Relevant bibliographies by topics / Smart hearing aid

Academic literature on the topic 'Smart hearing aid'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Smart hearing aid"

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Jarng. "Acoustic Wireless Communication from Smart Phone to Hearing Aid." Journal of the Acoustical Society of Korea 34, no. 2 (2015): 091. http://dx.doi.org/10.7776/ask.2015.34.2.091.

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Nossier, Soha A., M. R. M. Rizk, Nancy Diaa Moussa, and Saleh el Shehaby. "Enhanced smart hearing aid using deep neural networks." Alexandria Engineering Journal 58, no. 2 (June 2019): 539–50. http://dx.doi.org/10.1016/j.aej.2019.05.006.

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Li, Yingdan, Fei Chen, Zhuoyi Sun, Zhaoyang Weng, Xian Tang, Hanjun Jiang, and Zhihua Wang. "A Smart Binaural Hearing Aid Architecture Based on a Mobile Computing Platform." Electronics 8, no. 7 (July 20, 2019): 811. http://dx.doi.org/10.3390/electronics8070811.

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This paper presents a new structure for hearing aids. Normally, the power consumption and user experience are contradictory. The proposed hearing aid structure mainly consists of three parts: the earpieces, the mobile computing platform, and the real-time speech-enhancement application. It can run complex algorithms without carrying out heavy calculations on the processors in the hearing aid. Thus, the binaural algorithm is utilized without being limited by complexity and power consumption to improve the user experience. Moreover, the speech-enhancement algorithm can be updated much more easily than in traditional built-in digital signal process hearing aids. A good level of user experience is achieved by combining the hearing aid and mobile computing platform with a 400-MHz transceiver; furthermore, the 400-MHz transceiver can reduce path loss around the body. The concept verification process showed that the overall usage of the central processing unit in the smartphone is around 16%, the signal-to-noise ratios show at least a 30% improvement in some environments, and the whole system delay is 8.8 ms. The presented objective and subjective results show significant improvements regarding user experience and usability brought about by the proposed structure.
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Sudharsan, Bharath, and Manigandan Chockalingam. "A Microphone Array and Voice Algorithm based Smart Hearing Aid." International Journal of Computer Applications 178, no. 41 (August 19, 2019): 1–6. http://dx.doi.org/10.5120/ijca2019919295.

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McGreal, Rory. "Hearables: eLearning in the Workplace." EDEN Conference Proceedings, no. 1 (June 16, 2019): 97–102. http://dx.doi.org/10.38069/edenconf-2019-ac-0012.

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Hearables, a term first coined by Hunn (2014), are wireless smart micro-computers with artificial intelligence that incorporate both speakers and microphones. They fit in the ears and can connect to the internet and to other devices; they are designed to be worn daily. These devices, such as the Bragi Dash, Vinci and Bose Hearphone are now appearing on the market, which is expected to exceed $40 billion in the USA by 2020 (Omnicom, 2018). Hearables are not headphones, nor hearing aids, nor ear plugs, although they could take on the affordances of any of these devices (Banks, 2018). Headphones are designed for listening to music. Hearing aids are designed as an aid for the hearing impaired. Ear plugs reduce unwanted sounds by cancelling noise. Hearables offer comparable features and additionally provide users with a microphone and connectivity to the internet supporting telephony and personal digital assistant (PDA) services (Computational Thinkers, n.d.). Prior to 2017, in the USA, such devices required the approval of the Food and Drug Administration. This approval is no longer required for hearables, as they are no longer considered to be medical hearing aids (Over the Counter Hearing Aid Act, 2017). This paves the way for the expansion in the market of significantly lower-priced hearables, undercutting the expensively-priced hearing aid market.
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Li, Yingdan, Fei Chen, Zhuoyi Sun, Junyu Ji, Wen Jia, and Zhihua Wang. "A Smart Binaural Hearing Aid Architecture Leveraging a Smartphone APP With Deep-Learning Speech Enhancement." IEEE Access 8 (2020): 56798–810. http://dx.doi.org/10.1109/access.2020.2982212.

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Mehrotra, Tushar, Neha Shukla, Tarunika Chaudhary, Gaurav Kumar Rajput, Majid Altuwairiqi, and Mohd Asif Shah. "Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering." Mathematical Problems in Engineering 2022 (May 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/1182608.

