Relevant bibliographies by topics / Audio sensor based detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Audio sensor based detection'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Audio sensor based detection"

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Ding, Yiwei, Chaeyeon Han, Pavan Seshadri, et al. "Toward audio-based sensing for pedestrian detection." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A282. http://dx.doi.org/10.1121/10.0027509.

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The detection and counting of pedestrians plays a central role for the design of smart cities. Although the use of cameras for this task has been shown to have high accuracy, they come at a high cost and are susceptible to challenges such as poor lighting, fog, and obstructed views. Our study investigates audio-based pedestrian detection, combining potentially low cost sensors with advanced machine learning based audio analysis algorithms. With an audio sensor installed along the walkway, machine learning algorithms can tell from the audio whether there is a pedestrian or not, or how far the p
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Smeaton, Alan F., and Michael McHugh. "Event detection in an audio-based sensor network." Multimedia Systems 12, no. 3 (2006): 179–94. http://dx.doi.org/10.1007/s00530-006-0060-y.

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B Priyanka. "Enhanced CNN and SVM with Adaptive Modality Switching and Audio-Based Video Summarization for Real-Time Agricultural Intrusion Detection." Journal of Information Systems Engineering and Management 10, no. 33s (2025): 880–96. https://doi.org/10.52783/jisem.v10i33s.5668.

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Smart intrusion detection in agriculture involves the use of IoT, AI, and sensor-based technologies to monitor fields for unauthorized human and animal activity. Advanced AI models enhance detection accuracy, reducing false alarms and improving response efficiency. The integration of edge computing and cloud-based analytics ensures rapid data processing, making intrusion detection systems more effective and reliable in modern agricultural security. Traditional security systems rely on either video-only or audio-only detection, and struggle in low-light conditions due to the absence of adaptive
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Banerjee, Debalina, Akashjyoti Banik, Sanjib Kumar Singh, and Kandarpa Kumar Sarma. "IoT platform based Smart Assistant for Surveillance." WSEAS TRANSACTIONS ON COMPUTERS 20 (August 10, 2021): 176–81. http://dx.doi.org/10.37394/23205.2021.20.18.

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Surveillance operations designed to be carried out by a robotic vehicle for entry into an area of higher risks and perform hazardous tasks form the core of this work. The system is integrated with a robotic vehicle that is controlled through a virtual interface and well supported by live video streaming. Here, the motion detection sensor is used as a simple but powerful human presence detector and alarm trigger. Also, the design has a metal detector and gas detecting sensor that can provide precaution against potential landmines present in the operations area and presence of chemicals, high en
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Banlawe, Ivane Ann P., and Jennifer C. dela Cruz. "Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor." Micromachines 14, no. 11 (2023): 1979. http://dx.doi.org/10.3390/mi14111979.

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The mango pulp weevil (MPW) is an aggressive pest that mates seasonally according to the cycle of the mango fruit. After discovering the existence of the mango pulp weevil in Palawan, the island has been under quarantine for exporting mangoes. Detection of the pest proves difficult as the pest does not leave a physical sign that the mango has been damaged. Infested mangoes are wasted as they cannot be sold due to damage. This study serves as a base study for non-invasive mango pulp weevil detection using MATLAB machine learning and audio feature extraction tools. Acoustic sensors were evaluate
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Yufei, Wu, Wang Dandan, and Zhu Yanwei. "Research on the Advantages of Digital Sensor Equipment in Language Audio-Visual and Oral Teaching." Journal of Sensors 2021 (November 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/3006397.

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Digital sensors use biotechnology and information processing technology to strengthen the processing of relevant visual and auditory information, which is helpful to ensure that the receiver can obtain more accurate information, so as to improve the learning effect and reduce the impact on the environment. This paper designs an experiment to explore the role of digital sensors in language audio-visual teaching, which provides a reference for the application of digital sensors in the future. The impulse response function in sensor technology is introduced. The speech time domain envelope and ti
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Zhang, Zhenwei, Shenming Zhang, Dong Ni, et al. "Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data." Sensors 24, no. 12 (2024): 3714. http://dx.doi.org/10.3390/s24123714.

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Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehen
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Wang, Chaoyi, Yaozhe Song, Haolong Liu, et al. "Real-Time Vehicle Sound Detection System Based on Depthwise Separable Convolution Neural Network and Spectrogram Augmentation." Remote Sensing 14, no. 19 (2022): 4848. http://dx.doi.org/10.3390/rs14194848.

