MEMS Microphone Array Research Articles

Development of low-cost MEMS microphone array for sound localization in urban environments

Understanding noise pollution and mitigating its impact on the quality of life in urban environment is a major challenge from a societal and health point of view. In order to monitor and ultimately understand these noisy environments a process of long term acoustic measurements and analysis is required. These urban environments are noisy and obtaining a clear recording of a desired acoustic signal is difficult hence an effective approach is the use of beamforming theory coupled with a microphone array. In this paper a novel approach utilizing compact low-cost microphone arrays equipped with MEMS sensors for continuous sound localization in urban areas is proposed. This approach focuses on leveraging the affordability and scalability of MEMS sensors to deploy distributed acoustic sensor networks across diverse urban locations. The aim is to capture real-time acoustic data, enabling comprehensive monitoring of sound sources and their spatial distribution within the urban environment. This sensor unit can be upgraded with a camera, i.e. acoustic camera, which will provide information on the conditions of the environment where the measurements are performed. This low-cost solution facilitates long-term monitoring, providing valuable insights into the dynamic nature of urban soundscapes.

AI-Enhanced Edge Device for Real-Time Snoring Detection

This paper presents the development of a cutting-edge, non-invasive edge device designed to monitor snoring and provide timely, moderate haptic feedback to users. Utilizing the Qualcomm Snapdragon 8cx Gen 3 processor, the device offers robust computing power and AI capabilities for real-time processing, making it a versatile tool for health monitoring applications. The system integrates a high-fidelity MEMS microphone array capable of capturing nuanced audio signals and a TDK piezoelectric haptic actuator, which delivers precise alerts through customized vibrations. The research explores the potential of this advanced hardware in detecting and managing obstructive sleep apnea (OSA), a condition often underdiagnosed due to a lack of patient awareness. By leveraging state-of-the-art digital signal processing and deep learning techniques, the device aims to enhance user awareness and intervention in sleep-related disorders, offering a promising new avenue for improving patient outcomes and quality of life.

Auralization and Visualization for Infrastructure Planning in the Joint Project "EAV-Infra"

Within the joint research project "Real-time Calculation, Auralization and Visualization of Sound Propagation and Noise Protection Measures for Infrastructure Projects" (EAV-Infra), funded by the Federal Ministry for Economic Affairs, the potentials of digital infrastructure planning based on Building Information Modeling (BIM) and the auralization of different states of planning will be combined. In this contribution, the objectives of the project will be presented with particular focus on the auralization. Currently, the effectiveness of noise mitigation measures like acoustic barriers or rail dampers is evaluated and communicated mostly by means of SPL values. Through auralization, a more vivid impression of different states of planning can be given during the whole planning process. In EAV-Infra, the auralization will be based on audio data of partial sound sources taken from real train passings. The data will be collected at various sites using a newly developed MEMS microphone array. To achieve a realistic hearing impression at the receiver position, a suitable model of the sound propagation will be implemented, which allows for a simulation in real time. In a last step, the partial sound source's signals will be convolved with the calculated sound propagation to synthesize the signal at the receiver position.

Real-time, adaptive microphone subset selection for a 128-elementarray

Some advantages of high-sensor count microphone arrays include gain over random noise, increased multiple-source resolution, and potential increases in effective frequency bandwidth. But in real-time applications, computational capabilities of embedded computing hardware might limit the number data channels that can be processed without data loss. For example, given a specific processing pipeline and sampling rate, perhaps only 24 of 128 channels can be supported in real-time. Therefore, the question of which 24-element subset of the 128 possible elements should be being used to detect and track multiple moving acoustic sources. In this presentation, principles of genetic search algorithms are used to develop a method to adaptively select optimal subsets of sensors as data is being processed. Discussion concerning optimality criteria, types of genetic algorithms, and algorithm performance will be presented as well as results corresponding to a 3-D 128-element array of digital MEMS microphones when multiple types of moving acoustic sources were nearby.

