Journal articles: 'Microphone based acoustic vector sensor'

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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 (October 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 layers. In this paper, we examine the measured vector field, both in-air and underwater, with a specific focus the energy balance when a source is directly overhead. While the construction of a neutrally buoyant volume for use in dense media (such as water) is fairly straightforward, the application to airborne acoustics requires extremely lightweight materials. Airborne measurements are provided by an Accelerometer-based Intensity Vector Sensor (AIVS) system, which is constructed from a lightweight MEMS accelerometer and microphone embedded in a spherical, expanded polystyrene (EPS) foam volume.

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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 (January 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 sparse representation method enhance the acoustical signal reflected from blockages and lateral connections and suppress unwanted higher-order modes. Based on the sparse representation results, higher-level wavelet functions representing the impulse response are used to localize the position of the sensor corresponding to a blockage or lateral connection with higher spatial resolution. It is shown that the wavelet components can be used to train and to test a support vector machine (SVM) classifier for the condition identification more accurately than with a time domain SVM classifier. This work paves the way for the development of simultaneous condition classification and localization methods to be deployed on autonomous robots working in buried pipes.

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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 (May 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 method is proposed to directly cluster microphones in the hyperbola areas to produce rich differential distance entropies and yield significant signal-to-noise ratio improvements. These methods can be easily applied to guide non-computer-aided optimal irregular array designs for human speech in acoustic scenes such as immersive cocktail party environments.

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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 (October 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 using the designed amplitude and phase correction algorithms. A test installation in a real-world scenario, consisting of a small AVS constructed from MEMS microphones and a state-of-art optic-based sensor, was used to evaluate the proposed method. A dataset representing many road vehicles moving at different speeds through the observed road section in varying conditions (dry and wet surface) was collected. Compared with the reference data, an evaluation of the proposed method and its accuracy in determining the road surface state is presented and discussed.The project has been subsidized by the Polish National Centre for Research and Development (NCBR) from the European Regional Development Fund No. POIR.04.01.04/2019 entitled: INFOLIGHT—“Cloud-based lighting system for smart cities.”

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Mohtadifar, Masoud, Michael Cheffena, and Alireza Pourafzal. "Acoustic- and Radio-Frequency-Based Human Activity Recognition." Sensors 22, no. 9 (April 19, 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 Network Analyzer measuring the 2.4 GHz frequency band and a microphone array. After recording data, we extracted the Mel-spectrogram feature of the audio data and the Doppler shift feature of the RF measurements. We fed these features to six classification algorithms. Our result shows that using a hybrid acoustic- and radio-based method increases the accuracy of recognition compared to just using only one kind of sensory data and shows the possibility of expanding for a variety of other different activities that can be recognized. We demonstrate that by using a hybrid method, the recognition accuracy increases in all classification algorithms. Among these classifiers, five of them achieve over 98% recognition accuracy.

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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 (June 19, 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 comparison of theoretical and experimental directivity beam patterns of an actual ADS based on MEMS thermal particle velocity sensors. (2) Additionally, a quantitative analysis method based on directivity beam pattern was proposed to easily estimate the specific magnitude of the mismatches, which was proven to be useful for the design of ADSs to estimate the magnitudes of different mismatches of an actual ADS. (3) Moreover, a correction algorithm based on the theoretical model of mixed mismatches and quantitative analysis method was successfully demonstrated to correct several groups of simulated and measured beam patterns with mixed mismatches.

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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 (August 23, 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 high probability using the established monotone functions, and then, a nonlinear soft mask of the target speech is generated. As a result, a masking-based speech enhancement method is developed, which is termed the AVS-SMASK method. Extensive experiments with simulated data and recorded data have been carried out to validate the effectiveness of our proposed AVS-SMASK method in terms of suppressing spatial speech interferences and reducing the adverse impact of the additive background noise while maintaining less speech distortion. Moreover, our AVS-SMASK method is computationally inexpensive, and the AVS is of a small physical size. These merits are favorable to many applications, such as robot auditory systems.

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Gabrielson, Thomas B., and Daniel C. Brown. "Fireworks." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 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-to-noise enable straightforward checks of the cross-correlation delays. In addition, a chirp sequence broadcast from a known location provides a ground-truth measurement and opportunities to implement matched filtering and envelope processing. Detection strategies can include received power, rate of change of power, normalized cross-correlation coefficient, or Fisher F-statistic. A least-squares fit by plane-wave approximation gives an estimate of the actual slowness vector, which, in turn, permits estimation of error and of local sound speed. Conversion of the slowness vector to arrival azimuth and elevation angles provides a simple exercise in 3D interpretation. The homework exercise can be as simple as determination of inter-sensor time delays or as complicated as demonstrating a scheme for automatic detection and interpretation.

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Ye, Liang, Peng Wang, Le Wang, Hany Ferdinando, Tapio Seppänen, and Esko Alasaarela. "A Combined Motion-Audio School Bullying Detection Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 12 (August 27, 2018): 1850046. http://dx.doi.org/10.1142/s0218001418500465.

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School bullying is a common social problem, which affects children both mentally and physically, making the prevention of bullying a timeless topic all over the world. This paper proposes a method for detecting bullying in school based on activity recognition and speech emotion recognition. In this method, motion and voice data are gathered by movement sensors and a microphone, followed by extraction of a set of motion and audio features to distinguish bullying incidents from daily life events. Among extracted motion features are both time-domain and frequency-domain features, while audio features are computed with classical MFCCs. Feature selection is implemented using the wrapper approach. At the next stage, these motion and audio features are merged to form combined feature vectors for classification, and LDA is used for further dimension reduction. A BPNN is trained to recognize bullying activities and distinguish them from normal daily life activities. The authors also propose an action transition detection method to reduce computational complexity for practical use. Thus, the bullying detection algorithm will only run, when an action transition event has been detected. Simulation results show that the combined motion-audio feature vector outperforms separate motion features and acoustic features, achieving an accuracy of 82.4% and a precision of 92.2%. Moreover, with the action transition method, the computation cost can be reduced by half.

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Jegorowa, Albina, Jarosław Kurek, Michał Kruk, and Jarosław Górski. "The Use of Multilayer Perceptron (MLP) to Reduce Delamination during Drilling into Melamine Faced Chipboard." Forests 13, no. 6 (June 15, 2022): 933. http://dx.doi.org/10.3390/f13060933.

