Evaluating a Human Ear-Inspired Sound Pressure Amplification Structure with Fabry–Perot Acoustic Sensor Using Graphene Diaphragm

In order to enhance the sensitivity of a Fabry–Perot (F-P) acoustic sensor without the need of fabricating complicated structures of the acoustic-sensitive diaphragm, a mini-type external sound pressure amplification structure (SPAS) with double 10 μm thickness E-shaped diaphragms of different sizes interconnected with a 5 mm length tapered circular rod was developed based on the acoustic sensitive mechanism of the ossicular chain in the human middle ear. The influence of thickness and Young’s modulus of the two diaphragms with the diameters of 15 mm and 3 mm, respectively, on the amplification ratio and frequency response were investigated via COMSOL acoustic field simulation, thereby confirming the dominated effect. Then, three kinds of dual-diaphragm schemes relating to steel and thermoplastic polyurethanes (TPU) materials were introduced to fabricate the corresponding SPASs. The acoustic test showed that the first scheme achieved a high resonant response frequency with lower acoustic amplification due to strong equivalent stiffness; in contrast, the second scheme offered a high acoustic amplification but reduced frequency range. As a result of sensitivity enhancement, adapted with the steel/TPU diaphragm structure, an optical fiber Fabry–Perot sensor using a multilayer graphene diaphragm with a diameter of 125 μm demonstrated a remarkable sensitivity of 565.3 mV/Pa @1.2 kHz due to the amplification ratio of up to ~29.9 in the range of 0.2–2.3 kHz, which can be further improved by miniaturizing structure dimension, along with the use of microstructure packaging technology.

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An Ear-Inspired Sound Pressure Amplification Structure for Fabry-Perot Acoustic Sensor

2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) ◽

10.1109/transducers50396.2021.9495434 ◽

2021 ◽

Author(s):

Xi Xiao ◽

Cheng Li ◽

Shangchun Fan ◽

Xuefeng Song

Keyword(s):

Sound Pressure ◽

Acoustic Sensor ◽

Pressure Amplification ◽

Fabry Perot

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Fiber-Optic Fabry–Pérot Acoustic Sensor With Multilayer Graphene Diaphragm

IEEE Photonics Technology Letters ◽

10.1109/lpt.2013.2256343 ◽

2013 ◽

Vol 25 (10) ◽

pp. 932-935 ◽

Cited By ~ 141

Author(s):

Jun Ma ◽

Haifeng Xuan ◽

Hoi Lut Ho ◽

Wei Jin ◽

Yuanhong Yang ◽

...

Keyword(s):

Fiber Optic ◽

Multilayer Graphene ◽

Acoustic Sensor ◽

Fabry Perot

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TWDM-Assisted Active Quadrature Demodulation of Fiber-Optic Fabry—Perot Acoustic Sensor Network

Journal of Lightwave Technology ◽

10.1109/jlt.2020.3043024 ◽

2020 ◽

pp. 1-1

Author(s):

Qiang Liu ◽

Ang Li ◽

Yueying Liu ◽

Zhenguo Jing ◽

Wei Peng

Keyword(s):

Sensor Network ◽

Fiber Optic ◽

Acoustic Sensor ◽

Quadrature Demodulation ◽

Fabry Perot

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Fabry–Perot Cavity Sensing Probe with High Thermal Stability for an Acoustic Sensor by Structure Compensation

Sensors ◽

10.3390/s18103393 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3393 ◽

Cited By ~ 4

Author(s):

Jin Cheng ◽

Yu Zhou ◽

Xiaoping Zou

Keyword(s):

Thermal Stability ◽

Thermal Expansion ◽

Cavity Length ◽

High Reliability ◽

Simple Approach ◽

High Thermal Stability ◽

Acoustic Sensor ◽

Practical Applications ◽

Fabry Perot ◽

Expansion Model

Fiber Fabry–Perot cavity sensing probes with high thermal stability for dynamic signal detection which are based on a new method of structure compensation by a proposed thermal expansion model, are presented here. The model reveals that the change of static cavity length with temperature only depends on the thermal expansion coefficient of the materials and the structure parameters. So, fiber Fabry–Perot cavity sensing probes with inherent temperature insensitivity can be obtained by structure compensation. To verify the method, detailed experiments were carried out. The experimental results reveal that the static cavity length of the fiber Fabry–Perot cavity sensing probe with structure compensation hardly changes in the temperature range of −20 to 60 °C and that the method is highly reproducible. Such a method provides a simple approach that allows the as-fabricated fiber Fabry–Perot cavity acoustic sensor to be used for practical applications, exhibiting the great advantages of its simple architecture and high reliability.

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Detection of small amplitude sound pressure by heterodyne fibre-optic underwater acoustic sensor

Optics & Laser Technology ◽

10.1016/0030-3992(93)90136-4 ◽

1993 ◽

Vol 25 (4) ◽

pp. 264

Author(s):

Sumio Takahashi ◽

Katsunori Okajima ◽

Osamu Sugiyama ◽

Koichi Hirama ◽

Masahiro Seya

Keyword(s):

Small Amplitude ◽

Sound Pressure ◽

Acoustic Sensor ◽

Fibre Optic ◽

Underwater Acoustic

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Analysis on Fourier characteristics of wavelength-scanned optical spectrum of low-finesse Fabry-Pérot acoustic sensor

Optics Express ◽

10.1364/oe.26.022064 ◽

2018 ◽

Vol 26 (17) ◽

pp. 22064 ◽

Cited By ~ 4

Author(s):

X. Fu ◽

P. Lu ◽

L. Zhang ◽

W. J. Ni ◽

D. M. Liu ◽

...

Keyword(s):

Optical Spectrum ◽

Acoustic Sensor ◽

Fabry Perot

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Flywheel-like diaphragm-based fiber-optic Fabry–Perot frequency tailored acoustic sensor

Journal of Physics D Applied Physics ◽

10.1088/1361-6463/ab95bc ◽

2020 ◽

Vol 53 (41) ◽

pp. 415102

Author(s):

Xiaoguang Qi ◽

Shuang Wang ◽

Junfeng Jiang ◽

Kun Liu ◽

Peng Zhang ◽

...

Keyword(s):

Fiber Optic ◽

Acoustic Sensor ◽

Fabry Perot

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Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network

Sensors ◽

10.3390/s20030903 ◽

2020 ◽

Vol 20 (3) ◽

pp. 903 ◽

Cited By ~ 3

Author(s):

Juan M. Navarro ◽

Raquel Martínez-España ◽

Andrés Bueno-Crespo ◽

Ramón Martínez ◽

José M. Cecilia

Keyword(s):

Neural Network ◽

Sensor Network ◽

Sound Pressure Level ◽

Deep Neural Network ◽

Sound Pressure ◽

Noise Pollution ◽

Pressure Level ◽

Acoustic Sensor ◽

Short Term ◽

Sound Levels

Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory.

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