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Journal articles on the topic 'Sound detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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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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2

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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6

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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7

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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10

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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Leung, Ada W. S., Pierre Jolicoeur, and Claude Alain. "Attentional Capacity Limits Gap Detection during Concurrent Sound Segregation." Journal of Cognitive Neuroscience 27, no. 11 (2015): 2186–96. http://dx.doi.org/10.1162/jocn_a_00849.

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Detecting a brief silent interval (i.e., a gap) is more difficult when listeners perceive two concurrent sounds rather than one in a sound containing a mistuned harmonic in otherwise in-tune harmonics. This impairment in gap detection may reflect the interaction of low-level encoding or the division of attention between two sound objects, both of which could interfere with signal detection. To distinguish between these two alternatives, we compared ERPs during active and passive listening with complex harmonic tones that could include a gap, a mistuned harmonic, both features, or neither. Duri

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Lee, Byung-Jin, Mi-Suk Lee, and Woo-Sug Jung. "Acoustic Based Fire Event Detection System in Underground Utility Tunnels." Fire 6, no. 5 (2023): 211. http://dx.doi.org/10.3390/fire6050211.

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Underground utility tunnels (UUTs) are convenient for the integrated management of various infrastructure facilities. They ensure effective control of underground facilities and reduce occupied space. However, aging UUTs require effective management and preventive measures for fire safety. The fundamental problems in operating UUTs are the frequent occurrence of mold, corrosion, and damage caused to finishing materials owing to inadequate waterproofing, dehumidification, and ventilation facilities, which result in corrosion-related electrical leakage in wiring and cables. To prevent this, an a

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Li, Suyi, Feng Li, Shijie Tang, and Wenji Xiong. "A Review of Computer-Aided Heart Sound Detection Techniques." BioMed Research International 2020 (January 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/5846191.

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Cardiovascular diseases have become one of the most prevalent threats to human health throughout the world. As a noninvasive assistant diagnostic tool, the heart sound detection techniques play an important role in the prediction of cardiovascular diseases. In this paper, the latest development of the computer-aided heart sound detection techniques over the last five years has been reviewed. There are mainly the following aspects: the theories of heart sounds and the relationship between heart sounds and cardiovascular diseases; the key technologies used in the processing and analysis of heart

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Aswin, Muhammad, I. N. G. Wardana, Yudy Surya Irawan, and Hadi Suyono. "Bearing Damage Detection Based on Sound Signal." Applied Mechanics and Materials 548-549 (April 2014): 698–702. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.698.

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This paper presents a new method in damage detection by taking the sound signals of the rolling bearings in different levels. The tested bearing was put on the end of the shaft rotated by permanent magnet synchronous motor. The sound signal produced by this rig was recorded separately for each bearing condition with the same experimental environment. The sound data signals are compared each other. Based on the cross-correlation analysis, the recorded sound signal proved that the signals were recorded with the same environment. The power spectra calculation has shown different harmonic frequenc

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Zhailau, Magzhan. "REAL-TIME SOUND ANOMALY DETECTION OF INDUSTRIALENVIRONMENTS WITH DEEP LEARNING." Suleyman Demirel University Bulletin Natural and Technical Sciences 65, no. 2 (2024): 69–86. https://doi.org/10.47344/sdubnts.v65i2.1264.

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In response to the increasing demand for enhanced industrialsafety and efficiency, this research delves into the domain of sound anomalydetection within industrial environments, leveraging the power of deep learning.Focused on addressing the limitations of traditional methods, the studyinvestigates various deep learning architectures, including convolutional neuralnetworks (CNNs), recurrent neural networks (RNNs), and hybrid models, todiscern their efficacy in detecting abnormal sounds. The survey rigorouslyevaluates datasets, preprocessing techniques, and benchmarks, providing acomprehensive

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Hsieh, Min-Chih, Hung-Jen Chen, Ming-Le Tong, and Cheng-Wu Yan. "Effect of Environmental Noise, Distance and Warning Sound on Pedestrians’ Auditory Detectability of Electric Vehicles." International Journal of Environmental Research and Public Health 18, no. 17 (2021): 9290. http://dx.doi.org/10.3390/ijerph18179290.