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Drilling into melamine-faced-wood-based panels is one of the most common processes in modern furniture manufacturing. Delamination is usually the main and the most troublesome quality defect in this case. A lot of scientific studies draw the conclusion that the progress of tool wearing during the cutting of wood-based materials is the key problem. Therefore, tool condition monitoring and the replacement of worn tools at the right time is the most useful and common (in the industrial practice) way to reduce delamination. However, the automation of this process is still a problem due to various issues. There is yet no commercial (even prototypical) offer for the furniture industry in this regard. For this reason, it is considered advisable to try to use the multilayer perceptron (MLP) algorithm to automatically identify a drill’s condition during drilling in a laminated chipboard. It has been established that, for practical purposes, it is important to distinguish between the three different classes of tool conditions, which can be conventionally described as “Green” (keep working), “Red” (implicitly stop and replace) and “Yellow” (warning signal—stop and replace if you want to avoid deterioration in cutting quality). To register the signals generated in the cutting zone and those constituting the basis for the identification of the tool condition in the “on-line” mode, the following elements were used: contact sensor of acoustic emission, accelerometer for vibration, two-component force gauge and a microphone. The classification effects (with an overall accuracy above 70%) were ultimately fairly decent but slightly worse than those of the classification algorithms tested earlier (i.e., “nearest neighbors” or “support vector machine” algorithms). The most troublesome, however, is the fact that serious errors (mistakes between “Green” and “Red” classes) were occasionally noted (for about 1% of the analyzed cases).

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Li, Chen, Jiang, and Han. "Chinese Traditional Musical Instrument Evaluation Based on a Smart Microphone Array Sensor." Proceedings 15, no. 1 (August 15, 2019): 40. http://dx.doi.org/10.3390/proceedings2019015040.

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For Chinese traditional musical instruments, the general subjective evaluation method by experts is not cost-effective and is limited by fewer and fewer experts, but a clear physical law is very hard to established by physicists. Considering the effectiveness of artificial neural networks (ANNs) for complex system, for a Chinese lute case, a neural network based 8-microphone array is applied to correlate the objective instrument acoustic features with expert subjective evaluations in this paper. The acoustic features were recorded by a microphone array sensor and extracted as the constant-Q transform coefficients, Mel-frequency cepstral coefficients and correlation coefficients between each microphone for ANNs input. The acoustic library establishment, acoustic features extractions, and deep learning model for Chinese lutes evaluation are reported in this paper.

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Wang, Qiao Yun, and Zhen He Ma. "Polymer Diaphragm Based Fiber Optic Fabry-Perot Acoustic Sensor." Applied Mechanics and Materials 401-403 (September 2013): 1087–90. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1087.

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This paper presents a polymer diaphragm based Fabry-Perot (F-P) sensor system for aeroacoustic measurement in air. The diaphragm of a novel polymer material poly phthalazinone ether sulfone ketone (PPESK) is used as the sensing element. The effective dimension of the diaphragm is 1.0mm in diameter and 6μm in thickness. Thanks to the good mechanical feature of the material and the interferometric-intensity demodulation mechanism, the sensor diaphragm shift sensitivity of 0.72 nm/Pa, correspond to an acoustic pressure to voltage sensitivity of 5.56mV/Pa has been achieved. Experimental results showed that the characteristics of the sensor system is comparable with traditional electrical microphone in frequency range 100Hz to 1kHz and the sensor has the potential to be used as an optical microphone.

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Guo, Huihui, Jianbo Li, Tingting Liu, Mingqiang Feng, and Yang Gao. "Design and Optimization of a BAW Microphone Sensor." Micromachines 13, no. 6 (June 2, 2022): 893. http://dx.doi.org/10.3390/mi13060893.

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A wind tunnel experiment is an important way and effective method to research the generation mechanism of aerodynamic noise and verify aerodynamic noise reduction technology. Acoustic measurement is an important part of wind tunnel experiments, and the microphone is the core device in an aerodynamic acoustic measurement system. Aiming at the problem of low sound pressure (several Pa) and the small measuring surface of an experimental model in a wind tunnel experiment, a microphone sensor head with high sensitivity and small volume, based on film bulk acoustic resonator (FBAR), is presented and optimized in this work. The FBARs used as a transducer are located at the edge of a diaphragm for sound pressure level detection. A multi-scale and multi-physical field coupling analysis model of the microphone is established. To improve the performance of the microphone, the structural design parameters of the FBAR and the diaphragm are optimized by simulation. The research results show that the microphone has a small size, good sensitivity, and linearity. The sensor head size is less than 1 mm × 1 mm, the sensitivity is about 400 Hz/Pa when the sensor worked at the first-order resonance frequency, and the linearity is better than 1%.

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Chikai, Manabu, Ayuko Kamiyanagi, Kenta Kimura, Yoshikazu Seki, Hiroshi Endo, Yuka Sumita, Hisashi Taniguchi, and Shuichi Ino. "Pilot Study on an Acoustic Measurements System of the Swallowing Function Using an Acoustic-Emissions Microphone." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (March 15, 2017): 293–300. http://dx.doi.org/10.20965/jaciii.2017.p0293.

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The goal of this study is to evaluate the swallowing functions of people with dysphagia using an acoustic microphone sensor. As a basic investigation towards this end, we measured the swallowing sounds using an acoustic emissions microphone sensor (AE sensor), then analyzed the frequency range of the measured signals, and we examined the method for obtaining the necessary information to evaluate the swallowing functions. For the measurement, two types of sensors, i.e., a condenser throat microphone and an AE sensor, were employed to measure the swallowing sounds. The acoustic signals obtained were subjected to spectral analysis using the wavelet transformation, and a comparison was performed between the measurable ranges of the acoustic signals obtained by the AE and the acoustic sensors. The results from the wavelet transformation of the acoustic signals obtained by the AE sensor indicated that acoustic signals generated during swallowing contained frequency information of 3 kHz and higher, which were not measurable with the acoustic sensor used in the experiment. In addition, we proposed a method of evaluating swallowing sounds using a novel approach based on the probability distribution. From the analysis results, it was found that the distance between the theoretical values and the measured values has a high correlation with the sample viscosity. Furthermore, it was found that the data measured with the AE sensor more sensitively reflected the difference in the sample viscosity. We were thus able to demonstrate the possibility of evaluating the swallowing function via the proposed method.