Acoustic detection and localisation system for Hylotrupes bajulus L. larvae using a MEMS microphone array

Acoustic detection and localisation system for Hylotrupes bajulus L. larvae using a MEMS microphone array

MEMS microphone arrays for quench detection in superconducting cables

An acoustic array is proposed as a quench detection method in superconducting magnets. A quench occurs when the current density in the superconductor exceeds a critical value, resulting in a loss of superconductivity and rapid local heating. This event is destructive and must be rapidly detected. It is thought that the quench may act as an acoustic source (Takayasu, 2019), which could be detected and localized by a microphone array inserted into the cryogenic coolant. A main advantage of this method is that acoustics propagate 1000 times faster than the normal zone propagation velocity in HTS conductors, providing for fast detection times. To demonstrate this concept, we first characterized the performance of a piezoelectric MEMS microphone and several potential preamplifiers under cryogenic conditions. An acoustic sense node was then constructed that operates down to 10 K. A cryogenic probe incorporating the MEMS array was used to study a quench event in a segment of a 2 mm wide REBCO tape. Quench experiments were carried out in a 7.6 cm diameter, 111 cm tall cryostat in Helium gas at 20 to 50 K. The MEMS array clearly detects a quench induced failure. Other observed acoustic features of unknown origin will be described.

Combining remote sensing and artificial intelligence to locate sounds

Smart cities use sensors to collect and analyze data to manage their resources more efficiently and thereby enhance the quality of life for residents, especially in densely populated cities. They monitor a wide range of information, including pollution, traffic, and parking. Urban environments feature large numbers of sounds, such as noise pollution and other specific sounds, which can be harmful to citizens. Therefore, this unrestrained growing issue of urban sounds should be addressed by smart cities to improve noise mitigation. To sidestep that problem, the “Listening system Using a Crow’s nest arraY” (LUCY) is currently in development at Fraunhofer FKIE. The acoustic system aims are to automatically detect meaningful audio events contained in noisy data, such as impulsive sounds, and to accurately estimate their geographic locations. To accomplish these tasks in near real-time, LUCY has to combine advanced array processing techniques, including beamforming, with artificial intelligence methods, such as deep learning using spectro-temporal features. The proposed acoustic system is a low-cost, small and lightweight system. It consists of a peculiar volumetric array of tiny MEMS microphones, called the “Crow’s Nest Array” (CNA), which has a crucial influence on the accuracy of the sound localization estimation, and a miniature computer to process methods including sound localization calculation. Due to its small size, LUCY can easily be deployed on numerous types of platforms, including Unmanned Aerial Vehicles (UAVs).

Design of a compact omnidirectional sound camera using the three-dimensional acoustic intensimetry

By using the 3D intensimetry algorithm, a sensor probe has a significant advantage at low frequencies, allowing for a compact array cluster that is far smaller than a wavelength. A super-compact 3D sound camera is designed to estimate the direction of arrival by calculating the three-dimensional intensity vectors based on the measured pressure data. An array of flush-mounted MEMS microphones is configured over the spherical surface of a commercial omnidirectional camera with a diameter of 38 mm. Implementing the operation over a wide frequency range requires the scattering caused by the microphone holder and the irregularity of the array’s directional response to be minimized. Although the spherical scattering reduces the effective upper-bound frequency by two thirds, a truncated stellated-octahedral array using five microphones can significantly reduce the spatial bias error at high frequencies. The test results in an anechoic chamber show an average localization error of 3.2° for human voices. The results in a reverberant room with T30 = 0.66 s reveal an average bearing angle error of 6.5° when a source is positioned within two times the critical distance in the interior space. In such a live room, it is demonstrated that the speakers wearing the face masks can be localized in real-time.

2Pa2-4 A Narrow Pitch Matrix-Type MEMS Microphone Array for Acoustic Localization in Near-Field

2Pa2-4 A Narrow Pitch Matrix-Type MEMS Microphone Array for Acoustic Localization in Near-Field

Cryogenic testing of microphones and preamplifiers for MEMS quench detection in superconducting magnets

