{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T13:34:01Z","timestamp":1770730441105,"version":"3.49.0"},"reference-count":124,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:00:00Z","timestamp":1702684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Things"],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>\n            This article presents\n            <jats:italic>EARFace<\/jats:italic>\n            , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user interfaces, AR\/VR applications, affective computing, and accessibility, among others. Although conventional vision-based solutions break down under poor lighting and occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking,\n            <jats:italic>EARFace<\/jats:italic>\n            shows the feasibility of completely in-ear sensing with a natural earphone form factor, thus enhancing the comfort levels of wearing. The core intuition exploited by\n            <jats:italic>EARFace<\/jats:italic>\n            is that the shape of the ear canal changes due to the movement of facial muscles during facial motion.\n            <jats:italic>EARFace<\/jats:italic>\n            tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response of the inner ear, ultimately resulting in tracking of the facial motion. A transformer-based machine learning model is designed to exploit spectral and temporal relationships in the ultrasonic channel frequency response data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in\n            <jats:italic>EARFace<\/jats:italic>\n            . The transformer-based machine learning model runs on smart phone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing\n            <jats:italic>EARFace<\/jats:italic>\n            \u2019s earphone platform in comparison with alternative form factors.\n          <\/jats:p>","DOI":"10.1145\/3614438","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T11:20:29Z","timestamp":1691580029000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["I Am an Earphone and I Can Hear My User\u2019s Face: Facial Landmark Tracking Using Smart Earphones"],"prefix":"10.1145","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7304-4571","authenticated-orcid":false,"given":"Shijia","family":"Zhang","sequence":"first","affiliation":[{"name":"Penn State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9695-3142","authenticated-orcid":false,"given":"Taiting","family":"Lu","sequence":"additional","affiliation":[{"name":"Penn State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7339-9006","authenticated-orcid":false,"given":"Hao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Penn State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4322-1818","authenticated-orcid":false,"given":"Yilin","family":"Liu","sequence":"additional","affiliation":[{"name":"Penn State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2342-1644","authenticated-orcid":false,"given":"Runze","family":"Liu","sequence":"additional","affiliation":[{"name":"Penn State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5325-5013","authenticated-orcid":false,"given":"Mahanth","family":"Gowda","sequence":"additional","affiliation":[{"name":"Penn State University, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,12,16]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Apple. 2023 Airpods. Retrieved August 12 2023 from https:\/\/www.apple.com\/airpods\/"},{"key":"e_1_3_2_3_2","unstructured":"Rebecca. 2020. What Are True Wireless Stereo (TWS) Headphones? Retrieved August 12 2023 from https:\/\/blog.taotronics.com\/headphones\/tws-headphones\/"},{"key":"e_1_3_2_4_2","unstructured":"Fine Art America. 2023. Facial Muscle Anatomy. Retrieved August 12 2023 from https:\/\/fineartamerica.com\/featured\/face-muscle-anatomy-maurizio-de-angelisscience-photo-library.html"},{"key":"e_1_3_2_5_2","unstructured":"Jennifer Shennan. n.d. Muscles of Facial Expression. Retrieved August 12 2023 from https:\/\/geekymedics.com\/muscles-of-facial-expression\/"},{"key":"e_1_3_2_6_2","unstructured":"Android for Developers. n.d. Profile Battery Usage with Batterystats and Battery Historian. Retrieved August 12 2023 from https:\/\/developer.android.com\/topic\/performance\/power\/setup-battery-historian"},{"key":"e_1_3_2_7_2","unstructured":"VRU. n.d. Sine Sweep Test. Retrieved August 12 2023 from https:\/\/vru.vibrationresearch.com\/lesson\/sine-sweep-test\/"},{"key":"e_1_3_2_8_2","unstructured":"CBC. 2023. The Seven Universal Emotions We Wear on Our Face. Retrieved August 12 2023 from https:\/\/www.cbc.ca\/natureofthings\/features\/the-seven-universal-emotions-we-wear-on-our-face#"},{"key":"e_1_3_2_9_2","unstructured":"Espressif Systems. 