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With developments in science and technology, the number of electric vehicles will increase, and they will even replace ICE vehicles. Thus, perceiving the presence of approaching electric vehicles on the road has become an important issue. In this study, the auditory detectability of the electric vehicle warning sound at different volumes, distances, and environmental noise levels was investigated. To this end, the detection rate was recorded in experiments with three environmental noise levels (50, 60, and 70 dBA), two sound pressure levels (SPLs) of the warning sound (46 and 51 dBA), three fr

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Hu, Qing Song, De Hui Chen, Wei Ding Wang, and Shou Yu Zhang. "Fish Sound Frequency Domain Analysis and Acoustic Spread Distance Experiment Research." Applied Mechanics and Materials 117-119 (October 2011): 716–20. http://dx.doi.org/10.4028/www.scientific.net/amm.117-119.716.

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Fish biological sounds reflect abundant information of its living state. It is important to systematically conduct research on the fish acoustic features. This paper designs program code based on Fast Fourier Transform (FFT) to deal with the sounds in frequency domain. By applying the program to analyze the sounds of piranha, oyster toadfish etc., the feature of main frequency is obtained and the sound features of different fishes are compared. This paper further designs the sound detection experiment about the acoustic spread distance; analyzes the sound decibel value varying trend according

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Golpaygani, Ali Tavakoli, Nahid Abolpour, Kamran Hassani, Kourosh Bajelani, and D. John Doyle. "Detection and identification of S1 and S2 heart sounds using wavelet decomposition method." International Journal of Biomathematics 08, no. 06 (2015): 1550078. http://dx.doi.org/10.1142/s1793524515500783.

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Phonocardiogram (PCG), the digital recording of heart sounds is becoming increasingly popular as a primary detection system for diagnosing heart disorders and it is relatively inexpensive. Electrocardiogram (ECG) is used during the PCG in order to identify the systolic and diastolic parts manually. In this study a heart sound segmentation algorithm has been developed which separates the heart sound signal into these parts automatically. This study was carried out on 100 patients with normal and abnormal heart sounds. The algorithm uses discrete wavelet decomposition and reconstruction to produ

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Sailikith, G., G. Yashwanth, J. Pavan, and Mr V. Devasekhar. "GUN SOUND RECOGNITION USING NLP AND YAMNET MODEL." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–7. https://doi.org/10.55041/ijsrem.ncft024.

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This approach introduces a hybrid methodology for detecting gunshot sounds by combining traditional and deep learning techniques. Initially, audio signals are analyzed using Mel-Frequency Cepstral Coefficients (MFCC), which effectively capture the unique spectral features of the sound. These extracted features are then fed into a Support Vector Machine (SVM), which classifies the sounds into gunshots or other types. To boost the system's accuracy and reliability, YAMNet—a pre-trained deep neural network model designed for sound classification—is also incorporated. YAMNet categorizes the audio

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Makimoto, Yoshihiro, Yuya Nara, Syuma Hirai, Akira Mizobuchi, Yuki Oe, and Hitoshi Ogawa. "Development of an Application for Smartphone to Detect Chattering Vibration in Single-Purpose Lathe." International Journal of Automation Technology 19, no. 2 (2025): 162–72. https://doi.org/10.20965/ijat.2025.p0162.

