, Y., Ruby, R., and Wu, K. (2020, January 23\u201327). SilentSign: Device-free Handwritten Signature Verification through Acoustic Sensing. Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom), Austin, TX, USA.","DOI":"10.1109\/PerCom45495.2020.9127372"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yang, Q., Ruby, R., Han, Y., Wu, S., Li, M., and Wu, K. (2019, January 7\u20139). EchoWrite: An Acoustic-based Finger Input System Without Training. Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Dallas, TX, USA.","DOI":"10.1109\/ICDCS.2019.00082"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yang, Q., Han, Y., Wang, D., Cao, J., and Wu, K. (2019, January 11\u201315). AcouDigits: Enabling Users to Input Digits in the Air. Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom 2019), Kyoto, Japan.","DOI":"10.1109\/PERCOM.2019.8767415"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Chen, T., Liu, Y., and Li, Z. (December, January 29). Mobile Phones Know Your Keystrokes through the Sounds from Finger\u2019s Tapping on the Screen. Proceedings of the 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), Singapore.","DOI":"10.1109\/ICDCS47774.2020.00102"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lu, L., Yu, J., Chen, Y., Zhu, Y., Xu, X., Xue, G., and Li, M. (May, January 29). KeyLiSterber: Inferring Keystrokes on QWERTY Keyboard of Touch Screen through Acoustic Signals. Proceedings of the IEEE INFOCOM 2019\u2014IEEE Conference on Computer Communications, Paris, France.","DOI":"10.1109\/INFOCOM.2019.8737591"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, Y., Shen, J., and Zheng, Y. (2020, January 6\u20139). Push the Limit of Acoustic Gesture Recognition. Proceedings of the IEEE INFOCOM 2020\u2014IEEE Conference on Computer Communications, Virtual.","DOI":"10.1109\/INFOCOM41043.2020.9155402"},{"key":"ref_54","first-page":"2620","article-title":"UltraGesture: Fine-Grained Gesture Sensing and Recognition","volume":"21","author":"Ling","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_55","unstructured":"Sun, K., Zhao, T., Wang, W., and Xie, L. (November, January 29). VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, New Delhi, India."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wirz, M., Roggen, D., and Tr\u00f6ster, G. (2010, January 10\u201313). A wearable, ambient sound-based approach for infrastructureless fuzzy proximity estimation. Proceedings of the International Symposium on Wearable Computers (ISWC) 2010, Seoul, Republic of Korea.","DOI":"10.1109\/ISWC.2010.5665863"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Shen, S. (2020, January 21\u201325). Voice localization using nearby wall reflections. Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, London, UK.","DOI":"10.1145\/3372224.3380884"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Wang, W., Li, J., He, Y., and Liu, Y. (2020, January 16\u201319). Symphony: Localizing multiple acoustic sources with a single microphone array. Proceedings of the 18th Conference on Embedded Networked Sensor Systems, Virtual Event, Japan.","DOI":"10.1145\/3384419.3430724"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tachikawa, M., Maekawa, T., and Matsushita, Y. (2016, January 12\u201316). Predicting location semantics combining active and passive sensing with environment-independent classifier. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971684"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1145\/3264945","article-title":"Deep Room Recognition Using Inaudible Echos","volume":"2","author":"Song","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"20640","DOI":"10.1109\/JIOT.2022.3177410","article-title":"Acoustic-Sensing-Based Location Semantics Identification Using Smartphones","volume":"9","author":"Chen","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_62","unstructured":"Rossi, M., Seiter, J., Amft, O., Buchmeier, S., and Tr\u00f6ster, G. (2013, January 7\u20138). RoomSense: An indoor positioning system for smartphones using active sound probing. Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Stuttgart, Germany."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5372","DOI":"10.1109\/JSEN.2021.3102916","article-title":"IndoLabel: Predicting Indoor Location Class by Discovering Location-Specific Sensor Data Motifs","volume":"22","author":"Dissanayake","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/3214278","article-title":"Smartphone-based Acoustic Indoor Space Mapping","volume":"2","author":"Pradhan","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhou, B., Elbadry, M., Gao, R., and Ye, F. (2017, January 19\u201323). BatMapper: Acoustic Sensing Based Indoor Floor Plan Construction Using Smartphones. Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, Niagara Falls, NY, USA.","DOI":"10.1145\/3081333.3081363"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4302\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:23:47Z","timestamp":1760124227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,26]]},"references-count":65,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094302"],"URL":"https:\/\/doi.org\/10.3390\/s23094302","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,26]]}}}