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Avots, Egils, Alekss Vecvanags, Jevgenijs Filipovs, Agris Brauns, Gundars Skudrins, Gundega Done, Janis Ozolins, Gholamreza Anbarjafari, and Dainis Jakovels. "Towards Automated Detection and Localization of Red Deer Cervus elaphus Using Passive Acoustic Sensors during the Rut." Remote Sensing 14, no. 10 (May 20, 2022): 2464. http://dx.doi.org/10.3390/rs14102464.

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Passive acoustic sensors have the potential to become a valuable complementary component in red deer Cervus elaphus monitoring providing deeper insight into the behavior of stags during the rutting period. Automation of data acquisition and processing is crucial for adaptation and wider uptake of acoustic monitoring. Therefore, an automated data processing workflow concept for red deer call detection and localization was proposed and demonstrated. The unique dataset of red deer calls during the rut in September 2021 was collected with four GPS time-synchronized microphones. Five supervised machine learning algorithms were tested and compared for the detection of red deer rutting calls where the support-vector-machine-based approach demonstrated the best performance of −96.46% detection accuracy. For sound source location, a hyperbolic localization approach was applied. A novel approach based on cross-correlation and spectral feature similarity was proposed for sound delay assessment in multiple microphones resulting in the median localization error of 16 m, thus providing a solution for automated sound source localization—the main challenge in the automation of the data processing workflow. The automated approach outperformed manual sound delay assessment by a human expert where the median localization error was 43 m. Artificial sound records with a known location in the pilot territory were used for localization performance testing.

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Hopper, Richard, Daniel Popa, Vasileios Tsoutsouras, Florin Udrea, and Phillip Stanley-Marbell. "Miniaturized Thermal Acoustic Gas Sensor Based on a CMOS Microhotplate and MEMS Microphone." Proceedings 56, no. 1 (December 7, 2020): 3. http://dx.doi.org/10.3390/proceedings2020056003.

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In this work, we present a novel thermal acoustic gas sensor, fabricated using a CMOS microhotplate and MEMS microphone. The sensing mechanism is based on the detection of changes in the thermal acoustic conversion efficiency which is dependent on the physical properties of the gas. The gas sensor has all the benefits of CMOS technology, including low cost and miniaturization. Here, we demonstrate its application for CO2 gas detection.

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Monteiro, Catarina S., Maria Raposo, Paulo A. Ribeiro, Susana O. Silva, and Orlando Frazão. "Acoustic Optical Fiber Sensor Based on Graphene Oxide Membrane." Sensors 21, no. 7 (March 27, 2021): 2336. http://dx.doi.org/10.3390/s21072336.

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A Fabry–Pérot acoustic sensor based on a graphene oxide membrane was developed with the aim to achieve a faster and simpler fabrication procedure when compared to similar graphene-based acoustic sensors. In addition, the proposed sensor was fabricated using methods that reduce chemical hazards and environmental impacts. The developed sensor, with an optical cavity of around 246 µm, showed a constant reflected signal amplitude of 6.8 ± 0.1 dB for 100 nm wavelength range. The sensor attained a wideband operation range between 20 and 100 kHz, with a maximum signal-to-noise ratio (SNR) of 32.7 dB at 25 kHz. The stability and sensitivity to temperatures up to 90 °C was also studied. Moreover, the proposed sensor offers the possibility to be applied as a wideband microphone or to be applied in more complex systems for structural analysis or imaging.

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Parkins, John W., Jiri Tichy, and Scott D. Sommerfeldt. "The effects of microphone mismatch on the performance of a spherical acoustic vector‐field sensor." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3035. http://dx.doi.org/10.1121/1.418648.

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Yan, Jiaming, Caihui Chen, Zhipeng Wu, Xiaoxia Ding, and Liang Lou. "An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge." Sensors 23, no. 3 (January 17, 2023): 1077. http://dx.doi.org/10.3390/s23031077.

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Partial discharge (PD) localization is important for monitoring and maintaining high-voltage equipment, which can help to prevent accidents. In this work, an acoustic localization sensor based on microelectromechanical system (MEMS) microphone array is proposed, which can detect and locate the partial discharge through a beam-forming algorithm. The MEMS microphone array consists of eight commercial MEMS microphones (SPV08A0LR5H-1, Knowles Electronics, IL, USA) with an aperture size of about 0.1 m × 0.1 m, allowing for a small hardware size and low cost. In order to optimize the acoustic performance of the array, a random array topology is designed. The simulation analysis indicates that the designed random topology is superior to several commonly used topologies. In terms of the localization algorithm, a deconvolution method called Fourier-based fast iterative shrinkage thresholding algorithm (FFT-FISTA) is applied. Simulation and experiment results demonstrate that FFT-FISTA used in the proposed acoustic localization sensor has significant advantages over the conventional beam-forming algorithm on spatial resolution and sidelobe suppression. Experimental results also show that the average localization error of the proposed scheme is about 0.04 m, which can meet the demands of practical application.

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Liu, Shuang, Yu Lan, and Qi Li. "Design of Underwater Acoustic Vector Sensor and its Elastic Suspension Element." Applied Mechanics and Materials 713-715 (January 2015): 569–72. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.569.

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Acoustic vector sensor is a kind of inertial sensors. Different from pressure-sensing sensor, it can sense the angle of target under the water. Now most suspension elements are installed on the acoustic vector sensor later. This paper outlines an acoustic vector sensor which is encased in silicon rubber. In this paper, a new structure of acoustic vector sensor is present. The radial stiffness of silicon rubber spring is analyzed by using theory and simulation calculation based on the finite element software ANSYS. At last, the acoustic vector sensor is measured. The results show that the new structural design of acoustic vector sensor could reduce the adverse impact of suspending elastic suspension elements repeatedly and improve the reliability of acoustic vector sensor.

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Song, Ping, Chuangbo Hao, Jiangpeng Wu, and Cheng Yang. "Acoustic source localization using 10-microphone array based on wireless sensor network." Sensors and Actuators A: Physical 267 (November 2017): 376–84. http://dx.doi.org/10.1016/j.sna.2017.10.019.

