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Relevant bibliographies by topics / Sound detection

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Academic literature on the topic 'Sound detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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Journal articles on the topic "Sound detection"

1

Muhammad Naqiuddin Zaini, Marina Yusoff, and Muhammad Amirul Sadikin. "Forest Sound Event Detection with Convolutional Recurrent Neural Network-Long Short-Term Memory." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 2 (2023): 242–54. http://dx.doi.org/10.37934/araset.32.2.242254.

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Sound event detection tackles an audio environment's complex sound analysis and recognition problem. The process involves localizing and classifying sounds mainly to estimate the start point and end points of the separate sounds and describe each sound. Sound event detection capability relies on the type of sound. Although detecting sequences of distinct temporal sounds is straightforward, the situation becomes complex when the sound is multiple overlapping of much single audio. This situation usually occurs in the forest environment. Therefore, this aim of the paper is to propose a Convolutio

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Constantin, Constantinescu, Brad Remus, and Bărglăzan Adrian. "BRAIN. Broad Research in Artificial Intelligence and Neuroscience - Lung Sounds Anomaly Detection with Respiratory Cycle Segmentation." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 3 (2024): 188–96. https://doi.org/10.70594/brain/15.3/14.

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Employing machine learning algorithms in the medical field has proven successful for some time now. Mostly computer vision techniques have been applied to medical images, while medical sound data has been somewhat overlooked. By using electronic stethoscopes, it is now possible to process both heartbeats and lung sounds. While some products are available for detecting anomalies in heartbeats, addressing lung-related anomalies presents a more intricate challenge. Applying a deep learning approach is hindered by insufficient data. Although some datasets do exist, the size and diversity of the da

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Norezmi, Jamal, Ibrahim Nabilah, Sha'abani MNAH, and Taat Zulkifli. "Detection of cardiac sounds components: a pilot study." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 3 (2020): 1330–37. https://doi.org/10.11591/ijeecs.v17.i3.pp1330-1337.

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This paper presents a preliminary study related to the detection and identification of cardiac sounds components including first sound (S1), second sound (S2) and murmurs. Detection and identification of cardiac sounds are an important process in automated cardiac sound analysis system in order to automatically diagnose people who are having cardiovascular disorder and determine the existence of murmurs. Sixteen of recorded cardiac sounds (eight normal cardiac sounds, four abnormal cardiac sounds with systole murmur, and four abnormal cardiac sounds with diastole murmur) from PASCAL Classifyin

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Keikhosrokiani, Pantea, A. Bhanupriya Naidu A/P Anathan, Suzi Iryanti Fadilah, Selvakumar Manickam, and Zuoyong Li. "Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony." DIGITAL HEALTH 9 (January 2023): 205520762211507. http://dx.doi.org/10.1177/20552076221150741.

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Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification

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Jamal, Norezmi, Nabilah Ibrahim, MNAH Sha’abani, and Zulkifli Taat. "Detection of cardiac sounds components: a pilot study." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (2020): 1330. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1330-1337.

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<span>This paper presents a preliminary study related to the detection and identification of cardiac sounds components including first sound (S1), second sound (S2) and murmurs. Detection and identification of cardiac sounds are an important process in automated cardiac sound analysis system in order to automatically diagnose people who are having cardiovascular disorder and determine the existence of murmurs. Sixteen of recorded cardiac sounds (eight normal cardiac sounds, four abnormal cardiac sounds with systole murmur, and four abnormal cardiac sounds with diastole murmur) from PASCA

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Raj, Bhiksha. "Improving sound event detection with ontologies." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A364. http://dx.doi.org/10.1121/10.0019176.

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Sound event recognition is the task of identifying and categorizing sounds in audio data. Automated algorithms for sound event recognition depend on having explicit models for individual sound event types to be recognized, which are trained on data tagged explicitly for those classes. The approach is data hungryand is fundamentally limited by the number of classes for which such data may be obtained. It also ignores the relationship between sounds being modeled. In this work, we attempt to address these deficiencies through the use of a human-generated sound ontology which represents sibling a

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Raveendran, Smitha, Jitendra Sonawan, Gajanan K. Birajdar, and Mukesh D. Patil. "Pathological Lung Sound Detection using Deep Transfer Learning Pathological Lung Sound Detection using Deep Transfer Learning." International Journal of Innovation in Multidisciplinary Scientific Research 02, no. 01 (2024): 01–07. http://dx.doi.org/10.61239/ijimsr.2024.2110.