High Temperature Superconducting (HTS) tapes are needed for high field magnets in high-energy physics and nuclear fusion. However, quench events, in which the superconductor loses its superconductivity, can cause significant and costly damage to the device if allowed to propagate. Consequently, rapid quench detection methods are crucial. Existing methods such as voltage taps can be less effective in HTS applications, as the normal zone propagation velocities of HTS devices are slow, drastically increasing quench detection time (Iwasa, Cryogenics, 2003). We propose an alternative method that utilizes a linear array of MEMS microphones embedded in the cryogenic coolant channel (Takayasu, IEEE Transactions on Applied Superconductivity, 2019). The system requires amplifiers and acoustic sensors capable of operation at cryogenic temperatures as low as 4.2 K, although operation at 20 K may be sufficient. We report on cryogenic testing of the Vesper microphone preamplifier ASIC, as well as opamp based preamplifiers using the OPA838, LT1464, AD8591, and AD8057 ICs. The amplifiers are being used with the Vesper VM4 MEMS microphone to achieve acoustic measurements in gaseous helium at temperatures as low as 8 K. A charge amp scheme using the AD8591 achieved the best results to date. [Support from DOE STTR DE-SC0019905.]

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.

Non-linear beamformer with long short-term memory network

Acoustic beamforming with a microphone array enables spatial filtering in a wide frequency range. It is a challenging issue to sharpen the main-lobe in the lower frequency region with a small-scale microphone array, of which the number and spacing of microphones are small. A neural network-based non-linear beamformer achieves a breakthrough in sharpening the main-lobe. The non-linear beamforming works well for the narrowband signals but is weak in wideband beamforming. The non-linear beamforming with the long short-term memory is proposed to deal with wideband speech signals. The long short-term memory network is trained in the recurrent neural network architecture with the sequence of audio data such as speech signals. The performance of the proposed beamformer is confirmed using a small-scale 8-ch MEMS microphone array, where eight microphones are linearly arranged with the neighboring spacing of 10 mm, under a real environment. The beam-pattern of the proposed non-linear beamformer succeeds in sharpening the main-lobe although the linear delay-and-sum beamformer could not achieve frequency selectivity. The feasibility of the proposed beamformer is also confirmed in speech enhancement.

Feasibility of Using a MEMS Microphone Array for Pedestrian Detection in an Autonomous Emergency Braking System.

Pedestrian detection by a car is typically performed using camera, LIDAR, or RADAR-based systems. The first two systems, based on the propagation of light, do not work in foggy or poor visibility environments, and the latter are expensive and the probability associated with their ability to detect people is low. It is necessary to develop systems that are not based on light propagation, with reduced cost and with a high detection probability for pedestrians. This work presents a new sensor that satisfies these three requirements. An active sound system, with a sensor based on a 2D array of MEMS microphones, working in the 14 kHz to 21 kHz band, has been developed. The architecture of the system is based on an FPGA and a multicore processor that allow the system to operate in real time. The algorithms developed are based on a beamformer, range and lane filters, and a CFAR (Constant False Alarm Rate) detector. In this work, tests have been carried out with different people and in different ranges, calculating, in each case and globally, the Detection Probability and the False Alarm Probability of the system. The results obtained verify that the developed system allows the detection and estimation of the position of pedestrians, ensuring that a vehicle travelling at up to 50 km/h can stop and avoid a collision.

Bearing estimation of screams using a volumetric microphone array mounted on a UAV

The use of Unmanned Aerial Vehicles (UAVs), also called drones, is increasing for emergency and rescue operations. UAVs can be equipped with state-of-the-art technology to enable surveillance and provide quick situational awareness. Considering that UAVs better reach inaccessible and larger areas than other types of vehicles, such as Unmanned Ground Vehicles (UGVs), they could be used to locate people during catastrophes, and support the rescue team. For this purpose, an automatic and accurate detection, bearing estimation, and source localization system focused on specific audio events, such as persons’ screams, is being developed at Fraunhofer FKIE. The solve those tasks, the system uses a particular volumetric array of MEMS microphones, called “Crow’s Nest Array,” combined with advanced array processing techniques, such as beamforming. The spatial distribution and number of microphones in arrays have a crucial influence on the bearing estimation of audio events; therefore, in this paper, microphone array configurations are described, and their performance in different open field experiments is presented.