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Al-Farzaq, Ayu Afifah, Harmadi Harmadi, and Elvaswer Elvaswer. "Wireless Data Logger Instrument for Indoor Acoustic Quality Measurement Based on Noise Background, Sound Distribution and Reverberation Time." JURNAL ILMU FISIKA | UNIVERSITAS ANDALAS 14, no. 1 (February 10, 2022): 37–44. http://dx.doi.org/10.25077/jif.14.1.37-44.2022.

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A wireless data logger system measuring indoor acoustic quality was developed using a NodeMCU ESP8266 and microphone sensor KY-037. The acoustic quality is based on parameters of sound pressure level and reverberation time. The system consists of a transmitter unit and a receiver unit. The transmitter unit is equipped with a Microphone KY-037 sensor as a sound detector and NodeMCU ESP8266 as a microcontroller and serial communication with the database. The transmitter unit of this measuring instrument is set at predetermined position points in a room. The results of testing the sound pressure level have an error percentage of 2.09% compared to the Digital Sound Level Meter GM1356 tool. Comprehensive testing of the tool has sent and processed sensor measurement data wirelessly into the database. The processed data is displayed through a GUI web server in the form of a background noise average, an average of the reverberation time value, and a graph of the sound pressure level in the room.

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Weber, Christian, Johannes Kapp, Katrin Schmitt, Hans-Fridtjof Pernau, and Jürgen Wöllenstein. "Resonant Photoacoustic Detection of NO2 with an LED Based Sensor." Proceedings 2, no. 13 (November 14, 2018): 1036. http://dx.doi.org/10.3390/proceedings2131036.

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In times of steadily increasing air pollution especially in urban areas, the monitoring of nitrogen dioxide (NO2) has gained in importance and with it the search for compact, low-cost sensors. We present a novel approach to measure NO2 in sub-ppm concentrations with a photoacoustic sensor utilizing a T-shaped resonance cell. An inexpensive single LED with a peak wavelength of 410 nm was used as radiation source and the acoustic detection was done with a commercial MEMS microphone. For optimal coupling of the divergent LED light into the cell, the T-shaped resonator was developed and fabricated with rapid prototyping methods. The resonator shows a acoustic Q-factor >10 while having nearly no zero gas signal.

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Finn, Anthony, Kevin Rogers, Feng Rice, Joshua Meade, Greg Holland, and Peter May. "A Comparison of Vertical Atmospheric Wind Profiles Obtained from Monostatic Sodar and Unmanned Aerial Vehicle–Based Acoustic Tomography." Journal of Atmospheric and Oceanic Technology 34, no. 10 (October 2017): 2311–28. http://dx.doi.org/10.1175/jtech-d-17-0070.1.

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AbstractThe natural sound generated by an unmanned aerial vehicle is used in conjunction with tomography to remotely sense the virtual temperature and wind profiles of the atmosphere in a horizontal plane up to an altitude of 1200 m and over a baseline of 600 m. Sound fields recorded on board the aircraft and by an array of microphones on the ground are compared and converted to sound speed estimates for the ray paths intersecting the intervening medium. Tomographic inversion is then used to transform these sound speed values into two-dimensional profiles of virtual temperature and wind vector, which enables the atmosphere to be visualized and monitored over time. The wind vector and temperature estimates are compared to measurements taken by a collocated midrange Doppler sodar and sensors on board the aircraft. Large-eddy simulations of daytime atmospheric boundary layers and error models of the tomographic inversion and sodar are also used to assess the magnitude and nature of anticipated differences. Both the simulations and field trials data show similar levels of correspondence between the tomographically derived and independently observed measurements.

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Xu, Xiang-Yuan, Hao Ge, Jing Zhao, Zhi-Fei Chen, Jun Zhang, Ming-Hui Lu, Ming Bao, Yan-Feng Chen, and Xiao-Dong Li. "A monolithic three-dimensional thermal convective acoustic vector sensor with acoustic-transparent heat sink." JASA Express Letters 2, no. 4 (April 2022): 044001. http://dx.doi.org/10.1121/10.0010275.

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An acoustic vector sensor can directly detect acoustic particle velocity based on the measured temperature difference between closely spaced heated wires. For the detection of velocity in three dimensions, an integrated three-dimensional (3 D) sensor is desired, but it remains challenging in MEMS (Micro-Electro-Mechanical System) manufacturing. Here, a novel monolithic 3 D acoustic vector sensor is proposed, which is constructed using in-plane distributed wires assembled with acoustically transparent heat sink. The planar MEMS structure of the proposed sensor makes it easy to be fabricated and packaged. This work offers a new method for the design of acoustic vector sensors and other thermal convection-based MEMS sensors.

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Zhou, Chenzheng, Junbin Zang, Chenyang Xue, Yuexuan Ma, Xiaoqiang Hua, Rui Gao, Zengxing Zhang, Bo Li, and Zhidong Zhang. "Design of a Novel Medical Acoustic Sensor Based on MEMS Bionic Fish Ear Structure." Micromachines 13, no. 2 (January 22, 2022): 163. http://dx.doi.org/10.3390/mi13020163.

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High-performance medical acoustic sensors are essential in medical equipment and diagnosis. Commercially available medical acoustic sensors are capacitive and piezoelectric types. When they are used to detect heart sound signals, there is attenuation and distortion due to the sound transmission between different media. This paper proposes a new bionic acoustic sensor based on the fish ear structure. Through theoretical analysis and finite element simulation, the optimal parameters of the sensitive structure are determined. The sensor is fabricated using microelectromechanical systems (MEMS) technology, and is encapsulated in castor oil, which has an acoustic impedance close to the human body. An electroacoustic test platform is built to test the performance of the sensor. The results showed that the MEMS bionic sensor operated with a bandwidth of 20–2k Hz. Its linearity and frequency responses were better than the electret microphone. In addition, the sensor was tested for heart sound collection application to verify its effectiveness. The proposed sensor can be effectively used in clinical auscultation and has a high SNR.

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Raghukumar, Kaustubha, Grace Chang, Frank Spada, and Craig Jones. "A Vector Sensor-Based Acoustic Characterization System for Marine Renewable Energy." Journal of Marine Science and Engineering 8, no. 3 (March 10, 2020): 187. http://dx.doi.org/10.3390/jmse8030187.

