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Relevant bibliographies by topics / Microphone based acoustic vector sensor

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Dissertations / Theses
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Academic literature on the topic 'Microphone based acoustic vector sensor'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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Journal articles on the topic "Microphone based acoustic vector sensor"

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Waite, Jim, David Dall'Osto, and Callum McCubbin. "Autonomous monitoring of traffic, rail, and industrial noise using acoustic vector beamformers based on 3D MEMS accelerometers." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 6 (2023): 2124–32. http://dx.doi.org/10.3397/in_2023_0312.

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Fully unattended acoustic monitoring has been hindered by the need to automatically discriminate specific sources from overall noise levels at a measurement location, even if that noise is very close in proximity. Existing 3D microphone-based acoustic intensity or camera solutions are unwieldy and expensive for real-time deployments. A new system has been developed for autonomously monitoring noise levels in transportation and industrial settings, managed within an IoT network. Each measurement node has one or two Acoustic Real-time Event Sensors (ARES) and a beamformer algorithm using 3D MEMS

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Dall'Osto, David R., Peter H. Dahl, and Jim Waite. "Measuring the effect of ground impedance on the vector field, both in air and underwater." Journal of the Acoustical Society of America 152, no. 4 (2022): A193. http://dx.doi.org/10.1121/10.0015996.

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One direct advantage to characterizing the acoustic field with a vector sensor is the ability to measure both components of acoustic energy, potential (pressure) and kinetic (particle velocity). While it is well known that the kinetic energy exceeds potential in the near-field of a source, equivalent and opposite imbalances are prevalent in the far-field due to propagation effects. For example, at normal incidence the field exhibits a distinct frequency dependent relationship that depends explicitly on the impedance of the ground reflector, including any internal reflections from sediment laye

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Yu, Yicheng, Rob Worley, Sean Anderson, and Kirill V. Horoshenkov. "Microphone array analysis for simultaneous condition detection, localization, and classification in a pipe." Journal of the Acoustical Society of America 153, no. 1 (2023): 367–83. http://dx.doi.org/10.1121/10.0016856.

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An acoustic method for simultaneous condition detection, localization, and classification in air-filled pipes is proposed. The contribution of this work is threefold: (1) a microphone array is used to extend the usable acoustic frequency range to estimate the reflection coefficient from blockages and lateral connections; (2) a robust regularization method of sparse representation based on a wavelet basis function is adapted to reduce the background noise in acoustical data; and (3) the wavelet components are used to localize and classify the condition of the pipe. The microphone array and spar

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Yu, Jingjing, Qi Xi, Runlei Li, Hui Tian, and Yaxi Xie. "Stochastic allocation strategy for irregular arrays based on geometric feature control." International Journal of Distributed Sensor Networks 16, no. 5 (2020): 155014772092177. http://dx.doi.org/10.1177/1550147720921775.

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Irregularities in microphone distribution enrich the diversity of spatial differences to decorrelate interferences from the beamforming target. However, the large degrees of freedom of irregular placements make it difficult to analyse and optimize array performance. This article proposes fast and feasible optimal irregular array design methods with improved beamforming performance for human speech. Important geometric features are extracted to be used as the input vector of the neural network structure to determine the optimal irregular arrangements of sensors. In addition, a hyperbola design

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Kotus, Jozef, Grzegorz Szwoch, Andrzej Czyzewski, and Bozena Kostek. "Assessment of road surface state with acoustic vector sensor." Journal of the Acoustical Society of America 152, no. 4 (2022): A193. http://dx.doi.org/10.1121/10.0015995.

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A method of determining the road surface state based on the sound intensity analysis is presented. The proposed method is designed for the passive, non-contact assessment of a road surface state, especially in dry/wet conditions. The proposed method is intended for monitoring stations utilizing low-cost hardware. The road surface state is determined from the analysis of sound intensity emitted by road vehicles passing by the sensor and recorded with an acoustic vector sensor (AVS). A frequency domain sound intensity analysis included spatial filtering to reduce the environmental interference u

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Mohtadifar, Masoud, Michael Cheffena, and Alireza Pourafzal. "Acoustic- and Radio-Frequency-Based Human Activity Recognition." Sensors 22, no. 9 (2022): 3125. http://dx.doi.org/10.3390/s22093125.