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Labeling speech signals is a critical activity that cannot be overlooked in any of the early phases of designing a system based on speech technology. For this, an efficient particle swarm optimization (PSO)-based clustering algorithm is proposed to classify the speech classes, i.e., voiced, unvoiced, and silence. A sample of 10 signal waves is selected, and their audio features are extracted. The audio signals are then partitioned into frames, and each frame is classified by using the proposed PSO-based clustering algorithm. The performance of the proposed algorithm is evaluated using various performance metrics such as accuracy, sensitivity, and specificity that are examined. Extensive experiments reveal that the proposed algorithm outperforms the competitive algorithms. The average accuracy of the proposed algorithm is 97%, sensitivity is 98%, and specificity is 96%, which depicts that the proposed approach is efficient in detecting and classifying the speech classes.
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Gutenberg, Johanna, Panagiotis Katrakazas, Lyubov Trenkova, Louisa Murdin, Dario Brdarić, Nina Koloutsou, Katherine Ploumidou, Niels Henrik Pontoppidan, and Ariane Laplante-Lévesque. "Big Data for Sound Policies: Toward Evidence-Informed Hearing Health Policies." American Journal of Audiology 27, no. 3S (November 19, 2018): 493–502. http://dx.doi.org/10.1044/2018_aja-imia3-18-0003.

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Purpose The scarcity of health care resources calls for their rational allocation, including within hearing health care. Policies define the course of action to reach specific goals such as optimal hearing health. The process of policy making can be divided into 4 steps: (a) problem identification and issue recognition, (b) policy formulation, (c) policy implementation, and (d) policy evaluation. Data and evidence, especially Big Data, can inform each of the steps of this process. Big Data can inform the macrolevel (policies that determine the general goals and actions), mesolevel (specific services and guidelines in organizations), and microlevel (clinical care) of hearing health care services. The research project EVOTION applies Big Data collection and analysis to form an evidence base for future hearing health care policies. Method The EVOTION research project collects heterogeneous data both from retrospective and prospective cohorts (clinical validation) of people with hearing impairment. Retrospective data from clinical repositories in the United Kingdom and Denmark will be combined. As part of a clinical validation, over 1,000 people with hearing impairment will receive smart EVOTION hearing aids and a mobile phone application from clinics located in the United Kingdom and Greece. These clients will also complete a battery of assessments, and a subsample will also receive a smartwatch including biosensors. Big Data analytics will identify associations between client characteristics, context, and hearing aid outcomes. Results The evidence EVOTION will generate is relevant especially for the first 2 steps of the policy-making process, namely, problem identification and issue recognition, as well as policy formulation. EVOTION will inform microlevel, mesolevel, and macrolevel of hearing health care services through evidence-informed policies, clinical guidelines, and clinical care. Conclusion In the future, Big Data can inform all steps of the hearing health policy-making process and all levels of hearing health care services.
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Abdulhamid, Mohanad, and Ndiwa Chesebe. "On Sign Language Toolbox Aid." Land Forces Academy Review 25, no. 1 (March 1, 2020): 47–60. http://dx.doi.org/10.2478/raft-2020-0007.

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AbstractThe world has been lately witnessing a landmark revolution in technology whereby more research and development is going into producing devices that are able to respond to human emotions, motions and behavior. These days, mobile phones are able to capture photos when the user is smiling or gesturing towards them. By making certain gestures, a user is able to control smart televisions and computers at the comfort of their homes, without the need of extra interface devices. The interaction between man and machines is being improved and made as natural as possible. More so, the application of hand gesture technology is finding use in making communication between people with hearing difficulties and those without, possible. This research presents the techniques that have made this possible. Most of these techniques however are in development stages. This is because of the significant challenges faced in providing this solution. First, there are over 30,000 sign language gestures which is quite a headache when trying to integrate them into a recognition system. Secondly, despite the fact that there are standard ways of making the sign language gestures, people have variations to them. Developers hence have to make allowance for these variations and in effect compromise on the accuracy of the system. This research evaluates the various algorithms developed, and tries to propose an algorithm that strikes the best balance between accuracy and allowance for variations in the sign language gestures.
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Lavy, Jeremy, and Fiona McClenaghan. "Stapes Surgery in Patients with a Small Air-Bone Gap." Ear, Nose & Throat Journal 97, no. 7 (July 2018): 198–212. http://dx.doi.org/10.1177/014556131809700709.