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This paper proposes a novel chattering vibration detection application (CVDA) for smartphones. The main target machine is a single-purpose lathe. The CVDA uses sound signals between 10 kHz and 20 kHz, and acceleration sensor signals. In general, when evaluating chattering vibration detection methods using sound signals, it is necessary to consider that the operating sound of the target lathe includes environmental and other machine tool operating noise. The environmental noise includes human voices, the sound of rain, and factory broadcasts. The frequencies of these sounds are often less than

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Abadi, Shima, Tor A. Bjorklund, Junzhe Liu, and H. P. Johnson. "Detection and monitoring of seafloor methane bubbles using hydrophones." Journal of the Acoustical Society of America 152, no. 4 (2022): A290. http://dx.doi.org/10.1121/10.0016312.

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Natural marine seeps of methane are important sources of greenhouse gas emissions that enter the global environment. Monitoring marine methane seeps will reveal important information about how they form, their source regions, and how much of the inventory is microbially consumed within the water column before the gas is released to the atmosphere. While active acoustics methods have been extensively used to detect and monitor methane emissions from the seafloor, there are only a few studies showing the use of passive acoustics for bubble sound detection and monitoring. In this presentation, we

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Constantinescu, Constantin, Remus Brad, and Adrian Bărglăzan. "Lung Sounds Anomaly Detection with Respiratory Cycle Segmentation." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 3 (2024): 188. http://dx.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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Takagi, Masanori. "Fundamental study for vehicle abnormal sound detection and the direction estimation methods using machine learning with noise reduction." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A143. http://dx.doi.org/10.1121/10.0023061.

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Detecting abnormal sounds during vehicle running condition is important to find out the vehicle’s problems at the early stage. In the mass production process, the detection test is generally carried out by the inspector. However, continuous inspection by a human for long time is hard and miss false inspection may occur. In addition, normal running noise, such as road noise, deteriorates the inspection accuracy. Therefore, an automatic accurate abnormal sound detection method under the normal noise is necessary to improve the detection accuracy with small man-hour. Furthermore, if the method ha

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Sonoda, Yoshito, and Yoichi Nakazono. "Development of Optophone with No Diaphragm and Application to Sound Measurement in Jet Flow." Advances in Acoustics and Vibration 2012 (May 14, 2012): 1–17. http://dx.doi.org/10.1155/2012/909437.

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The optophone with no diaphragm, which can detect sound waves without disturbing flow of air and sound field, is presented as a novel sound measurement technique and the present status of development is reviewed in this paper. The method is principally based on the Fourier optics and the sound signal is obtained by detecting ultrasmall diffraction light generated from phase modulation by sounds. The principle and theory, which have been originally developed as a plasma diagnostic technique to measure electron density fluctuations in the nuclear fusion research, are briefly introduced. Based on

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Arslan, Merve, and Şerif Ali Sadık. "AUDIO COPY-MOVE FORGERY DETECTION WITH MACHINE LEARNING METHODS." Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 26, no. 2 (2025): 132–49. https://doi.org/10.18038/estubtda.1624909.

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Converting original sounds into fake sounds using various methods and using these sounds for fraud or misinformation purposes poses serious risks and threats. In this study, a classification system using machine learning methods is created and performance analysis is performed in order to detect sounds created with copy-move forgery, which is one of the types of sound forgery. Sound files are treated as raw data. Then, Mel-spectrograms are obtained to visually represent the spectral features of the sound over time. Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), K-Neares

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Rafiq, Arif Ainur, Sugeng Dwi Riyanto, and Hera Susanti. "Noise Detection System in The Classroom Using Sound Sensors and NodeMCU ESP6288." Journal of Electronics Technology Exploration 1, no. 1 (2023): 1–14. http://dx.doi.org/10.52465/joetex.v1i1.185.

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The educational environment is the process of educational activities, so the environment is expected to be comfortable and avoid noise. Noise can be disturbing, such as the ringing of mobile phones or sounds produced by humans. Sound noise in a room can cause loss of concentration, so that indoor activities can be disrupted. Based on these problems, it is necessary to have a tool used to detect the noise level of sound in the classroom. Therefore, a noise detection system in the learning room uses a sound sensor based on NodeMCU ESP8266, which can send email notifications and be monitored thro

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Zhang, Lu. "Design of Heart Sound Analyzer." Advanced Materials Research 1042 (October 2014): 131–34. http://dx.doi.org/10.4028/www.scientific.net/amr.1042.131.