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NoiseSpotter is a passive acoustic monitoring system that characterizes, classifies, and geo-locates anthropogenic and natural sounds in near real time. It was developed with the primary goal of supporting the evaluation of potential acoustic effects of offshore renewable energy projects. The system consists of a compact array of three acoustic vector sensors, which measures acoustic pressure and the three-dimensional particle velocity vector associated with the propagation of an acoustic wave, thereby inherently providing bearing information to an underwater source of sound. By utilizing an array of three vector sensors, the application of beamforming techniques can provide sound source localization, allowing for characterization of the acoustic signature of specific underwater acoustic sources. Here, performance characteristics of the system are presented, using data from controlled acoustic transmissions in a quiet environment and ambient noise measurements in an energetic tidal channel in the presence of non-acoustic flow noise. Data quality is demonstrated by the ability to reduce non-acoustic flow noise contamination, while system utility is shown by the ability to characterize and localize sources of sound in the underwater environment.

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Vandendriessche, Jurgen, Bruno da Silva, Lancelot Lhoest, An Braeken, and Abdellah Touhafi. "M3-AC: A Multi-Mode Multithread SoC FPGA Based Acoustic Camera." Electronics 10, no. 3 (January 29, 2021): 317. http://dx.doi.org/10.3390/electronics10030317.

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Acoustic cameras allow the visualization of sound sources using microphone arrays and beamforming techniques. The required computational power increases with the number of microphones in the array, the acoustic images resolution, and in particular, when targeting real-time. Such a constraint limits the use of acoustic cameras in many wireless sensor network applications (surveillance, industrial monitoring, etc.). In this paper, we propose a multi-mode System-on-Chip (SoC) Field-Programmable Gate Arrays (FPGA) architecture capable to satisfy the high computational demand while providing wireless communication for remote control and monitoring. This architecture produces real-time acoustic images of 240 × 180 resolution scalable to 640 × 480 by exploiting the multithreading capabilities of the hard-core processor. Furthermore, timing cost for different operational modes and for different resolutions are investigated to maintain a real time system under Wireless Sensor Networks constraints.

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López, Juan Manuel, Jesús Alonso, César Asensio, Ignacio Pavón, Luis Gascó, and Guillermo de Arcas. "A Digital Signal Processor Based Acoustic Sensor for Outdoor Noise Monitoring in Smart Cities." Sensors 20, no. 3 (January 22, 2020): 605. http://dx.doi.org/10.3390/s20030605.

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Presently, large cities have significant problems with noise pollution due to human activity. Transportation, economic activities, and leisure activities have an important impact on noise pollution. Acoustic noise monitoring must be done with equipment of high quality. Thus, long-term noise monitoring is a high-cost activity for administrations. For this reason, new alternative technological solutions are being used to reduce the costs of measurement instruments. This article presents a design for a versatile electronic device to measure outdoor noise. This device has been designed according to the technical standards for this type of instrument, which impose strict requirements on both the design and the quality of the device’s measurements. This instrument has been designed under the original equipment manufacturer (OEM) concept, so the microphone–electronics set can be used as a sensor that can be connected to any microprocessor-based device, and therefore can be easily attached to a monitoring network. To validate the instrument’s design, the device has been tested following the regulations of the calibration laboratories for sound level meters (SLM). These tests allowed us to evaluate the behavior of the electronics and the microphone, obtaining different results for these two elements. The results show that the electronics and algorithms implemented fully fit within the requirements of type 1 noise measurement instruments. However, the use of an electret microphone reduces the technical features of the designed instrument, which can only fully fit the requirements of type 2 noise measurement instruments. This situation shows that the microphone is a key element in this kind of instrument and an important element in the overall price. To test the instrument’s quality and show how it can be used for monitoring noise in smart wireless acoustic sensor networks, the designed equipment was connected to a commercial microprocessor board and inserted into the infrastructure of an existing outdoor monitoring network. This allowed us to deploy a low-cost sub-network in the city of Málaga (Spain) to analyze the noise of conflict areas due to high levels of leisure noise. The results obtained with this equipment are also shown. It has been verified that this equipment meets the similar requirements to those obtained for type 2 instruments for measuring outdoor noise. The designed equipment is a two-channel instrument, that simultaneously measures, in real time, 86 sound noise parameters for each channel, such as the equivalent continuous sound level (Leq) (with Z, C, and A frequency weighting), the peak level (with Z, C, and A frequency weighting), the maximum and minimum levels (with Z, C, and A frequency weighting), and the impulse, fast, and slow time weighting; seven percentiles (1%, 5%, 10%, 50%, 90%, 95%, and 99%); as well as continuous equivalent sound pressure levels in the one-third octave and octave frequency bands.

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Li, Xin Bo, Nan Nan Liu, Nan Jiang, Xiao Bo Long, and Xiao Yang Jiao. "Second-Order Statistics-Based Multi-Parameter Estimation of Near-Field Acoustic Sources." Applied Mechanics and Materials 461 (November 2013): 977–83. http://dx.doi.org/10.4028/www.scientific.net/amm.461.977.

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In this paper, a new approach based on the second-order statistics (SOS) and acoustic vector sensor (AVS) array is proposed, for localization estimation of near-field acoustic narrowband sources. Firstly, we choose the centrosymmetric uniform linear-array as the AVS arrangement, and the array is consistent with the coordinate axis direction of the acoustic vector-sensor. This estimation method makes good use of the acquisition information from the AVS, such as one-dimensional sound pressure and three-dimensional particle velocity, and has shown preferable performance for the parameter estimation of direction-of-arrival (DOA) and range of target acoustic sources in the near field. The estimation algorithm expands the near-ﬁeld array manifold of one single acoustic vector sensor to the acoustic vector-sensor’s uniform linear-array, and the near-ﬁeld acoustic vector sensor linear array output model is deduced. The autocorrelation and cross-correlation function of the velocity field and the pressure field are used to construct the rotational invariance frame, which helps to extract the expected information. Consequently, the closed-form solutions of the incident source’s DOA and range are derived explicitly through the parameter pairing operation. The proposed method reduces the computational burden and has good spatial recognition ability and high resolution in the case of limited array elements. It also has better engineering application prospect. Eventually, the performance of the method is verified by Monte Carlo simulation experiments.