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The objective of this study was to determine hearing outcomes in patients undergoing stapes surgery with a preoperative air-bone gap (ABG) <21.25 dB. Patients with a unilateral or bilateral preoperative ABG <21.25 dB undergoing primary stapes surgery were identified from a database of all stapes surgeries performed in a tertiary center over 15 years. A total of 254 ears met the inclusion criteria. The primary outcome measure was the degree of closure of the preoperative ABG. A secondary outcome measure was improvement in bone-conduction thresholds at 4 kHz. All patients underwent stapes surgery under local anesthesia. Ossicular reconstruction was achieved using a SMart 360 nitinol fluoroplastic piston (Gyrus ACMI, Inc.; South-borough, Mass.), and complete posterior crurotomy was performed with a KTP laser. Hearing was assessed with clinical voice testing immediately postoperatively and with pure-tone audiometry at 6 weeks postoperatively. A total of 248 ears (97.6%) demonstrated ABG closure to <10 dB. Bone-conduction thresholds showed an increase in 114 (44.9%), no change 74 (29.1%), and a decrease in 66 (26.0%). There is a slight increase in the risk of stapes mobilization in ears with a small ABG when compared to those with larger ABGs; however, this can be overcome by using a laser-assisted technique in combination with good surgical experience. The benefit in terms of hearing aid avoidance and the restoration of symmetrical hearing is both achievable and significant for the patient.
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Dissertations / Theses on the topic "Smart hearing aid"

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Amanda, Tannor Ophelia. "Hearing aid combined with virtual reality function." Thesis, Boston, USA, 2020. http://openarchive.nure.ua/handle/document/11840.

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In this paper the structure scheme of a smart hearing aid with augmented reality was proposed. This scheme consists of 6 blocks: sensor (microphone), ADC, microprocessor or microcontroller, battery, Bluetooth module, speaker. Such a device should not only improve hearing but also increase the psychological confidence of a person. The next stage of work is the development of an electrical circuit diagram of the device and the assembly of the prototype
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Books on the topic "Smart hearing aid"

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New Jersey. Legislature. Senate. Environment Committee. Public hearing before Senate Environment Committee: Testimony regarding issues of smart growth. Trenton, N.J. (PO Box 068, Trenton 08625-0068): Office of Legislative Services, Public Information Office, Hearing Unit, 2002.

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United States. Congress. Senate. Committee on Finance. Nomination of S. Bruce Smart, Jr.: Hearing before the Committee on Finance, United States Senate, Ninety-ninth Congress, first session, on the nomination of S. Bruce Smart, Jr., to be Under Secretary of Commerce for International Trade, June 26, 1985. Washington: U.S. G.P.O., 1985.

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Finance, United States Congress Senate Committee on. Nomination of S. Bruce Smart, Jr.: Hearing before the Committee on Finance, United States Senate, Ninety-ninth Congress, first session, on the nomination of S. Bruce Smart, Jr., to be Under Secretary of Commerce for International Trade, June 26, 1985. Washington: U.S. G.P.O., 1985.

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Finance, United States Congress Senate Committee on. Nomination of S. Bruce Smart, Jr.: Hearing before the Committee on Finance, United States Senate, Ninety-ninth Congress, first session, on the nomination of S. Bruce Smart, Jr., to be Under Secretary of Commerce for International Trade, June 26, 1985. Washington: U.S. G.P.O., 1985.

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New York (State). Legislature. Senate. Standing Committee on Local Government. In the matter of a public hearing on Smart Growth Economic Competitiveness Act of 1999: Before the New York State Senate Standing Committee on Local Government. [New York?]: Candyco Transcription Service, 1999.

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New York (State). Legislature. Senate. Standing Committee on Local Government. In the matter of a public hearing on the Smart Growth Economic Competitiveness Act of 1999: Before the New York State Senate Standing Committee on Local Government. [Clifton Park, N.Y.]: Candyco Transcription Service, 1999.

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United States. Congress. Senate. Committee on Banking, Housing, and Urban Affairs. Nomination of S. Bruce Smart, Jr.: Hearing before the Committee on Banking, Housing, and Urban Affairs, United States Senate, Ninety-ninth Congress, first session, on the nomination of S. Bruce Smart, Jr., to be Under Secretary of Commerce for International Trade, July 11, 1985. Washington: U.S. G.P.O., 1985.