2023. ESP32. Retrieved August 12 2023 from https:\/\/www.espressif.com\/en\/products\/socs\/esp32"},{"key":"e_1_3_2_10_2","unstructured":"Centers for Disease Control and Prevention. 1998. Criteria for a Recommended Standard: Occupational Noise Exposure. Retrieved August 12 2023 from https:\/\/www.cdc.gov\/niosh\/docs\/98-126\/default.html"},{"key":"e_1_3_2_11_2","unstructured":"Sonion. 2022. EST65DB01. Retrieved August 12 2023 from https:\/\/www.sonion.com\/product\/est65da01\/"},{"key":"e_1_3_2_12_2","unstructured":"Sonion. 2022. Data Sheet: Microphone P11AC03. Retrieved August 12 2023 from https:\/\/www.sonion.com\/wp-content\/uploads\/ds-P11AC03_v3.pdf"},{"key":"e_1_3_2_13_2","unstructured":"SciPy Community. 2022. scipy.signal.chirp. Retrieved August 12 2023 from https:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.signal.chirp.html"},{"key":"e_1_3_2_14_2","unstructured":"Texas Instruments. 2022. Low Power Single-Supply Rail-to-Rail Operational Amplifiers: MicroAmplifier Series. Retrieved August 12 2023 from https:\/\/www.ti.com\/lit\/ds\/symlink\/opa344.pdf"},{"key":"e_1_3_2_15_2","unstructured":"PJRC. 2022. Audio Adaptor Boards for Teensy 3.x and Teensy 4.x. Retrieved August 12 2023 from https:\/\/www.pjrc.com\/store\/teensy3_audio.html"},{"key":"e_1_3_2_16_2","unstructured":"PJRC. 2022. Teensyduino. Retrieved August 12 2023 from https:\/\/www.pjrc.com\/teensy\/teensyduino.html"},{"key":"e_1_3_2_17_2","unstructured":"PJRC. 2022. Teensy 4.1 Development Board. Retrieved August 12 2023 from https:\/\/www.pjrc.com\/store\/teensy41.html"},{"key":"e_1_3_2_18_2","unstructured":"GitHub. 2022. WiFiNINA Library for Arduino. Retrieved August 12 2023 from https:\/\/github.com\/adafruit\/WiFiNINA"},{"key":"e_1_3_2_19_2","first-page":"265","volume-title":"Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201916)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi et\u00a0al. 2016. TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201916). 265\u2013283."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41699-018-0064-4"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3341163.3347747"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3095176"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3126594.3126649"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00019"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc3010014"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3225761"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3300061.3345454"},{"issue":"3","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1109\/TVCG.2013.249","article-title":"FaceWarehouse: A 3D facial expression database for visual computing","volume":"20","author":"Cao Chen","year":"2013","unstructured":"Chen Cao, Yanlin Weng, Shun Zhou, Yiying Tong, and Kun Zhou. 2013. FaceWarehouse: A 3D facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics 20, 3 (2013), 413\u2013425.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430730"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.143"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472621"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ridd.2014.10.015"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2015.09.033"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3379337.3415879"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534597"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446382.3450216"},{"key":"e_1_3_2_38_2","unstructured":"Streamable. 2022. Demo. Retrieved August 12 2023 from https:\/\/streamable.com\/t34w8l"},{"key":"e_1_3_2_39_2","volume-title":"Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201909)","year":"2009","unstructured":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201909)."},{"key":"e_1_3_2_40_2","unstructured":"arXiv preprint arXiv:1810.04805 2018 BERT: Pre-training of deep bidirectional transformers for language understanding"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00047"},{"key":"e_1_3_2_42_2","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy Alexey","year":"2020","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jacob Uszkoreit, and Neil Houlsby. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).","journal-title":"arXiv preprint arXiv:2010.11929"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3494988"},{"key":"e_1_3_2_44_2","unstructured":"TensorFlow. 2019. Home Page. Retrieved August 12 2023 from https:\/\/www.tensorflow.org\/lite"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-06925-2"},{"key":"e_1_3_2_46_2","unstructured":"Remote Sensing 2017 Pre-trained AlexNet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_48_2","volume-title":"Proceedings of the Asian Conference on Computer Vision","author":"He Sen","year":"2020","unstructured":"Sen He, Wentong Liao, Hamed R. Tavakoli, Michael Yang, Bodo Rosenhahn, and Nicolas Pugeault. 