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Lung sound analysis has gained prominence as a non-invasive method for diagnosing respiratory conditions. Recent development in deep transfer learning models have signified the potential to enhance the accuracy of lung sound detection, enabling early and accurate diagnosis. This paper presents an approach for lung sound detection using deep transfer learning techniques. A deep neural network architecture pretrained on a large external dataset and fine-tuned on a specialized lung sound dataset to leverage both general and domain-specific features. Firstly, input lung sound recordings are transf

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Zabidi, Muhammad Munim, Kah Liang Wong, Usman Ullah Sheikh, Shahidatul Sadiah Abdul Manan, and Muhammad Afiq Nurudin Hamzah. "Bird Sound Detection with Binarized Neural Networks." ELEKTRIKA- Journal of Electrical Engineering 21, no. 1 (2022): 48–53. http://dx.doi.org/10.11113/elektrika.v21n1.349.

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By analysing the behavioural patterns of bird species in a specific region, researchers can predict future changes in the ecosystem. Many birds can be identified by their sounds, and autonomous recording units (ARUs) can capture real-time bird vocalisations. The recordings are analysed to see if there are any bird sounds. The sound of a bird can be used for further analysis, such as determining its species. Bird sound detection using Deep Neural Networks (DNNs) has been shown to outperform traditional methods. DNNs, however, necessitate a lot of storage and processing power. The use of Binariz

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Zabidi, Muhammad Munim, Kah Liang Wong, Usman Ullah Sheikh, Shahidatul Sadiah Abdul Manan, and Muhammad Afiq Nurudin Hamzah. "Bird Sound Detection with Binarized Neural Networks." ELEKTRIKA- Journal of Electrical Engineering 21, no. 1 (2022): 48–53. http://dx.doi.org/10.11113/elektrika.v21n1.349.

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By analysing the behavioural patterns of bird species in a specific region, researchers can predict future changes in the ecosystem. Many birds can be identified by their sounds, and autonomous recording units (ARUs) can capture real-time bird vocalisations. The recordings are analysed to see if there are any bird sounds. The sound of a bird can be used for further analysis, such as determining its species. Bird sound detection using Deep Neural Networks (DNNs) has been shown to outperform traditional methods. DNNs, however, necessitate a lot of storage and processing power. The use of Binariz

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Kovalenko, Andriy, and Anton Poroshenko. "ANALYSIS OF THE SOUND EVENT DETECTION METHODS AND SYSTEMS." Advanced Information Systems 6, no. 1 (2022): 65–69. http://dx.doi.org/10.20998/2522-9052.2022.1.11.

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Detection and recognition of loud sounds and characteristic noises can significantly increase the level of safety and ensure timely response to various emergency situations. Audio event detection is the first step in recognizing audio signals in a continuous audio input stream. This article presents a number of problems that are associated with the development of sound event detection systems, such as the deviation for each environment and each sound category, overlapping audio events, unreliable training data, etc. Both methods for detecting monophonic impulsive audio event and polyphonic sou

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Dissertations / Theses on the topic "Sound detection"

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Vignola, Joseph F. "Laser detection of sound." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/17311.

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Olvera, Zambrano Mauricio Michel. "Robust sound event detection." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0324.

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De l'industrie aux applications d'intérêt général, l'analyse automatique des scènes et événements sonores permet d'interpréter le flux continu de sons quotidiens. Une des principales dégradations rencontrées lors du passage des conditions de laboratoire au monde réel est due au fait que les scènes sonores ne sont pas composées d'événements isolés mais de plusieurs événements simultanés. Des différences entre les conditions d'apprentissage et de test surviennent aussi souvent en raison de facteurs extrinsèques, tels que le choix du matériel d'enregistrement et des positions des microphones, et

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Li, Yun Popescu Mihail. "Fall detection using sound sensors." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6651.

