Road to Acquisition: Preparing a MEMS Microphone Array for Measurement of Fuselage Surface Pressure Fluctuations

Preparing and pre-testing experimental setups for flight tests is a lengthy but necessary task. One part of this preparation is comparing newly available measurement technology with proven setups. In our case, we wanted to compare acoustic Micro-Electro-Mechanical Systems (MEMS) to large and proven surface-mounted condenser microphones. The task started with the comparison of spectra in low-speed wind tunnel environments. After successful completion, the challenge was increased to similar comparisons in a transonic wind tunnel. The final goal of performing in-flight measurements on the outside fuselage of a twin-engine turboprop aircraft was eventually achieved using a slim array of 45 MEMS microphones with additional large microphones installed on the same carrier to drawn on for comparison. Finally, the array arrangement of MEMS microphones allowed for a complex study of fuselage surface pressure fluctuations in the wavenumber domain. The study indicates that MEMS microphones are an inexpensive alternative to conventional microphones with increased potential for spatially high-resolved measurements even at challenging experimental conditions during flight tests.

Readout Circuits for Capacitive Sensors

The development of microelectromechanical system (MEMS) processes enables the integration of capacitive sensors into silicon integrated circuits. These sensors have been gaining considerable attention as a solution for mobile and internet of things (IoT) devices because of their low power consumption. In this study, we introduce the operating principle of representative capacitive sensors and discuss the major technical challenges, solutions, and future tasks for a capacitive readout system. The signal-to-noise ratio (SNR) is the most important performance parameter for a sensor system that measures changes in physical quantities; in addition, power consumption is another important factor because of the characteristics of mobile and IoT devices. Signal power degradation and noise, which degrade the SNR in the sensor readout system, are analyzed; circuit design approaches for degradation prevention are discussed. Further, we discuss the previous efforts and existing studies that focus on low power consumption. We present detailed circuit techniques and illustrate their effectiveness in suppressing signal power degradation and achieving lower noise levels via application to a design example of an actual MEMS microphone readout system.

FPGA-Based High-Performance Phonocardiography System for Extraction of Cardiac Sound Components Using Inverse Delayed Neuron Model

The study focuses on the extraction of cardiac sound components using a multi-channel micro-electromechanical system (MEMS) microphone-based phonocardiography system. The proposed multi-channel phonocardiography system classifies the cardiac sound components using artificial neural networks (ANNs) and synaptic weights that are calculated using the inverse delayed (ID) function model of the neuron. The proposed ANN model was simulated in MATLABR and implemented in a field-programmable gate array (FPGA). The proposed system examined both abnormal and normal samples collected from 30 patients. Experimental results revealed a good sensitivity of 99.1% and an accuracy of 0.9.

An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array

Currently, the detection of targets using drone-mounted imaging equipment is a very useful technique and is being utilized in many areas. In this study, we focus on acoustic signal detection with a drone detecting targets where sounds occur, unlike image-based detection. We implement a system in which a drone detects acoustic sources above the ground by applying a phase difference microphone array technique. Localization methods of acoustic sources are based on beamforming methods. The background and self-induced noise that is generated when a drone flies reduces the signal-to-noise ratio for detecting acoustic signals of interest, making it difficult to analyze signal characteristics. Furthermore, the strongly correlated noise, generated when a propeller rotates, acts as a factor that degrades the noise source direction of arrival estimation performance of the beamforming method. Spectral reduction methods have been effective in reducing noise by adjusting to specific frequencies in acoustically very harsh situations where drones are always exposed to their own noise. Since the direction of arrival of acoustic sources estimated from the beamforming method is based on the drone’s body frame coordinate system, we implement a method to estimate acoustic sources above the ground by fusing flight information output from the drone’s flight navigation system. The proposed method for estimating acoustic sources above the ground is experimentally validated by a drone equipped with a 32-channel time-synchronized MEMS microphone array. Additionally, the verification of the sound source location detection method was limited to the explosion sound generated from the fireworks. We confirm that the acoustic source location can be detected with an error performance of approximately 10 degrees of azimuth and elevation at the ground distance of about 150 m between the drone and the explosion location.

MEMS microphone intensity array for cabin noise measurements

Aircraft cabin noise measurements in flight are used toto quantify the noise level, and to identify the entry point of acoustic energy into the cabin. Sound intensity probes are the state-of-the-art measurement technique for this task. During measurements, additional sound absorbing material is used to ease the rather harsh acoustic measurement environment inside the cabin. In order to decrease the expensive in-flight measurement time, an intensity array approach was chosen. This intensity probe consists of 512 MEMS-Microphones. Depending on the frequency, these microphones can be combined as an array of hundreds of 3D- intensity probes. The acoustic velocity is estimated using a high order 3D finite difference stencil. At low frequencies, a larger spacing is used to reduce the requirement of accurate phase match of the microphone sensors. Measurements were conducted in the ground-based Dornier 728 cabin noise simulation as well as in-flight.

MEMS digital microphone and Arduino compatible microcontroller: an embedded system for noise monitoring

Noise assessment and monitoring are essential parts of an acoustician's work since it helps to understand the environment and propose better solutions for noise control and urban noise management. Traditionally, equipment to carry out this task is standardized, and, eventually, expensive for the early career professional. This work develops a high-quality (and cost-effective) prototype for an embedded noise monitoring device based upon a digital I2S MEMS microphone and an Arduino compatible microcontroller, named Teensy. Its small size and low power consumption are also advantages designed for the project. The system captures and processes sound in real-time, computes A and C frequency-weighted equivalent sound levels, along with time-weighted instant levels with a logging interval of 125 ms. Part of the software handles the audio environment, while the biquadratic IIR filters present in the Cortex Microcontroller library are responsible for the frequency- and time-weightings - using floating-point for enhanced precision. The prototype results were compared against a Class 2 Sound Level Meter, rendering very similar results for the tested situations, proving a powerful and reliable tool. Improvements and further testing are also being conducted to refine its functioning and characterization. Ultimately, the prototype achieved promising performance, confirming as a solution for noise monitoring.

Applicability of MEMS microphones for environmental sound level monitoring

This paper explores the challenges associated with the integration of MEMS microphone technology into IEC 61672 classified or type-approved environmental sound level monitors. A comparison is drawn between MEMS microphones and electret condenser capsule microphones to highlight key performance differences within the technologies, and a basic integration method for both technologies is suggested. A review of the IEC 61672 and type-approval standards is conducted against the suggested integration method for a MEMS microphone; key shortcomings are reported and objectively reviewed. Development trends for MEMS microphones are explored, providing key insights into the progression of the technology against electret condenser capsule microphones. Furthermore, the evolution of environmental sound level monitoring systems is explored with a key focus on networked and sound localisation technology. The importance of MEMS microphones within the evolution of environmental sound level monitoring systems is presented alongside key arguments for the practical suitability of MEMS technology over electret condenser capsule technology.