2020. Image captioning through image transformer. In Proceedings of the Asian Conference on Computer Vision."},{"key":"e_1_3_2_49_2","first-page":"269","article-title":"Modeling the external ear acoustics for insert headphone usage","volume":"58","author":"Hiipakka Marko","year":"2010","unstructured":"Marko Hiipakka, Miikka Tikander, and Matti Karjalainen. 2010. Modeling the external ear acoustics for insert headphone usage. Journal of the Audio Engineering Society 58 (2010), 269\u2013281.","journal-title":"Journal of the Audio Engineering Society"},{"key":"e_1_3_2_50_2","article-title":"Universal language model fine-tuning for text classification","author":"Howard Jeremy","year":"2018","unstructured":"Jeremy Howard and Sebastian Ruder. 2018. Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018).","journal-title":"arXiv preprint arXiv:1801.06146"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i5.25743"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21010091"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-8944-7_11"},{"key":"e_1_3_2_54_2","article-title":"Averaging weights leads to wider optima and better generalization","author":"Izmailov Pavel","year":"2018","unstructured":"Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. 2018. Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407 (2018).","journal-title":"arXiv preprint arXiv:1803.05407"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534613"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2688239"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3274783.3275188"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2012.09.038"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2018.8444585"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446382.3448363"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053591"},{"key":"e_1_3_2_62_2","article-title":"Adam: A method for stochastic optimization","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).","journal-title":"arXiv preprint arXiv:1412.6980"},{"key":"e_1_3_2_63_2","volume-title":"Proceedings of the XII International Conference on Speech and Computer (SPECOM\u201907)","author":"Kinnunen Tomi","year":"2012","unstructured":"Tomi Kinnunen, Evgenia Chernenko, Marko Tuononen, Pasi Franti, and Haizhou Li. 2012. Voice activity detection using MFCC features and support vector machine. In Proceedings of the XII International Conference on Speech and Computer (SPECOM\u201907)."},{"key":"e_1_3_2_64_2","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS\u201912)","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS\u201912). 1097\u20131105."},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411841"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00084"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3369837"},{"key":"e_1_3_2_68_2","article-title":"Model based face reconstruction for animation","author":"Lee Won-Sook","year":"1997","unstructured":"Won-Sook Lee, Prem Kalra, and Nadia Magnenat-Thalmann. 1997. Model based face reconstruction for animation. In Proceedings of the International Multimedia Modeling Conference (MMM\u201997). 323\u2013338.","journal-title":"Proceedings of the International Multimedia Modeling Conference (MMM\u201997)."},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00902"},{"issue":"2","key":"e_1_3_2_70_2","first-page":"1","article-title":"EarIO: A low-power acoustic sensing earable for continuously tracking detailed facial movements","volume":"6","author":"Li Ke","year":"2022","unstructured":"Ke Li, Ruidong Zhang, Bo Liang, Fran\u00e7ois Guimbreti\u00e8re, and Cheng Zhang. 2022. EarIO: A low-power acoustic sensing earable for continuously tracking detailed facial movements. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2 (2022), 1\u201324.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3130800.3130813"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3146798"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449890"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450268.3453537"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3223600"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1145\/3396339.3396362"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3082031.3083240"},{"issue":"9","key":"e_1_3_2_78_2","first-page":"4568","article-title":"Text steganography based on Ci-poetry generation using Markov chain model","volume":"10","author":"Luo Yubo","year":"2016","unstructured":"Yubo Luo, Yongfeng Huang, Fufang Li, and Chinchen Chang. 