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Febrina, Melany, Eko Satria, Mitra Djamal, Wahyu Srigutomo, and Martin Liess. "Acoustic CO2 Gas Sensor Based on Phase Difference Measurement." Journal of Science and Applicative Technology 5, no. 2 (October 5, 2021): 397. http://dx.doi.org/10.35472/jsat.v5i2.680.

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In this research, an acoustic sensor has been successfully built to measure the concentration of CO2 gas in a mixture of gases (N2 and CO2). The nitrogen and carbon dioxide gases used are ultra-high purity (UHP) gas. The measurement parameter used is the speed of sound by utilizing the phase shift between ultrasonic wave signals that are sent and received continuously. The acoustic method in this research is by using the speaker as an ultrasonic wave transmitter, and the microphone as an ultrasonic wave receiver emitted by the speaker on the gas medium. This acoustic phase shift method is very sensitive to be used to determine the speed of sound on a gas medium. From the sensor testing, the sensor has good linearity in detecting changes in CO2 concentration in the gas mixture. The sensor test results have been validated theoretically and obtained an RMS error of 3.36 (3.36% with a maximum concentration of 100%), this proves that the work of the sensor is in accordance with the theory. In addition to theoretical validation, the work of the sensor has also been validated by looking at the direct relationship between sensor input and output through the inverse function, and an RMS error of 3.51 (3.51% with a maximum concentration of 100%) is obtained. From the overall results obtained, the acoustic CO2 gas sensor that is built can detect changes in CO2 concentrations in the gas mixture accurately, fabrication of the sensor is easy to do, and the costs required in the manufacturing process are cheap.

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Sezen, A. S., S. Sivaramakrishnan, S. Hur, R. Rajamani, W. Robbins, and B. J. Nelson. "Passive Wireless MEMS Microphones for Biomedical Applications." Journal of Biomechanical Engineering 127, no. 6 (July 8, 2005): 1030–34. http://dx.doi.org/10.1115/1.2049330.

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This paper introduces passive wireless telemetry based operation for high frequency acoustic sensors. The focus is on the development, fabrication, and evaluation of wireless, batteryless SAW-IDT MEMS microphones for biomedical applications. Due to the absence of batteries, the developed sensors are small and as a result of the batch manufacturing strategy are inexpensive which enables their utilization as disposable sensors. A pulse modulated surface acoustic wave interdigital transducer (SAW-IDT) based sensing strategy has been formulated. The sensing strategy relies on detecting the ac component of the acoustic pressure signal only and does not require calibration. The proposed sensing strategy has been successfully implemented on an in-house fabricated SAW-IDT sensor and a variable capacitor which mimics the impedance change of a capacitive microphone. Wireless telemetry distances of up to 5 centimeters have been achieved. A silicon MEMS microphone which will be used with the SAW-IDT device is being microfabricated and tested. The complete passive wireless sensor package will include the MEMS microphone wire-bonded on the SAW substrate and interrogated through an on-board antenna. This work on acoustic sensors breaks new ground by introducing high frequency (i.e., audio frequencies) sensor measurement utilizing SAW-IDT sensors. The developed sensors can be used for wireless monitoring of body sounds in a number of different applications, including monitoring breathing sounds in apnea patients, monitoring chest sounds after cardiac surgery, and for feedback sensing in compression (HFCC) vests used for respiratory ventilation. Another promising application is monitoring chest sounds in neonatal care units where the miniature sensors will minimize discomfort for the newborns.

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Li, Tingting, and Xiukun Li. "Acoustic vector‐sensor array beamforming based on fourth‐order cumulants." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3334. http://dx.doi.org/10.1121/1.2933858.

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Guan, Quansheng, Fei Ji, Yun Liu, Hua Yu, and Weiqi Chen. "Distance-Vector-Based Opportunistic Routing for Underwater Acoustic Sensor Networks." IEEE Internet of Things Journal 6, no. 2 (April 2019): 3831–39. http://dx.doi.org/10.1109/jiot.2019.2891910.

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Chen, Yang, Guangyuan Zhang, Rui Wang, Hailong Rong, and Biao Yang. "Acoustic Vector Sensor Multi-Source Detection Based on Multimodal Fusion." Sensors 23, no. 3 (January 23, 2023): 1301. http://dx.doi.org/10.3390/s23031301.

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The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion.

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Haddad, Diego B., Markus V. S. Lima, Wallace A. Martins, Luiz W. P. Biscainho, Leonardo O. Nunes, and Bowon Lee. "Acoustic Sensor Self-Localization: Models and Recent Results." Wireless Communications and Mobile Computing 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/7972146.

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The wide availability of mobile devices with embedded microphones opens up opportunities for new applications based on acoustic sensor localization (ASL). Among them, this paper highlights mobile device self-localization relying exclusively on acoustic signals, but with previous knowledge of reference signals and source positions. The problem of finding the sensor position is stated as a function of estimated times-of-flight (TOFs) or time-differences-of-flight (TDOFs) from the sound sources to the target microphone, and the main practical issues involved in TOF estimation are discussed. Least-squares ASL solutions are introduced, followed by other strategies inspired by sound source localization solutions: steered-response power, which improves localization accuracy, and a new region-based search, which alleviates complexity. A set of complementary techniques for further improvement of TOF/TDOF estimates are reviewed: sliding windows, matching pursuit, and TOF selection. The paper proceeds with proposing a novel ASL method that combines most of the previous material, whose performance is assessed in a real-world example: in a typical lecture room, the method achieves accuracy better than 20 cm.

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Bonilla-Manrique, Oscar E., Julio E. Posada-Roman, Jose A. Garcia-Souto, and Marta Ruiz-Llata. "Sub-ppm-Level Ammonia Detection Using Photoacoustic Spectroscopy with an Optical Microphone Based on a Phase Interferometer." Sensors 19, no. 13 (June 29, 2019): 2890. http://dx.doi.org/10.3390/s19132890.