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Title from PDF of title page (University of Missouri--Columbia, viewed on March 10, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. Mihail Popescu. Includes bibliographical references.

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Simpson, Paul Martin. "Estimating underwater sound levels." Thesis, University of Salford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308290.

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Roemer, Jake. "Practical High-Coverage Sound Predictive Race Detection." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563505463237874.

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Lineberger, Ernest R. "Bubble detection using a dual frequency sound field." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/22932.

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VESPERINI, FABIO. "Deep Learning for Sound Event Detection and Classification." Doctoral thesis, Università Politecnica delle Marche, 2019. http://hdl.handle.net/11566/263536.

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I recenti progressi riguardanti l’elaborazione del segnale acustico e le tecniche di machine learning hanno permesso lo sviluppo di tecnologie innovative per l’ana- lisi automatica di eventi sonori. In particolare, uno degli approcci attualmente piu` in voga in questo ambito consiste nell’impiego di tecniche di Deep Learning (DL). Tradizionalmente, tali algoritmi si basavano su tecniche di di modellazio- ne statistica come i Gaussian Mixture Models, gli Hidden Markov Models o le Support Vector Machines, ma il recente ritorno di interesse verso gli strumenti di apprendimento automatico come il

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Zwiebel, Alicia A. "Limited view sound speed imaging for breast cancer detection." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377866373.

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Khlaifi, Hajer. "Preliminary study for detection and classiﬁcation of swallowing sound". Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2485/document.

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Les maladies altérant le processus de la déglutition sont multiples, affectant la qualité de vie du patient et sa capacité de fonctionner en société. La nature exacte et la gravité des changements post/pré-traitement dépendent de la localisation de l’anomalie. Une réadaptation efficace de la déglutition, cliniquement parlant, dépend généralement de l’inclusion d’une évaluation vidéo-fluoroscopique de la déglutition du patient dans l’évaluation post-traitement des patients en risque de fausse route. La restriction de cette utilisation est due au fait qu’elle est très invasive, comme d’autres mo

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Mason, S. M. "Objective waveform detection in electric response audiometry." Thesis, University of Nottingham, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.353922.

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Books on the topic "Sound detection"

1

Lineberger, Ernest R. Bubble detection using a dual frequency sound field. Naval Postgraduate School, 1988.

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Jones, D. F. An end-capped cylindrical hydrophone for underwater sound detection. Defence Research Establishment Atlantic, 1992.

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Nevins, Francis M. The sound of detection: Ellery Queen's adventures in radio. Borgo Press, 1985.

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Nevins, Francis M. The sound of detection: Ellery Queen's adventures in radio. 2nd ed. OTR Pub., 2002.

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Neta, Beny. Benefit of sound cueing in combat simulation. Naval Postgraduate School, 1993.

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Udevitz, Mark S. Detection of sea otters in boat-based surveys of Prince William Sound, Alaska. U.S. Fish and Wildlife Service, 1995.

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Udevitz, Mark S. Detection of sea otters in boat-based surveys of Prince William Sound, Alaska. U.S. Fish and Wildlife Service, 1995.

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Udevitz, Mark S. Detection of sea otters in boat-based surveys of Prince William Sound, Alaska. U.S. Fish and Wildlife Service, 1995.

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Evers, Läslo Gerardus. The inaudible symphony: On the detection and source identification of atmospheric infrasound. Delf Univerity of Technology, 2008.

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Evers, Läslo Gerardus. The inaudible symphony: On the detection and source identification of atmospheric infrasound. Delf Univerity of Technology, 2008.

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Book chapters on the topic "Sound detection"

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Moussavi, Zahra. "Adventitious Sound Detection." In Fundamentals of Respiratory System and Sounds Analysis. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01617-2_7.

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Kapoor, B. G., and Bhavna Khanna. "Sound Production, Detection and Communication." In Ichthyology Handbook. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07844-0_16.

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Rujen, Jeffrey, Parth Sharma, Rakshit Keshri, and Purushottam Sharma. "COVID Detection Using Cough Sound." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7346-8_69.

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Rován, Ernesto, Pablo Monzón, and Facundo Benavides. "Sound-Based Parakeets Detection System." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80366-6_19.