2016. Text steganography based on Ci-poetry generation using Markov chain model. KSII Transactions on Internet and Information Systems 10, 9 (2016), 4568\u20134584.","journal-title":"KSII Transactions on Internet and Information Systems"},{"key":"e_1_3_2_79_2","article-title":"Efficient multitask learning on resource-constrained systems","author":"Luo Yubo","year":"2023","unstructured":"Yubo Luo, Le Zhang, Zhenyu Wang, and Shahriar Nirjon. 2023. Efficient multitask learning on resource-constrained systems. arXiv preprint arXiv:2302.13155 (2023).","journal-title":"arXiv preprint arXiv:2302.13155"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS.2017.00013"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISMAR50242.2020.00064"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/2856767.2856770"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025692"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025692"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1145\/3458709.3458945"},{"key":"e_1_3_2_86_2","article-title":"Transformers with convolutional context for ASR","author":"Mohamed Abdelrahman","year":"2019","unstructured":"Abdelrahman Mohamed, Dmytro Okhonko, and Luke Zettlemoyer. 2019. Transformers with convolutional context for ASR. arXiv preprint arXiv:1904.11660 (2019).","journal-title":"arXiv preprint arXiv:1904.11660"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0153128"},{"key":"e_1_3_2_88_2","volume-title":"Image Analysis and Recognition","author":"Nawaz Wajahat","year":"2018","unstructured":"Wajahat Nawaz, Sagheer Ahmed, Ali Tahir, and Hassan Aqeel Khan. 2018. Classification of breast cancer histology images using AlexNet. In Image Analysis and Recognition. Lecture Notes in Computer Science, Vol. 10882. Springer, 869\u2013876."},{"key":"e_1_3_2_89_2","volume-title":"Information on Levels of Environmental Noise Requisite to Protect Public Health and Welfare with an Adequate Margin of Safety","author":"Abatement U.S. Office of Noise","year":"1974","unstructured":"U.S. Office of Noise Abatement. 1974. Information on Levels of Environmental Noise Requisite to Protect Public Health and Welfare with an Adequate Margin of Safety. Number 2115. U.S. Government Printing Office."},{"key":"e_1_3_2_90_2","unstructured":"OnePlus. 2023. OnePlus 9 Pro. Retrieved August 12 2023 from https:\/\/www.oneplus.com\/us\/9-pro"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01123"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2009.58"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419197"},{"key":"e_1_3_2_94_2","volume-title":"Cross-Correlation between Mandibular Condylar Movements and Distortion of External Auditory Meatus.","author":"Qi JunRong","year":"2016","unstructured":"JunRong Qi. 2016. Cross-Correlation between Mandibular Condylar Movements and Distortion of External Auditory Meatus.Ph. D. dissertation. Matsumoto Dental University. https:\/\/ci.nii.ac.jp\/naid\/500000981228"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331341"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.08.013"},{"key":"e_1_3_2_97_2","unstructured":"Samsung. n.d. Africa: Samsung Galaxy Note 20. https:\/\/www.samsung.com\/africa_en\/smartphones\/galaxy-note20\/models\/"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00795"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21041262"},{"key":"e_1_3_2_100_2","doi-asserted-by":"crossref","unstructured":"Xingzhe Song Kai Huang and Wei Gao. 2022. FaceListener: Recognizing human facial expressions via acoustic sensing on commodity headphones. In Proceedings of the 2022 21st ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN\u201922) .","DOI":"10.1109\/IPSN54338.2022.00019"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1145\/3550281"},{"key":"e_1_3_2_102_2","unstructured":"Paul Stoffregen. 2022. Teensy Audio Implementation Library. Retrieved August 12 2023 from https:\/\/github.com\/PaulStoffregen\/Audio"},{"key":"e_1_3_2_103_2","article-title":"SEANet: A multi-modal speech enhancement network","author":"Tagliasacchi Marco","year":"2020","unstructured":"Marco Tagliasacchi, Yunpeng Li, Karolis Misiunas, and Dominik Roblek. 2020. SEANet: A multi-modal speech enhancement network. arXiv preprint arXiv:2009.02095 (2020).","journal-title":"arXiv preprint arXiv:2009.02095"},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.220"},{"issue":"2","key":"e_1_3_2_105_2","first-page":"87","article-title":"Ear acoustic authentication technology: Using sound to identify the distinctive shape of the ear canal","volume":"13","author":"Takayuki Arakawa","year":"2019","unstructured":"Arakawa Takayuki. 