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This paper proposes a lightweight model combined with data augmentation for vehicle detection in an intelligent sensor system. Vehicle detection can be considered as a binary classification problem, vehicle or non-vehicle. Deep neural networks have shown high accuracy in audio classification, and convolution neural networks are widely used for audio feature extraction and audio classification. However, the performance of deep neural networks is highly dependent on the availability of large quantities of training data. Recordings such as tracked vehicles are limited, and data augmentation techn
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Zhou, Yurui, and Guolong Zhao. "English Pronunciation Calibration Model Based on Multimodal Acoustic Sensor." Journal of Sensors 2022 (April 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/2208653.

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In recent years, with the increasing frequency of international exchanges, people have gradually realized that language is a tool of communication and communication, and language learning should attach importance to oral teaching. However, in traditional classrooms, one of the problems faced by oral teaching is the mismatch of the teacher-student ratio: a teacher has to deal with dozens of students, one-on-one oral teaching and pronunciation guidance is impossible, and it is also affected by the teachers and the environment constraints. Therefore, the research on how to efficiently automate pr
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Shen, Min, and Zhiling Tang. "Audio Signal and Troubleshooting System Based on Wireless Sensor." International Journal of Online Engineering (iJOE) 14, no. 06 (2018): 113. http://dx.doi.org/10.3991/ijoe.v14i06.8702.

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<p class="0abstract"><span lang="EN-US">To explore</span><span lang="EN-US"> the application of audio signal in the troubleshooting system, a sound detection system </span><span lang="EN-US">wa</span><span lang="EN-US">s designed. The system consists of three parts: voice acquisition node, aggregation node and host computer monitoring software. Time protocol synchronization </span><span lang="EN-US">(</span><span lang="EN-US">TPSN</span><span lang="EN-US">)</span><span lang="EN-US"> algorithm </s
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Dissertations / Theses on the topic "Audio sensor based detection"

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Dalton, Benjamin Christopher. "Audio-based localisation for ubiquitous sensor networks." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34103.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.<br>Includes bibliographical references (p. 97-101).<br>This research presents novel techniques for acoustic-source location for both actively triggered, and passively detected signals using pervasive, distributed networks of devices, and investigates the combination of existing resources available in personal electronics to build a digital sensing 'commons'. By connecting personal resources with those of the people nearby, tasks can be achieved, through distribut
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Sun, Kailiang. "Fluorescence based optical sensor for protein detection." Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2010r/ksun.pdf.

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Menglei, Min. "Anomaly detection based on multiple streaming sensor data." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36275.

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Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the appli
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Sun, Yang. "Intelligent wireless sensor network based vehicle detection and classification /." Full text available from ProQuest UM Digital Dissertations, 2007. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=1&did=1414125751&SrchMode=1&sid=3&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1219779610&clientId=22256.

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Sikdar, Ankita. "Depth based Sensor Fusion in Object Detection and Tracking." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1515075130647622.

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Bui, Manh-Tuan. "Vision-based multi-sensor people detection system for heavy machines." Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2156/document.

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Ce travail de thèse a été réalisé dans le cadre de la coopération entre l’Université de Technologie de Compiègne (UTC) et le Centre Technique des Industries Mécaniques (CETIM). Nous présentons un système de détection de personnes pour l’aide à la conduite dans les engins de chantier. Une partie du travail a été dédiée à l’analyse du contexte de l’application, ce qui a permis de proposer un système de perception composé d’une caméra monoculaire fisheye et d’un Lidar. L’utilisation des caméras fisheye donne l’avantage d’un champ de vision très large avec en contrepartie, la nécessité de gérer le
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Lakshmanan, Ramji S. Chin Bryan Allen. "Phage-based magnetoelastic sensor for the detection of Salmonella typhimurium." Auburn, Ala, 2008. http://hdl.handle.net/10415/1483.

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JANJUA, ZAFFAR HAIDER. "SENSOR-BASED BEHAVIORAL MONITORING FOR EARLY DETECTION OF COGNITIVE DECLINE." Doctoral thesis, Università degli Studi di Milano, 2016. http://hdl.handle.net/2434/391782.

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The advancement in medical science has increased life expectancy in developed countries, which results in a rise in the elderly population. Consequently, age related health issues are also increasing and aging societies are continuously searching for new technologies to provide better healthcare services to the elderly population. One of the prominent health issues relates to the elderly population is cognitive impairment. The decline in cognitive health does not only affect day to day matters of the individual, but also creates difficulties for clinicians and caregivers in terms of providing
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Liu, Congrui. "Wearable Fall Detection using Barometric Pressure Sensor." Thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29968.