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A sensitive optical microphone for photoacoustic spectroscopy based on the common path topology of a fibre laser Doppler vibrometer (FLDV) using phase-generated carrier demodulation and a slim diaphragm as an acoustic wave transducer was demonstrated. A resonant gas cell was adapted to enhance gas-detection performance and simultaneously provide efficient cancellation of the window background acoustic signal. Ammonia (NH3) was selected as the target gas. The absorption line was experimentally identified using a distributed feedback laser diode emitting at 1530 nm. The linearity and sensitivity of the gas sensor were measured using wavelength modulation spectroscopy with second harmonic detection. A Teflon diaphragm was used to implement the optical microphone, along with the FLDV, showing a minimum detectable pressure of 79.5 μPa/Hz1/2. The noise-equivalent absorption sensitivity for NH3 detection at the absorption line at 1531.7 nm was 1.85 × 10−8 W cm−1 Hz−1/2, and the limit of detection was 785 ppbv.

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Bayrakli, Ismail, Hatice Akman, and Filiz Sari. "Sensor using a photo-acoustic absorption cell with two perpendicular acoustic resonators to analyze multiple molecules." Applied Optics 62, no. 25 (August 23, 2023): 6689. http://dx.doi.org/10.1364/ao.495411.

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An ultra-high sensitivity multi-molecule sensor based on a photo-acoustic cell with two perpendicular acoustic resonators and a common microphone has been reported. In this work, a 4.5 µm distributed-feedback quantum cascade laser and a 1.5 µm external cavity diode laser (EC-DL) were used as optical excitation sources. Considering the spectral ranges of the lasers used, it is possible to analyze eight molecules (QCL:N2O and CO2, EC-DL: H2O, H2S, NH3, CO, CH4, and C2H2). The N2O molecule was used to evaluate the performance of the photo-acoustic spectroscopy (PAS)-based sensor. A sensitivity of 0.073 V/ppm and a linearity of 0.99 were found by analyzing the PAS signal as a function of N2O concentration at 2237.656cm−1. The long-term performance of the sensor was determined by performing an Allan deviation analysis. A minimum detection limit of 9.8 ppb for 90 s integration time was achieved. The simultaneous multi-trace gas detection capability was verified by measurement of N2O, CO2, and H2O. Depending on the coarse/fine-tuning ranges of the lasers used, the number of molecules analyzed can be further increased. Such a sensor could provide simultaneous diagnosis of many diseases through an analysis of breath air and simultaneous monitoring of the most important greenhouse gases.

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Zhou, Hao, and Wen Lin Huang. "Study on Vector Hydrophone Array DOA Estimation." Applied Mechanics and Materials 543-547 (March 2014): 2589–93. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2589.

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Vector hydrophone is composed of acoustic pressure sensor and particle velocity sensor, which can simultaneously measure acoustic pressure and orthogonal components of particle velocity. MUSIC (Multiple Signal Classification) algorithm is a high resolution spatial spectrum analysis method based on subspace decomposition. This paper introduces the operation principles of this algorithm in detail and investigates the application of MUSIC algorithm to the DOA estimation of acoustic sources by a vector-hydrophone ULA (Uniform Linear Array) output model. Simulation results indicate that the resolution capability of MUSIC algorithm under larger SNRs is excellent.

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Lauwers, Thomas, Alain Glière, and Skandar Basrour. "An all-Optical Photoacoustic Sensor for the Detection of Trace Gas." Sensors 20, no. 14 (July 16, 2020): 3967. http://dx.doi.org/10.3390/s20143967.

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A highly sensitive Fabry–Perot based transduction method is proposed as an all-optical alternative for the detection of trace gas by the photoacoustic spectroscopy technique. A lumped element model is firstly devised to help design the whole system and is successfully compared to finite element method simulations. The fabricated Fabry–Perot microphone consists in a hinged cantilever based diaphragm, processed by laser cutting, and directly assembled at the tip of an optical fiber. We find a high acoustic sensitivity of 630 mV/Pa and a state-of-the-art noise equivalent pressure, as low as ~ 2 μ Pa / Hz at resonance. For photoacoustic trace gas detection, the Fabry–Perot microphone is further embedded in a cylindrical multipass cell and shows an ultimate detection limit of 15 ppb of NO in nitrogen. The proposed optical trace gas sensor offers the advantages of high sensitivity and easy assembling, as well as the possibility of remote detection.

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Zhang, Lan-yue, and De-sen Yang. "Min-norm spatial spectrum based on acoustic vector-sensor linear array." Journal of Marine Science and Application 5, no. 3 (September 2006): 41–45. http://dx.doi.org/10.1007/s11804-006-0095-7.

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Khan, Md Abdullah Al Hafiz, Nirmalya Roy, and H. M. Sajjad Hossain. "Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments." Mobile Information Systems 2018 (2018): 1–21. http://dx.doi.org/10.1155/2018/4570182.

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Occupancy detection helps enable various emerging smart environment applications ranging from opportunistic HVAC (heating, ventilation, and air-conditioning) control, effective meeting management, healthy social gathering, and public event planning and organization. Ubiquitous availability of smartphones and wearable sensors with the users for almost 24 hours helps revitalize a multitude of novel applications. The inbuilt microphone sensor in smartphones plays as an inevitable enabler to help detect the number of people conversing with each other in an event or gathering. A large number of other sensors such as accelerometer and gyroscope help count the number of people based on other signals such as locomotive motion. In this work, we propose multimodal data fusion and deep learning approach relying on the smartphone’s microphone and accelerometer sensors to estimate occupancy. We first demonstrate a novel speaker estimation algorithm for people counting and extend the proposed model using deep nets for handling large-scale fluid scenarios with unlabeled acoustic signals. We augment our occupancy detection model with a magnetometer-dependent fingerprinting-based localization scheme to assimilate the volume of location-specific gathering. We also propose crowdsourcing techniques to annotate the semantic location of the occupant. We evaluate our approach in different contexts: conversational, silence, and mixed scenarios in the presence of 10 people. Our experimental results on real-life data traces in natural settings show that our cross-modal approach can achieve approximately 0.53 error count distance for occupancy detection accuracy on average.

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Bozzi, Fabricio A., and Sérgio M. Jesus. "Vector Sensor Steering-Dependent Performance in an Underwater Acoustic Communication Field Experiment." Sensors 22, no. 21 (October 30, 2022): 8332. http://dx.doi.org/10.3390/s22218332.