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In this work, a hybrid radio frequency (RF)- and acoustic-based activity recognition system was developed to demonstrate the advantage of combining two non-invasive sensors in Human Activity Recognition (HAR) systems and smart assisted living. We used a hybrid approach, employing RF and acoustic signals to recognize falling, walking, sitting on a chair, and standing up from a chair. To our knowledge, this is the first work that attempts to use a mixture of RF and passive acoustic signals for Human Activity Recognition purposes. We conducted experiments in the lab environment using a Vector Net

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Kotus, Józef, and Grzegorz Szwoch. "Calibration of acoustic vector sensor based on MEMS microphones for DOA estimation." Applied Acoustics 141 (December 2018): 307–21. http://dx.doi.org/10.1016/j.apacoust.2018.07.025.

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Yang, Lingmeng, Zhezheng Zhu, Wangnan Chen, Chengchen Gao, Yilong Hao, and Zhenchuan Yang. "Quantitative Analysis Method and Correction Algorithm Based on Directivity Beam Pattern for Mismatches between Sensitive Units of Acoustic Dyadic Sensors." Sensors 23, no. 12 (2023): 5709. http://dx.doi.org/10.3390/s23125709.

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Acoustic dyadic sensors (ADSs) are a new type of acoustic sensor with higher directivity than microphones and acoustic vector sensors, which has great application potential in the fields of sound source localization and noise cancellation. However, the high directivity of an ADS is seriously affected by the mismatches between its sensitive units. In this article, (1) a theoretical model of mixed mismatches was established based on the finite-difference approximation model of uniaxial acoustic particle velocity gradient and its ability to reflect the actual mismatches was proven by the comparis

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Zou, Yuexian, Zhaoyi Liu, and Christian Ritz. "Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor." Applied Sciences 8, no. 9 (2018): 1436. http://dx.doi.org/10.3390/app8091436.

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Enhancing speech captured by distant microphones is a challenging task. In this study, we investigate the multichannel signal properties of the single acoustic vector sensor (AVS) to obtain the inter-sensor data ratio (ISDR) model in the time-frequency (TF) domain. Then, the monotone functions describing the relationship between the ISDRs and the direction of arrival (DOA) of the target speaker are derived. For the target speech enhancement (SE) task, the DOA of the target speaker is given, and the ISDRs are calculated. Hence, the TF components dominated by the target speech are extracted with

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Gabrielson, Thomas B., and Daniel C. Brown. "Fireworks." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A216. http://dx.doi.org/10.1121/10.0018701.

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Once a year, in many parts of the country, transient acoustic sources light up the sky. A fourth-of-July fireworks display provides an excellent opportunity for exploring array-based detection and direction-of-arrival determination. Multiple-boom events are difficult to process; however, less-frequent single-boom events are often sufficiently isolated in time for unambiguous interpretation. Cross-correlations between channels of a four-microphone array provide time delays for angle-of-arrival determination and measures for event detection. The distinct time-domain wave shapes and high signal-t

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Dissertations / Theses on the topic "Microphone based acoustic vector sensor"

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Wajid, Mohd. "Design and analysis of microphone based acoustic vector sensor for 2D direction of arrival estimation." Thesis, 2018. http://localhost:8080/iit/handle/2074/7693.

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Akula, Rajani. "Acoustic vector sensor based doa estimation of snow avalanches with infrasound modeling." Thesis, 2018. http://localhost:8080/iit/handle/2074/7604.

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Malhi, Charanjeet Kaur. "Studies on the Design of Novel MEMS Microphones." Thesis, 2014. http://etd.iisc.ac.in/handle/2005/3125.

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MEMS microphones have been a research topic for the last two and half decades. The state-of-the-art comprises surface mount MEMS microphones in laptops, mobile phones and tablets, etc. The popularity and the commercial success of MEMS microphones is largely due to the steep cost reduction in manufacturing afforded by the mass scale production with microfabrication technology. The current MEMS microphones are de-signed along the lines of traditional microphones that use capacitive transduction with or without permanent charge (electret type microphones use permanent charge of their sensor eleme

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Malhi, Charanjeet Kaur. "Studies on the Design of Novel MEMS Microphones." Thesis, 2014. http://hdl.handle.net/2005/3125.

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MEMS microphones have been a research topic for the last two and half decades. The state-of-the-art comprises surface mount MEMS microphones in laptops, mobile phones and tablets, etc. The popularity and the commercial success of MEMS microphones is largely due to the steep cost reduction in manufacturing afforded by the mass scale production with microfabrication technology. The current MEMS microphones are de-signed along the lines of traditional microphones that use capacitive transduction with or without permanent charge (electret type microphones use permanent charge of their sensor eleme

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Book chapters on the topic "Microphone based acoustic vector sensor"

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Liu, Guangzhong, and Xueqin Chen. "A Positioning Research of Underwater Acoustic Sensor Networks Based on Support Vector Regression." In Future Computing, Communication, Control and Management. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27326-1_2.