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United, States Congress Senate Committee on Banking Housing and Urban Affairs. Nomination of S. Bruce Smart, Jr.: Hearing before the Committee on Banking, Housing, and Urban Affairs, United States Senate, Ninety-ninth Congress, first session, on the nomination of S. Bruce Smart, Jr., to be Under Secretary of Commerce for International Trade, July 11, 1985. Washington: U.S. G.P.O., 1985.

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United, States Congress Senate Committee on Banking Housing and Urban Affairs. Nomination of S. Bruce Smart, Jr.: Hearing before the Committee on Banking, Housing, and Urban Affairs, United States Senate, Ninety-ninth Congress, first session, on the nomination of S. Bruce Smart, Jr., to be Under Secretary of Commerce for International Trade, July 11, 1985. Washington: U.S. G.P.O., 1985.

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Resources, United States Congress Senate Committee on Labor and Human. Smart start: The Community Collaborative for Early Childhood Development Act of 1988 : hearings before the Committee on Labor and Human Resources, United States Senate, One Hundredth Congress, second session on S. 2270 ... February 23, May 11, and June 10, 1988. Washington: U.S. G.P.O., 1988.

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Book chapters on the topic "Smart hearing aid"

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Chen, Wei-Zen, Shih-Hao Huang, and Jhong-Ting Jian. "OEICs for High-Speed Data Links and Tympanic Membrane Transducer of Hearing Aid Device." In Smart Sensors at the IoT Frontier, 127–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55345-0_6.

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Sinha, Ishaan, and Owen Caverly. "EyeHear: Smart Glasses for the Hearing Impaired." In HCI International 2020 – Late Breaking Papers: Universal Access and Inclusive Design, 358–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60149-2_28.

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Bakken, Jeffrey P., Prasanthi Putta, and Vladimir L. Uskov. "Smart Universities: Assistive Technologies for Students with Hearing Impairments." In Smart Education and e-Learning 2021, 487–503. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2834-4_41.

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Bujas, Goran, Luka Bonetti, Zeljka Car, and Marin Vukovic. "Voice Controlled Quiz for People with Hearing Impairment." In Smart Objects and Technologies for Social Good, 166–75. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61949-1_18.

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Bakke, Matthew H. "Assistive Devices for People with Hearing Loss." In The Engineering Handbook of Smart Technology for Aging, Disability, and Independence, 191–202. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470379424.ch10.

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Karia, Deval, Rohit S. Nambiar, Agniwesh Pratap Maurya, A. Ramesh, and Manish Arora. "Application of Waterfall Design Process in Designing of a Holistic System for Children with Hearing Impairment in Resource-Constrained Settings." In Smart Innovation, Systems and Technologies, 929–40. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5974-3_80.

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Berger, Ales, and Filip Maly. "Prototype of a Smart Google Glass Solution for Deaf (and Hearing Impaired) People." In Mobile Web and Intelligent Information Systems, 38–47. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97163-6_4.

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vom Stein, Antti Matthias, and Willibald A. Günthner. "Using Smart Glasses for the Inclusion of Hearing-Impaired Warehouse Workers into Their Working Environment." In HCI in Business, Government, and Organizations: Information Systems, 358–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39399-5_34.

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Tannous, Wadad Kathy, and Laney McGrew. "Removing the Constraints of Disability." In Advances in Human and Social Aspects of Technology, 205–19. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6772-2.ch013.

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One billion people globally live with disabilities that are physical, sensory, psychiatric, neurological, cognitive, or intellectual. Their disabilities are dynamic and can be temporary or permanent, singular or plural, from birth or developed, and can change over time. People with disabilities face barriers to economic, social, political, and cultural participation. Assistive technology, artificial intelligence, and broader technology can amplify their inclusion, participation, and independence. This chapter will highlight emerging and evolving technologies, rooted in machine learning and neural networks, which assist across different disabilities and seek to improve the user's sense of ability and independence. These include Seeing AI app, OXSIGHT, OrCam, Envision smart glasses, and Dot Watch for vision impairment; Ava app and cognitive hearing aid for hearing impairment; Liftware self-stabilising utensils for limited hand mobility; Eyegaze and Tobii – assistive technologies that allow users to control computer and smartphone screens with their eyes; and 3D printed prosthetics.
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Golda Jeyasheeli, P., and N. Indumathi. "Deep Learning Based Indian Sign Language Words Identification System." In Recent Trends in Intensive Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210272.