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There is important physiological and pathological information in heart sound, so the patients’ information can be obtained by detection of their heart sounds. In the hardware of the system, the heart sound sensor HKY06B is used to acquire the heart sound signal, and the DSP chip TMS320VC5416 is used to process the heart sound. De-noising based on wavelet and HHT and other technical are used in the process of heart sound. There are five steps in the system: acquisition, de-noising, segmentation, feature extraction, and finally, heart sounds are classified

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Maunder, David, Julien Epps, Eliathamby Ambikairajah, and Branko Celler. "Robust Sounds of Activities of Daily Living Classification in Two-Channel Audio-Based Telemonitoring." International Journal of Telemedicine and Applications 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/696813.

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Despite recent advances in the area of home telemonitoring, the challenge of automatically detecting the sound signatures of activities of daily living of an elderly patient using nonintrusive and reliable methods remains. This paper investigates the classification of eight typical sounds of daily life from arbitrarily positioned two-microphone sensors under realistic noisy conditions. In particular, the role of several source separation and sound activity detection methods is considered. Evaluations on a new four-microphone database collected under four realistic noise conditions reveal that

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Vignola, Joseph F., Yves H. Berthelot, and Jacek Jarzynski. "Laser detection of sound." Journal of the Acoustical Society of America 90, no. 3 (1991): 1275–86. http://dx.doi.org/10.1121/1.401920.

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Jamison, David Thomas, and Mark J. Maritch. "Ventilation sound detection system." Journal of the Acoustical Society of America 118, no. 2 (2005): 599. http://dx.doi.org/10.1121/1.2040295.

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Seager, Clive. "Simple sound detection circuit." Electronics Education 2003, no. 3 (2003): 11–12. http://dx.doi.org/10.1049/ee.2003.0029.

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Cai, Rui, Qian Wang, Yucheng Hou, and Haorui Liu. "Event Monitoring of Transformer Discharge Sounds based on Voiceprint." Journal of Physics: Conference Series 2078, no. 1 (2021): 012066. http://dx.doi.org/10.1088/1742-6596/2078/1/012066.

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Abstract This paper investigates the operation inspection and anomaly diagnosis of transformers in substations, and carries out an application study of artificial intelligence-based sound recognition technology in transformer discharge diagnosis to improve the timeliness and diagnostic capability of intelligent monitoring of substation equipment operation. In this study, a sound parameterization technology in the field of sound recognition is used to implement automatic discharge sound detections. The sound samples are pre-processed and then Mel-frequency cepstrum coefficients (MFCCs) are extr

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Hsu, Fu-Shun, Shang-Ran Huang, Chien-Wen Huang, et al. "Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1." PLOS ONE 16, no. 7 (2021): e0254134. http://dx.doi.org/10.1371/journal.pone.0254134.

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A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios—such as in monitoring disease progression of coronavirus disease 2019—to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio file

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Lei, Baiying, and Man-Wai Mak. "Robust scream sound detection via sound event partitioning." Multimedia Tools and Applications 75, no. 11 (2015): 6071–89. http://dx.doi.org/10.1007/s11042-015-2555-z.

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Kimura, Keisuke, Taro Kasahara, and Hikaru Watabe. "Anomaly detection and visualization for sound data with pre-trained deep neural networks." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 6 (2023): 2276–83. http://dx.doi.org/10.3397/in_2023_0336.