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Xu, Hui-Chao, Su Wang, and Bao-Jun Zhao. "Research and Analysis of Architectural Construction Safety Dynamic Infrasound Network System Based on Acoustic Vector Sensor." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18997-0_53.

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Van Den Broeck B., Vuegen L., Van hamme H., Moonen M., Karsmakers P., and Vanrumste B. "Footstep Localization based on In-home Microphone-array Signals." In Assistive Technology Research Series. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-304-9-90.

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This paper describes a system able to detect footstep locations. Through acoustic information retrieved from a wireless sensor network with small and relatively cheap microphone arrays. A dataset was recorded in order to validate the accuracy of the detection. Results on this dataset show that a best median of errors of 31cm per time moment are achievable, but results heavily depend on the positions of the microphones relative to the footsteps.

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Boulmaiz, Amira, Djemil Messadeg, Noureddine Doghmane, and Abdelmalik Taleb-Ahmed. "Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species Using TMS320C6713 DSK." In Sensor Technology. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch038.

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In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for clas

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Cai Zongyi. "Comparison on Two Methods for Ground Target Bearing Estimation Based on the Theory of Acoustic Vector Sensor." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2019. https://doi.org/10.3233/faia190293.

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Conference papers on the topic "Microphone based acoustic vector sensor"

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Prajapati, Sandhya, Shubham Soni, Arun Kumar, and Rajendar Bahl. "FEM-Based Study of Acoustic Shadowing Effects on Underwater Acoustic Vector Sensor Due to Nearby Supports." In 2025 IEEE Underwater Technology (UT). IEEE, 2025. https://doi.org/10.1109/ut61067.2025.10947438.

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Zhang, Xu, Xiaochun Zhu, Changshuang Huang, and Shengguo Shi. "Robust DOA Estimation Method for Acoustic Vector Sensor Array Based on Element-Space Combination." In 2024 OES China Ocean Acoustics (COA). IEEE, 2024. http://dx.doi.org/10.1109/coa58979.2024.10723354.

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Prajapati, Sandhya, Arun Kumar, and Rajendar Bahl. "FEM-Based Study for Development of Prototype Underwater Acoustic Vector Sensor with integrated Vibration Noise Cancellation." In 2025 IEEE Underwater Technology (UT). IEEE, 2025. https://doi.org/10.1109/ut61067.2025.10947389.

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Li, Xianglu, Youyang Xiang, Zhijiang Huang, et al. "Source Number Estimation Based on the Pressure and Particle Velocity Combined Processing for Acoustic Vector-Sensor Arrays." In 2024 IEEE SENSORS. IEEE, 2024. https://doi.org/10.1109/sensors60989.2024.10784756.

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Saleh, Muhsin Hassanu, Haifa Takruri, and Rukayya Musa Ismail. "Enhanced Energy Aware and Void Avoidance Routing Protocol Based on Vector Based Forwarding for Underwater Acoustic Wireless Sensor Network." In 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). IEEE, 2024. http://dx.doi.org/10.1109/csndsp60683.2024.10636466.

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Gur, M. Berke. "Vector sensor array based higher order acoustic sensors." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830604.

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Agarwal, Ashish, Arun Kumar, and Monika Agrawal. "Cumulants based processing of Acoustic Vector Sensor data." In 2015 IEEE Underwater Technology (UT). IEEE, 2015. http://dx.doi.org/10.1109/ut.2015.7108316.

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Chen, Cheng, Jianwu Tao, and Bin Zeng. "Estimation of airspeed based on acoustic vector sensor array." In 2012 11th International Conference on Signal Processing (ICSP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icosp.2012.6491662.

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Peng Wang, Pan-Pan Wang, Guo-jun Zhang, and Ji-jun Xiong. "Spatial smoothing algorithm based on acoustic vector sensor array." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5622427.

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Guzma, Juraj, Roman Beresik, and Jozef Puttera. "Small-Sized Seismic-Acoustic Sensor System Based on MEMS Accelerometer and MEMS Microphone." In 2018 New Trends in Signal Processing (NTSP). IEEE, 2018. http://dx.doi.org/10.23919/ntsp.2018.8524100.

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