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Wearable wireless sensor devices, which are implemented by deploying sensor nodes on objects, are widely utilized in a broad field of applica-tions, especially in the healthcare system for improving the quality of life or monitoring different types of physical data from the observed objects. The aim of this study is to design an in-home, small-size and long-term wearable fall detection system in wireless network by using barometric pressure sensing for elderly or patient who needs healthcare monitoring. This threshold-based fall detection system is to measure the altitude of different position
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Willcox, Simon Ware. "Robust sensor fault diagnosis for aircraft based on analytical redundancy." Thesis, University of York, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329855.

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Books on the topic "Audio sensor based detection"

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7467-7.

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INTEGRATED CMOS-BASED BIOCHEMICAL SENSOR MICROSYSTEMS: Oxygen and Bacteria Detection. VDM Verlag Dr. Müller, 2011.

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer, 2019.

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer, 2018.

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On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification. Independently Published, 2019.

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Toxic Gas Sensors and Biosensors. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901175.

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The book focuses on novel sensor materials and their environmental and healthcare applications, such as NO2 detection, toxic gas and biosensing, hydrazine determination, glucose sensing and the detection of toxins and pollutants on surfaces. Materials covered include catalytic nanomaterials, metal oxides, perovskites, zeolites, spinels, graphene-based gas sensors, CNT/Ni nanocomposites, glucose biosensors, single and multi-layered stacked MXenes, black phosphorus, transition metal dichalcogenides and P3OT thin films.
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Spencer, Amy, ed. Ambient Stories in Practice and Research. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350234161.

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From a range of academic and practice-led perspectives, this book explores how a combination of place-based writing and location-based technologies are producing new kinds of experimental ambient literary experience.In so doing, it unpacks how situated literary experiences delivered through text, audio and sensor-based delivery offer distinctive new forms of reading and listening and lay the ground for a new poetics of situated writing practices. Exploring an experimental, practice-based approach to digital literary forms and its emerging poetics, this book critically examines the ecology of a
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Book chapters on the topic "Audio sensor based detection"

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Mendoza, Jose Marie, Vanessa Tan, Vivencio Fuentes, Gabriel Perez, and Nestor Michael Tiglao. "Audio Event Detection Using Wireless Sensor Networks Based on Deep Learning." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06158-6_11.

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Dosbayev, Zhandos, Rustam Abdrakhmanov, Oxana Akhmetova, et al. "Audio Surveillance: Detection of Audio-Based Emergency Situations." In Advances in Computational Collective Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88113-9_33.

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Baillie, Mark, and Joemon M. Jose. "Audio-Based Event Detection for Sports Video." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45113-7_30.

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Tao, Biaoli, Rangding Wang, Diqun Yan, Chao Jin, Yanan Chen, and Li Zhang. "Audio Tampering Detection Based on Quantization Artifacts." In Cloud Computing and Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48674-1_38.

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Golovnin, Oleg, Artem Privalov, Anastasiya Stolbova, and Anton Ivaschenko. "Audio-Based Vehicle Detection Implementing Artificial Intelligence." In Recent Research in Control Engineering and Decision Making. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65283-8_51.

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Liu, Tong, Liang Ye, Tian Han, Tapio Seppänen, and Esko Alasaarela. "Campus Bullying Detection Algorithm Based on Audio." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_57.

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Fox, Dieter, Wolfram Burgard, and Sebastian Thrun. "Markov Localization for Reliable Robot Navigation and People Detection." In Sensor Based Intelligent Robots. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10705474_1.

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Lenka, Archita, Bandita Panda, Chinmaya Kumar Sahu, Narayan Panda, and Sandip Kumar Dash. "Optical Sensor-Based Hydrogen Gas Detection." In Sensors for Stretchable Electronics in Nanotechnology. CRC Press, 2021. http://dx.doi.org/10.1201/9781003123781-8.

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Dieckmann, U., P. Plankensteiner, R. Schamburger, B. Fröba, and S. Meller. "SESAM: A biometric person identification system using sensor fusion." In Audio- and Video-based Biometric Person Authentication. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0016009.

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Tankus, Ariel, Hezy Yeshurun, and Nathan Intrator. "Face detection by direct convexity estimation." In Audio- and Video-based Biometric Person Authentication. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0015978.

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Conference papers on the topic "Audio sensor based detection"

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Wang, Ling, and Li Shi. "Audio detection based on segmented spectrograms." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT). IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872568.