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Anomaly detection in the industrial domain is a critical issue. Although anomaly detection needs expert's experiences or huge person-hours, recent researches of deep neural networks can save much labor. This paper proposes an anomaly detection method for sound data with pre-trained deep neural networks, and an anomaly visualization method to specify the frequency components of anomalous sounds. Common anomaly detection methods train deep neural networks which can detect anomalies in specific domains. However, training such networks from scratch costs a lot because the neural networks have nume

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Huang, Jun, and Baoli Zhang. "Voice Detection and Music Smart Classroom Teaching Application Based on Mobile Edge Computing." Computational Intelligence and Neuroscience 2022 (September 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/4718421.

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Audio monitoring information technology plays an important role in the application of monitoring systems, and it is an indispensable and important link. Whether intelligent audio monitoring management can be successfully realized, the key is to successfully detect abnormal sounds from a variety of external environment background sounds. The core technology of abnormal sound detection is a pattern classification task. The dimension of features is fixed in the traditional abnormal sound detection model. Such an ordinary solution will lead to a long time-consuming detection process and increase t

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Mukhamadiyev, Abdinabi, Ilyos Khujayarov, Dilorom Nabieva, and Jinsoo Cho. "An Ensemble of Convolutional Neural Networks for Sound Event Detection." Mathematics 13, no. 9 (2025): 1502. https://doi.org/10.3390/math13091502.

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Sound event detection tasks are rapidly advancing in the field of pattern recognition, and deep learning methods are particularly well suited for such tasks. One of the important directions in this field is to detect the sounds of emotional events around residential buildings in smart cities and quickly assess the situation for security purposes. This research presents a comprehensive study of an ensemble convolutional recurrent neural network (CRNN) model designed for sound event detection (SED) in residential and public safety contexts. The work focuses on extracting meaningful features from

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Hsu, Fu-Shun, Shang-Ran Huang, Chien-Wen Huang, et al. "A Progressively Expanded Database for Automated Lung Sound Analysis: An Update." Applied Sciences 12, no. 15 (2022): 7623. http://dx.doi.org/10.3390/app12157623.

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We previously established an open-access lung sound database, HF_Lung_V1, and developed deep learning models for inhalation, exhalation, continuous adventitious sound (CAS), and discontinuous adventitious sound (DAS) detection. The amount of data used for training contributes to model accuracy. In this study, we collected larger quantities of data to further improve model performance and explored issues of noisy labels and overlapping sounds. HF_Lung_V1 was expanded to HF_Lung_V2 with a 1.43× increase in the number of audio files. Convolutional neural network–bidirectional gated recurrent unit

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Jacobsen, Thomas, Erich Schröger, István Winkler, and János Horváth. "Familiarity Affects the Processing of Task-irrelevant Auditory Deviance." Journal of Cognitive Neuroscience 17, no. 11 (2005): 1704–13. http://dx.doi.org/10.1162/089892905774589262.

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The effects of familiarity on auditory change detection on the basis of auditory sensory memory representations were investigated by presenting oddball sequences of sounds while participants ignored the auditory stimuli. Stimulus sequences were composed of sounds that were familiar and sounds that were made unfamiliar by playing the same sounds backward. The roles of frequently presented stimuli (standards) and infrequently presented ones (deviants) were fully crossed. Deviants elicited the mismatch negativity component of the event-related brain potential. We found an enhancement in detecting

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Xu, Shuting, Ravinesh C. Deo, Oliver Faust, Prabal D. Barua, Jeffrey Soar, and Rajendra Acharya. "Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals." Diagnostics 15, no. 9 (2025): 1155. https://doi.org/10.3390/diagnostics15091155.

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Background and objective: Chronic respiratory diseases, such as asthma and COPD, pose significant challenges to human health and global healthcare systems. This pioneering study utilises AI analysis and modelling of cough and respiratory sound signals to classify and differentiate between asthma, COPD, and healthy subjects. The aim is to develop an AI-based diagnostic system capable of accurately distinguishing these conditions, thereby enhancing early detection and clinical management. Our study, therefore, presents the first AI system that leverages dual acoustic signals to enhance the diagn

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Du, Shi Bin, Guan Yu Tian, Shu Zhong Bai, and Lan Tian. "An ICA-Based Audio Feature Fault Detection Method for Transformer Equipments." Advanced Materials Research 805-806 (September 2013): 706–11. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.706.