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In Indian Population there is about 1 percent of the people are deaf and dumb. Deaf and dumb people use gestures to interact with each other. Ordinary humans fail to grasp the significance of gestures, which makes interaction between deaf and mute people hard. In attempt for ordinary citizens to understand the signs, an automated sign language identification system is proposed. A smart wearable hand device is designed by attaching different sensors to the gloves to perform the gestures. Each gesture has unique sensor values and those values are collected as an excel data. The characteristics of movements are extracted and categorized with the aid of a convolutional neural network (CNN). The data from the test set is identified by the CNN according to the classification. The objective of this system is to bridge the interaction gap between people who are deaf or hard of hearing and the rest of society.
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Conference papers on the topic "Smart hearing aid"

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Noymai, Anukool, Krit Janard, Sangvorn Seesutas, Tharapong Soonrach, and Pasin Israsena. "Smart Control of Hearing Aid Using EEG." In 2019 12th Biomedical Engineering International Conference (BMEiCON). IEEE, 2019. http://dx.doi.org/10.1109/bmeicon47515.2019.8990268.

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Garcia-Espinosa, E., O. Longoria-Gandara, A. Veloz-Guerrero, and G. G. Riva. "Hearing aid devices for smart cities: A survey." In 2015 IEEE First International Smart Cities Conference (ISC2). IEEE, 2015. http://dx.doi.org/10.1109/isc2.2015.7366198.

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Zhang, Peiyu. "MEMS microphone array for hearing aid application." In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009. SPIE, 2009. http://dx.doi.org/10.1117/12.815766.

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Scherer, Moritz, Kiran Menachery, and Michele Magno. "SmartAid: A Low-Power Smart Hearing Aid For Stutterers." In 2019 IEEE Sensors Applications Symposium (SAS). IEEE, 2019. http://dx.doi.org/10.1109/sas.2019.8706115.

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Rajan, Bina, B. Bhavana, K. R. Anusha, G. Kusumanjali, and G. S. Pavithra. "IoT based Smart and Efficient Hearing Aid using ARM Cortex Microcontroller." In 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE, 2020. http://dx.doi.org/10.1109/icstcee49637.2020.9277110.

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Li, Yingdan, Fei Chen, Zhuoyi Sun, Zhaoyang Weng, Xian Tang, Hanjun Jiang, and Zhihua Wang. "System architecture of a smart binaural hearing aid using a mobile computing platform." In 2017 IEEE 12th International Conference on ASIC (ASICON). IEEE, 2017. http://dx.doi.org/10.1109/asicon.2017.8252508.

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Dhanaskodi, Deepa, Poongodi Chenniappan, Shoukath Ali K, Perarasi T, and Thangavel Palaniappan. "Adaptive Threshold and Modified Adaptive Gain Function based Speech Enhancement Algorithm for Digital Hearing Aid." In 2021 Smart Technologies, Communication and Robotics (STCR). IEEE, 2021. http://dx.doi.org/10.1109/stcr51658.2021.9588809.

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Ghosh, Ria, Ram Charan Chandra Shekar, and John H. L. Hansen. "Portable Smart-Space Research Interface to Predetermine Environment Acoustics for Cochlear implant and Hearing aid users with CCi-MOBILE." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175825.

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Jarng, Soon Suck, Lingfeng Chen, and You Jung Kwon. "A CMOS single-supply logarithmic amplifier for hearing aids." In ICMIT 2005: Merchatronics, MEMS, and Smart Materials, edited by Yunlong Wei, Kil To Chong, Takayuki Takahashi, Shengping Liu, Zushu Li, Zhongwei Jiang, and Jin Young Choi. SPIE, 2005. http://dx.doi.org/10.1117/12.664247.

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Savary, E., W. Rahajandraibe, S. Meillere, E. Kussener, H. Barthelemy, J. Czarny, H. Lhermet, and P. Robert. "High resolution NEMS smart audio sensor based on resistive silicon nano wires for hearing aids." In 2014 21st IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2014. http://dx.doi.org/10.1109/icecs.2014.7050046.

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