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Oswald, Julie N., Christine Erbe, William L. Gannon, Shyam Madhusudhana, and Jeanette A. Thomas. "Detection and Classification Methods for Animal Sounds." In Exploring Animal Behavior Through Sound: Volume 1. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97540-1_8.

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AbstractClassification of the acoustic repertoires of animals into sound types is a useful tool for taxonomic studies, behavioral studies, and for documenting the occurrence of animals. Classification of acoustic repertoires enables the identification of species, age, gender, and individual identity, correlations between sound types and behavior, the identification of changes in vocal behavior over time or in response to anthropogenic noise, comparisons between the repertoires of populations living in different geographic regions and environments, and the development of software tools for auto

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Sueur, Jérôme. "Comparison and Automatic Detection." In Sound Analysis and Synthesis with R. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77647-7_17.

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Mohmmad, Sallauddin, and Suresh Kumar Sanampudi. "Detecting Logging of Forest Trees Using Sound Event Detection." In Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2746-3_23.

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Ramesh, Babu Durai C., and Ram S. Vishnu. "CNN and Sound Processing-Based Audio Classifier for Alarm Sound Detection." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0199-9_31.

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Levin, Vladimir, Robert Palmer, Shaz Qadeer, and Sriram K. Rajamani. "Sound Transaction-Based Reduction Without Cycle Detection." In Model Checking Software. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11537328_11.

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Tsuzuki, Hirofumi, Mauricio Kugler, Susumu Kuroyanagi, and Akira Iwata. "A Novel Approach for Sound Approaching Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17534-3_50.

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Conference papers on the topic "Sound detection"

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Shin, Sanghyun, Yiqing Ding, and Inseok Hwang. "Cockpit Alarm Detection and Identification Algorithm for Helicopters." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11532.

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In recent years, the National Transportation Safety Board (NTSB) has emphasized the importance of analyzing flight data such as cockpit voice recordings as an effective method to improve the safety of helicopter operations. Cockpit voice recordings contain the sounds of engines, crew conversations, alarms, switch activations, and others within a cockpit. Thus, analyzing cockpit voice recordings can contribute to identifying the causes of an accident or incident. Among various types of the sounds in cockpit voice recordings, this paper focuses on cockpit alarm sounds as an object of analysis. I

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Chen, Donger, Xu Ma, Ying He, et al. "Sound: Unknown Object Detection for Autonomous Driving." In 2025 IEEE 3rd International Conference on Mobility, Operations, Services and Technologies (MOST). IEEE, 2025. https://doi.org/10.1109/most65065.2025.00020.

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TEMME, S. "LOUDSPEAKER RUB AND BUZZ AND LOOSE PARTICLE DETECTION." In Reproduced Sound 2003. Institute of Acoustics, 2023. http://dx.doi.org/10.25144/18169.

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ANGUS, JAS. "HOWL-ROUND DETECTION AND CONTROL USING ADAPTIVE FILTERING TECHNIQUES." In Reproduced Sound 1987. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/22016.

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Mezei, Jozsef, Viktor Fiaska, and Andras Molnar. "Drone sound detection." In 2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2015. http://dx.doi.org/10.1109/cinti.2015.7382945.

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Fu, Yingwei, Kele Xu, Haibo Mi, Huaimin Wang, Dezhi Wang, and Boqing Zhu. "A Mobile Application for Sound Event Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/941.

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Sound event detection is intended to analyze and recognize the sound events in audio streams and it has widespread applications in real life. Recently, deep neural networks such as convolutional recurrent neural networks have shown state-of-the-art performance in this task. However, the previous methods were designed and implemented on devices with rich computing resources, and there are few applications on mobile devices. This paper focuses on the solution on the mobile platform for sound event detection. The architecture of the solution includes offline training and online detection. During

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VIVATVAKIN, K., and IH FLINDELL. "DETECTION OF SPEECH IN THE PRESENCE OF DELAYED SAME SIGNAL REINFORCEMENT." In Reproduced Sound 2001. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/18527.

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Shimonishi, Kanta, Kota Dohi, and Yohei Kawaguchi. "Anomalous Sound Detection Based on Sound Separation." In INTERSPEECH 2023. ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-591.