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Experienced engineers in transformer substation can judge the equipment condition via just listening to the working sounds of electrical equipments. Use audio signal processing applied in engines and other mechanical equipments for reference. A scheme to monitor the working condition of electrical equipments is proposed. Firstly, the basic principles and system structure of this scheme is outlined. It introduces the method of colleting electrical equipments working sounds by Microphone array, because Microphone array form a beam to target the source sound, which can reduce the noise and reverb

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Liu, Longshen, Bo Li, Ruqian Zhao, Wen Yao, Mingxia Shen, and Ji Yang. "A Novel Method for Broiler Abnormal Sound Detection Using WMFCC and HMM." Journal of Sensors 2020 (January 13, 2020): 1–7. http://dx.doi.org/10.1155/2020/2985478.

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Broilers produce abnormal sounds such as cough and snore when they suffer from respiratory diseases. The aim of this research work was to develop a method for broiler abnormal sound detection. The sounds were recorded in a broiler house for one week (24/7). There were 20 thousand white feather broilers reared on the floor in a building. Results showed that the developed recognition algorithm, using wavelet transform Mel frequency cepstrum coefficients (WMFCCs), correlation distance Fisher criterion (CDF), and hidden Markov model (HMM), provided an average accuracy, precision, recall, and F1 of

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Lv, Jianxun, Penghui Zhao, Haiwen Yuan, and Xinyu Liu. "Research on the Detection Technology of Audible Noise Sources of UHVDC Transmission Lines." Journal of Physics: Conference Series 2166, no. 1 (2022): 012059. http://dx.doi.org/10.1088/1742-6596/2166/1/012059.

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Abstract In order to directly and deeply study the law and characteristics of audible noise sources of UHVDC transmission lines, a method of detecting the sound intensity vector at the source position in the bundle conductor is proposed. First, the structure of the sound intensity detection device is designed, and the method for calculating the sound intensity vector is deduced based on this prototype. Then, the simulation of the electric field distribution on the surface of the device in the UHV extreme electric field environment is carried out. The change of the maximum electric field intens

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Shin, Sungho, Seongju Lee, Changhyun Jun, and Kyoobin Lee. "BattleSound: A Game Sound Benchmark for the Sound-Specific Feedback Generation in a Battle Game." Sensors 23, no. 2 (2023): 770. http://dx.doi.org/10.3390/s23020770.

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A haptic sensor coupled to a gamepad or headset is frequently used to enhance the sense of immersion for game players. However, providing haptic feedback for appropriate sound effects involves specialized audio engineering techniques to identify target sounds that vary according to the game. We propose a deep learning-based method for sound event detection (SED) to determine the optimal timing of haptic feedback in extremely noisy environments. To accomplish this, we introduce the BattleSound dataset, which contains a large volume of game sound recordings of game effects and other distracting

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Yang, Yuhua, Bo Wang, Jiangong Cui, et al. "Design and Realization of MEMS Heart Sound Sensor with Concave, Racket-Shaped Cilium." Biosensors 12, no. 7 (2022): 534. http://dx.doi.org/10.3390/bios12070534.

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The biomedical acoustic signal plays an important role in clinical non-invasive diagnosis. In view of the deficiencies in early diagnosis of cardiovascular diseases, acoustic properties of S1 and S2 heart sounds are utilized. In this paper, we propose an integrated concave cilium MEMS heart sound sensor. The concave structure enlarges the area for receiving sound waves to improve the low-frequency sensitivity, and realizes the low-frequency and high-sensitivity characteristics of an MEMS heart sound sensor by adopting a reasonable acoustic package design, reducing the loss of heart sound disto

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Sawada, Hideyuki, and Toshiya Takechi. "A Robotic Auditory System that Interacts with Musical Sounds and Human Voices." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 10 (2007): 1177–83. http://dx.doi.org/10.20965/jaciii.2007.p1177.