2019. Ear acoustic authentication technology: Using sound to identify the distinctive shape of the ear canal. NEC Technical Journal\u2014Special Issue on Social Value Creation Using Biometrics 13, 2 (2019), 87\u201390.","journal-title":"NEC Technical Journal\u2014Special Issue on Social Value Creation Using Biometrics"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.36548\/jiip.2021.1.003"},{"key":"e_1_3_2_107_2","first-page":"1001","volume-title":"Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA\u201918)","author":"Torvi Vishwas G.","year":"2018","unstructured":"Vishwas G. Torvi, Aditya Bhattacharya, and Shayok Chakraborty. 2018. Deep domain adaptation to predict freezing of gait in patients with Parkinson\u2019s disease. In Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA\u201918). IEEE, Los Alamitos, CA, 1001\u20131006."},{"key":"e_1_3_2_108_2","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, 1\u201311.https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1145\/3478085"},{"key":"e_1_3_2_110_2","unstructured":"Pedro Villanueva. 2022. Teensy ADC Implementation Library. Retrieved August 12 2023 from https:\/\/github.com\/pedvide\/ADC"},{"key":"e_1_3_2_111_2","volume-title":"Advances in Neural Information Processing Systems","author":"Wager Stefan","year":"2013","unstructured":"Stefan Wager, Sida Wang, and Percy Liang. 2013. Dropout training as adaptive regularization. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdevneu.2014.05.012"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483252"},{"key":"e_1_3_2_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.75"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00583"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419213"},{"key":"e_1_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-020-09817-2"},{"key":"e_1_3_2_118_2","article-title":"Pretraining-based natural language generation for text summarization","author":"Zhang Haoyu","year":"2019","unstructured":"Haoyu Zhang, Jianjun Xu, and Ji Wang. 2019. Pretraining-based natural language generation for text summarization. arXiv preprint arXiv:1902.09243 (2019).","journal-title":"arXiv preprint arXiv:1902.09243"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1109\/T-AFFC.2011.13"},{"key":"e_1_3_2_120_2","first-page":"55","volume-title":"Proceedings of the 2022 IEEE\/ACM 7th International Conference on Internet-of-Things Design and Implementation (IoTDI\u201922)","author":"Zhang Shijia","year":"2022","unstructured":"Shijia Zhang, Yilin Liu, and Mahanth Gowda. 2022. Let\u2019s grab a drink: Teacher-student learning for fluid intake monitoring using smart earphones. In Proceedings of the 2022 IEEE\/ACM 7th International Conference on Internet-of-Things Design and Implementation (IoTDI\u201922). IEEE, Los Alamitos, CA, 55\u201366."},{"issue":"4","key":"e_1_3_2_121_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3569486","article-title":"I spy you: Eavesdropping continuous speech on smartphones via motion sensors","volume":"6","author":"Zhang Shijia","year":"2023","unstructured":"Shijia Zhang, Yilin Liu, and Mahanth Gowda. 2023. I spy you: Eavesdropping continuous speech on smartphones via motion sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1\u201331.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_122_2","article-title":"Revisiting few-sample BERT fine-tuning","author":"Zhang Tianyi","year":"2020","unstructured":"Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, and Yoav Artzi. 2020. Revisiting few-sample BERT fine-tuning. arXiv preprint arXiv:2006.05987 (2020).","journal-title":"arXiv preprint arXiv:2006.05987"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2011.6163888"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.506"},{"key":"e_1_3_2_125_2","first-page":"4998","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201915)","author":"Zhu Shizhan","year":"2015","unstructured":"Shizhan Zhu, Cheng Li, Chen Change Loy, and Xiaoou Tang. 2015. Face alignment by coarse-to-fine shape searching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201915). 4998\u20135006."}],"container-title":["ACM Transactions on Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3614438","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3614438","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:30Z","timestamp":1750287030000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3614438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,16]]},"references-count":124,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2,29]]}},"alternative-id":["10.1145\/3614438"],"URL":"https:\/\/doi.org\/10.1145\/3614438","relation":{},"ISSN":["2691-1914","2577-6207"],"issn-type":[{"value":"2691-1914","type":"print"},{"value":"2577-6207","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,16]]},"assertion":[{"value":"2022-10-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-24","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}