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Charles, J. A., D. Fitzgerald, and E. Coyle. "Violin sound quality detection." In IET Irish Signals and Systems Conference (ISSC 2008). IEE, 2008. http://dx.doi.org/10.1049/cp:20080676.

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Nichols, Eric, Daniel Tompkins, and Jianyu Fan. "Hierarchical Sound Event Classification." In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). New York University, 2019. http://dx.doi.org/10.33682/v0ns-1352.

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Reports on the topic "Sound detection"

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Marr, Jeffrey, Edward Walsh, JoAnn McGee, et al. Detection and Perception of Sound by Eagles and Surrogate Raptors. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1763993.

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Smyth, Christopher C. Modeling Workload for Target Detection from a Moving Vehicle With a Head-Mounted Display and Sound Localization. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada405050.

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Scales, John A., Martin Smith, and Brian Zadler. Generation of Intense Low-Frequency Collimated Sound Beams by Nonlinear Acoustics and Detection by a Millimeter-Wave Vibrometer. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada533326.

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Kamrath, Matthew, Vladimir Ostashev, D. Wilson, Michael White, Carl Hart, and Anthony Finn. Vertical and slanted sound propagation in the near-ground atmosphere : amplitude and phase fluctuations. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40680.

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Sound propagation along vertical and slanted paths through the near-ground atmosphere impacts detection and localization of low-altitude sound sources, such as small unmanned aerial vehicles, from ground-based microphone arrays. This article experimentally investigates the amplitude and phase fluctuations of acoustic signals propagating along such paths. The experiment involved nine microphones on three horizontal booms mounted at different heights to a 135-m meteorological tower at the National Wind Technology Center (Boulder, CO). A ground-based loudspeaker was placed at the base of the towe

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Scharine, Angelique A., and Tomasz R. Letowski. The Measurement of the Effects of Helmet Form on Sound Source Detection and Localization Using a Portable Four-Loudspeaker Test Array. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada588880.

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Mizrach, Amos, Michal Mazor, Amots Hetzroni, et al. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7586535.bard.

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This interdisciplinaray work combines expertise in engineering and entomology in Israel and the US, to develop an acoustic trap for mate-seeking female medflies. Medflies are among the world's most economically harmful pests, and monitoring and control efforts cost about $800 million each year in Israel and the US. Efficient traps are vitally important tools for medfly quarantine and pest management activities; they are needed for early detection, for predicting dispersal patterns and for estimating medfly abundance within infested regions. Early detection facilitates rapid response to invasio

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Tossey, Brett, and Ramgopal Thodla. PR-180-094506-R01 Challenges for Safe and Reliable On-Shore Pipeline Transport of Supercritical CO2. Pipeline Research Council International, Inc. (PRCI), 2010. http://dx.doi.org/10.55274/r0010712.

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There is interest within the pipeline industry in transporting supercritical CO2 in pipelines. A significant issue is the lack of an independent industry standard for supercritical CO2 pipelines. Existing industry standard for liquid and gas transmission are used for mechanical design requirements, but selected properties of supercritical CO2 make it a unique product. Impurities in the gas steam, materials selection, and leak detection in supercritical CO2 require special consideration. The objective of this project is to engage the supercritical CO2 industry in a workshop and use their knowle

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Iselin, Columbus O'Donnell. Summary of bathythermograph observations from the western North Atlantic : October 1940 - December 1941. Woods Hole Oceanographic Institution, 2022. http://dx.doi.org/10.1575/1912/29563.

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The range of submarine detection is frequently limited by the refraction produced by vertical temperature gradients in the superficial layers of the ocean. In order to measure these temperature gradients and thus to permit predictions of the range, the bathythermograph was developed and is now being used on a considerable number of anti-submarine vessels, while a somewhat modified version of the instrument is being tried out on submarines. Some 6675 bathythermograph observations from the western North Atlantic have been examined in order to determine how frequently such observations should be

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Mellinger, David K. Datasets of Odontocete Sounds Annotated for Developing Automatic Detection Methods. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535841.

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Smyth, Christopher C. Detecting Targets From a Moving Vehicle With a Head-Mounted Display and Sound Localization. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada402807.

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