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This paper shows the performance resulting from combining vector sensor directional components in an underwater acoustic communication experiment. The objective is to relate performance with transmission direction and range. Receiver structures based on beamforming and passive time-reversal are tested in order to quantify and compare the steerability impact of vector sensor directional components. A shallow water experiment is carried out with a bottom fixed two-axis pressure-gradient vector sensor. A ship suspended acoustic source transmits coherent modulated communication signals at various ranges and from several directions. Results show that one vector sensor can provide an up to 10 times smaller error bit rate than a pressure sensor, favoring communication robustness without size penalty.

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Wang, Guibao, Xinkuan Wang, Lanmei Wang, and Xiangyu Wang. "Research on Ambiguity Resolution Algorithm by Quaternion Based on Acoustic Vector Sensor." Journal of Sensors 2020 (November 18, 2020): 1–8. http://dx.doi.org/10.1155/2020/2402489.

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The increase in element spacing can increase the aperture of the array and improve its resolution performance. However, phase ambiguity will occur when the array element interval is larger than the minimum half wavelength of the incident signal. The three acoustic velocity components of the acoustic vector are ingeniously constructed into a new kind of quaternions because of the special structure of the acoustic vector sensor array, and the rough estimation of the direction of arrival (DOA) is obtained using the rotation relationship between the subarray steering vectors corresponding to quaternion data. The rough estimate is used to resolve the phase ambiguity of the spatial phase difference between the array elements, and the high-precision DOA estimation of the signal can be obtained. Simulation results show that the method is effective.

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Zhang, Xiao Ping, and Yang Wang. "Novel Acoustic Source Localization Method in WSN Based on LSSVR Regression Modeling." Advanced Materials Research 468-471 (February 2012): 2296–303. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.2296.

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To solve the problem of acoustic source localization in wireless sensor networks (WSN) under interference of environmental noise, a novel acoustic source localization method in WSN based on Least Square Support Vector Regression (LSSVR) modeling (ASL-LRM) was proposed. The ideal measured values of acoustic sensors were used to compose feature vector at first. Then LSSVR models were built by LSSVR modeling on the mapping relation between feature vector and acoustic source coordinate. The acoustic source was then located by inputting feature vector composed of real measured values of the sensors into LSSVR models. The modeling parameters optimization method based on localization effect in sample locations was also discussed. Experiments were performed in 100 test locations. RMSE values by ASL-LRM method in 72-76 test locations were less than MLE method and reduced by 60%-74% at most. In lower signal-to-noise ratio case, there were 87 test locations where RMSE values by ASL-LRM method were less than 2 meters, while there were only 12 test locations by MLE method. It shows ASL-LRM method achieves better localization effects in a large part of the region surrounded by sensor nodes. It especially has advantage on the occasions like lower signal-to-noise ratio or high precision localization.

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Safizadeh, M. S., and A. Golmohammadi. "Ball bearing fault detection via multi-sensor data fusion with accelerometer and microphone." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 3 (March 1, 2021): 168–75. http://dx.doi.org/10.1784/insi.2021.63.3.168.

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Early detection of defects in bearings is essential to avoid the complete failure of machinery and the associated costs. This study presents a novel method for fault diagnosis of bearings using sensor fusion with a microphone and an accelerometer. The system has five modules, namely data acquisition, signal processing, feature extraction, classification and decision-making. A test-rig is designed to collect acoustic and vibration signals. Then, for each signal, indices are calculated in the time and frequency domains. After using principal component analysis (PCA) for feature extraction, the k-nearest neighbours (kNN) method is used in the classification module. Finally, a decision on the kind of fault and its size is made based on the decision fusion module. The aim of this study is to propose a fusion method to improve the effectiveness and reliability of bearing defect diagnosis compared to what can be achieved with vibration or acoustic measurements alone. The results obtained from this preliminary study show that condition monitoring using the accelerometer is the more effective technique for determining the type of fault, while the microphone is effective for classifying the size of fault. Experimental results also confirm these findings.

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Romashko, R. V., D. V. Storozhenko, M. N. Bezruk, D. A. Bobruyko, and Y. N. Kulchin. "Fiber-optic vector acoustic receiver based on adaptive holographic interferometer." Laser Physics 32, no. 2 (January 4, 2022): 025101. http://dx.doi.org/10.1088/1555-6611/ac44a4.

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Abstract A mobile scalar–vector acoustic receiver is proposed, experimentally implemented and investigated. The key components of the receiver are (a) the six-channel fiber-optic coil-type sensor configured as to detect three projections of acoustic intensity vector, (b) the six-channel optical phase demodulator based on six-channel adaptive holographic interferometer configured with use of dynamic holograms multiplexed in a photorefractive crystal of cadmium telluride and (c) the signals recording ADC-based system combined with software package for data processing. Field tests of the developed receiver applied for obtaining scalar and vector parameters of acoustic waves generated by a stationary and moving acoustic source in open air and water area are carried out. Experimental results show perceptiveness of use of the fiber-optical adaptive interferometry system for bearing of weak acoustic sources in real conditions.

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Zhang, Yao, Jin Fu, and Guannan Li. "A Novel Self-Calibration Method for Acoustic Vector Sensor." Mathematical Problems in Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/1219670.

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The acoustic vector sensor (AVS) can measure the acoustic pressure field’s spatial gradient, so it has directionality. But its channels may have nonideal gain/phase responses, which will severely degrade its performance in finding source direction. To solve this problem, in this study, a self-calibration algorithm based on all-phase FFT spectrum analysis is proposed. This method is “self-calibrated” because prior knowledge of the training signal’s arrival angle is not required. By measuring signals from different directions, the initial phase can be achieved by taking the all-phase FFT transform to each channel. We use the amplitude of the main spectrum peak of every channel in different direction to formulate an equation; the amplitude gain estimates can be achieved by solving this equation. In order to get better estimation accuracy, bearing difference of different training signals should be larger than a threshold, which is related to SNR. Finally, the reference signal’s direction of arrival can be estimated. This method is easy to implement and has advantage in accuracy and antinoise. The efficacy of this proposed scheme is verified with simulation results.

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Posatskiy, A. O., and T. Chau. "Design and evaluation of a novel microphone-based mechanomyography sensor with cylindrical and conical acoustic chambers." Medical Engineering & Physics 34, no. 8 (October 2012): 1184–90. http://dx.doi.org/10.1016/j.medengphy.2011.12.007.

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

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