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Voice and sounds are the primary media employed for human communication. Humans are able to exchange information smoothly using voice under different situations, such as a noisy environment and in the presence of multiple speakers. We are surrounded by various sounds, and yet are able to detect the location of a sound source in 3D space, extract a particular sound from a mixture of sounds, and recognize the source of a specific sound. Also, music is composed of various sounds generated by musical instruments, and directly affects our emotions and feelings. This paper introduces real-time detec

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Tan, Xiao, and S. M. Yiu. "Introduction of a Novel Anomalous Sound Detection Methodology." International Journal on Soft Computing 13, no. 3 (2022): 1–21. http://dx.doi.org/10.5121/ijsc.2022.13301.

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This paper is to introduce a novel semi-supervised methodology, the enhanced incremental principal component analysis (“IPCA”) based deep convolutional neural network autoencoder (“DCNN-AE) for Anomalous Sound Detection (“ASD”) with high accuracy and computing efficiency. This hybrid methodology is to adopt Enhanced IPCA to reduce the dimensionality and then to use DCNN-AE to extract the features of the sample sound and detect the anomality. In this project, 228 sets of normal sounds and 100 sets of anomaly sounds of same machine are used for the experiments. And the sound files of machines (s

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Tan, Xiao, and S. M. Yiu. "Introduction of a Novel Anomalous Sound Detection Methodology." International Journal on Cybernetics & Informatics 11, no. 4 (2022): 31–51. http://dx.doi.org/10.5121/ijci.2022.110403.

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This paper is to introduce a novel semi-supervised methodology, the enhanced incremental principal component analysis (“IPCA”) based deep convolutional neural network autoencoder (“DCNN-AE) for Anomalous Sound Detection (“ASD”) with high accuracy and computing efficiency. This hybrid methodology is to adopt Enhanced IPCA to reduce the dimensionality and then to use DCNN-AE to extract the features of the sample sound and detect the anomality. In this project, 228 sets of normal sounds and 100 sets of anomaly sounds of same machine are used for the experiments. And the sound files of machines (s

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Xiang, Ning, and Thomas Metzger. "Prediction model formulations for detection, enumeration, and localization of multiple sound sources using spherical harmonics." Journal of the Acoustical Society of America 151, no. 4 (2022): A231. http://dx.doi.org/10.1121/10.0011157.

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A spherical microphone array is used to detect and localize sound sources in terms of model-based machine learning (ML). In this application, it is crucial to establish parametric models to distinguish background sound environment from presence of sound sources. In the presence of sound sources, the parameter models are also used to localize an unknown number of potentially multiple sound sources. In this work, a model-based Bayesian learning framework is presented for localizing an unknown number of sound sources. Among them, a no-source scenario needs to be accounted for. The model-based mac

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Gai, Yan, Vibhakar C. Kotak, Dan H. Sanes, and John Rinzel. "On the localization of complex sounds: temporal encoding based on input-slope coincidence detection of envelopes." Journal of Neurophysiology 112, no. 4 (2014): 802–13. http://dx.doi.org/10.1152/jn.00044.2013.

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Behavioral and neural findings demonstrate that animals can locate low-frequency sounds along the azimuth by detecting microsecond interaural time differences (ITDs). Information about ITDs is also available in the amplitude modulations (i.e., envelope) of high-frequency sounds. Since medial superior olivary (MSO) neurons encode low-frequency ITDs, we asked whether they employ a similar mechanism to process envelope ITDs with high-frequency carriers, and the effectiveness of this mechanism compared with the process of low-frequency sound. We developed a novel hybrid in vitro dynamic-clamp appr

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