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Xie, Yuankun, Chenxu Xiong, Xiaopeng Wang, et al. "Does Current Deepfake Audio Detection Model Effectively Detect ALM-Based Deepfake Audio?" In 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024. https://doi.org/10.1109/iscslp63861.2024.10800375.

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Karna, Siwani, Samudrala Sai Santhoshi Haneesha, Poluru Reddy Jahanve, Peeta Basa Pati, and Roshni M. Balakrishnan. "AudioGuard: Deep Learning Based Telugu DeepFake Audio Detection." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725692.

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Swathi, S., and B. H. Manjunatha Kumar. "A Review On Audio-Based Suspicious Activity Detection." In 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2025. https://doi.org/10.1109/ickecs65700.2025.11035870.

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Vieira, J., M. Coelho, L. Santiago, et al. "Forest Fire Detection and Audio Alarming System." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jtu4a.35.

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In this work, we present an optimized forest fires detection distributed temperature sensor that uses existing telecommunications fiber networks, integrated with an audio alert radio broadcasting system to warn car drivers near the fire zone.
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K.Anusha, K.Karthika, R.Praveen, S.Madhan, S. Mathesh Kumar, and G. Pragatheesh. "Image and Audio Based Emotion Detection using Deep Learning." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012153.

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Pfeifer, Rachel, Sudip Vhaduri, and James Eric Dietz. "Mitigating Sex Bias in Audio Data-Driven COPD and COVID-19 Breathing Pattern Detection Models." In 2024 IEEE 20th International Conference on Body Sensor Networks (BSN). IEEE, 2024. https://doi.org/10.1109/bsn63547.2024.10780524.

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Zajdel, W., J. D. Krijnders, T. Andringa, and D. M. Gavrila. "CASSANDRA: audio-video sensor fusion for aggression detection." In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance. IEEE, 2007. http://dx.doi.org/10.1109/avss.2007.4425310.

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Smeaton, Alan F., and Mike McHugh. "Towards event detection in an audio-based sensor network." In the third ACM international workshop. ACM Press, 2005. http://dx.doi.org/10.1145/1099396.1099414.

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Nguyen, Quang, Sang-Seok Yun, and JongSuk Choi. "Detection of audio-based emergency situations using perception sensor network." In 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2016. http://dx.doi.org/10.1109/urai.2016.7734109.

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Reports on the topic "Audio sensor based detection"

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Kumar, Praveen. PR753-233900-R01 Enhanced Leak Detection Using Minimally Invasive Multi-Sensor Device Based Inspection. Pipeline Research Council International, Inc. (PRCI), 2024. http://dx.doi.org/10.55274/r0000078.

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The project team investigated the feasibility of identifying pipeline leaks using Novel sensing approaches that have been recently gaining popularity in the "Pipeline Integrity assessment" realm (such as multi-Sensor inline inspection tools) that incorporate sensors such as Audio, Magnetometry, Pressure etc. The flow loop setup at the PRCI TDC site was leveraged to create a customized test setup and a test execution methodology was developed and executed towards this end. Two Sensing equipment vendors (hereinafter referred to as Vendor A and Vendor B) were used to collect various sensor datase
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from t
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Griffin, Stephen M., and L. L. Helms. Sub-Audio Magnetics: Miniature Sensor Technology for Simultaneous Magnetic and Electromagnetic Detection of UXO. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada578948.

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Kang, Kyung. MicroCantilever (MC) based nanomechanical sensor for detection of molecular interactions. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1037743.

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Cooper, Robert Lee, Peter Marleau, and Patrick J. Griffin. Ground water and snow sensor based on directional detection of cosmogenic neutrons. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1177063.

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Loui, A., S. McCall, and J. Zumstein. Research and Development of Non-Spectroscopic MEMS-Based Sensor Arrays for Targeted Gas Detection. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1059450.

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Loui, A., and S. McCall. Research and Development of Non-Spectroscopic MEMS-Based Sensor Arrays for Targeted Gas Detection. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1035279.

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Lavely, Eugene. Sensor Phenomenology and Feature Development for Improved Sonar-based Detection & Classification of Underwater UXO. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada520643.

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Li, Zhenning, and Bo Shen. Compressor is A Sensor: A Universal Refrigerant Charge Fault Detection and Diagnostics Method Based on Pump Down Operation. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1885304.

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Brent Marquis. A Sensor System Based on Semi-Conductor Metal Oxide Technology for In Situ Detection of Coal Fired Combustion Gases. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/944414.

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