Spectral analysis of turbulent boundary layer pressure fluctuations collected by a MEMS microphone array

Spectral analysis of turbulent boundary layer pressure fluctuations collected by a MEMS microphone array

Cryogenic testing of MEMS microphones towards their utilization as a quench detection method in superconducting magnets

High Temperature Superconducting (HTS) tapes such as Rare Earth Barium Copper Oxide (REBCO) are an attractive option for high field magnets operated in cryogenic fluids. However, quench events can occur in which the conductor locally loses its superconducting properties. It is critical to rapidly detect and respond to such an event. Conventional quench detection methods using voltage taps are difficult in HTS devices, since the normal zone propagation velocities are 2–3 orders of magnitude lower in HTS compared to low temperature superconductors (Iwasa, Cryogenics, 2003). We propose an alternative in which a linear array of MEMS microphones is distributed down the central cooling channel of a cable in-conduit conductor. The array can detect the acoustic signature caused by a quench event which propagates in the cooling fluid. The proposed method differs from Acoustic Emission (AE) detection, which uses sensors mounted on the magnet surface to detect structural vibration (Tsukamoto, Appl. Phys. Lett., 1981). In order to implement the system, MEMS microphones and preamplifiers must operate in cryogenic fluids. We report on characterization of commercial MEMS microphones in cryogenic gaseous helium between 0.5 and 1.5 bar down to 20 K, and in liquid nitrogen at 1 bar and 77 K.

Development of an MEMS ultrasonic microphone array system and its application to compressed wavefield imaging of concrete

Although contactless ultrasonic wavefield imaging shows potential for effective nondestructive inspection of various engineering materials, it has been rarely applied to concrete materials owing to technical challenges including low signal-to-noise ratio (SNR) caused by inherent heterogeneity of concrete. This paper presents development of a multi-channel MEMS ultrasonic microphone array system and its application to compressed wavefield imaging of concrete materials. The developed multi-channel MEMS ultrasonic microphone array system contains eight MEMS ultrasonic microphone elements and a signal conditioning circuit that enables measurements of ultrasonic signals with high SNR. A compressed sensing approach, based on the multiple measurement vector (MMV) concept, is applied to reconstruct a full dense ultrasonic wavefield data from sparsely sampled ultrasonic wavefield data. Experiments are carried out on a laboratory concrete sample to verify the performance of the developed MEMS microphone array system and proposed compressed sensing approach and then large-scale concrete samples to demonstrate practical application. The experimental results demonstrate that the developed MEMS microphone array system provides high-quality (SNR > 20 dB) ultrasonic data collected from concrete elements; furthermore, the proposed compressed sensing approach provides accurate reconstruction of dense wavefield data, as determined by peak signal-to-noise ratio (PSNR), from sparsely measured wavefield data with compression ratios up to 85% and PSNR above 25 dB in data collected form realistic large-scale concrete samples. By combining the MEMS array system and compressed sensing approach, the total ultrasonic data acquisition time needed to produce dense wavefield data can be significantly reduced.

Comparison of Methodologies for the Detection of Multiple Failures Using Acoustic Images in Fan Matrices

This paper presents the comparison of three methodologies to detect if some fans in a matrix are not working properly. These methodologies are based on detecting fan failures by analysing acoustic images of the fan matrix, obtained using a planar array of MEMS microphones. Geometrical parameters of these acoustic images for different frequencies are then used to train a support vector machine (SVM) classifier, in order to detect the fan failures. One of the methodologies is based on the detection of the faulty fan in the matrix, under the hypothesis that only one fan can fail. Other methodology is based on the detection of the specific working situation of the matrix. And finally, the third methodology that is compared is based on determining individually if each of the fans of the matrix is working properly or not. The comparison shows that this third methodology is the most reliable.

Acoustic Enhanced Camera Tracking System Based on Small-Aperture MEMS Microphone Array

The camera tracking systems based on visual image processing face a problem that they are completely ineffective in their blind zones. To address this problem, a design of acoustic enhanced tracking system combining visual and auditory target tracking methods is reported in this article. The system holds the abilities of performing sound direction estimation and target tracking in real-time. Estimating direction of arrival of the sound accompanied with the target helps the camera turn towards the target outside the field of view. This sound-triggered mode of camera operation makes a significant supplement to conventional cameras' working state. Considering the embedded system is necessary in consideration of the cost and size of the system in practical application, we designed a small aperture array with 7 digital omnidirectional MEMS microphones and built the overall system based on FPGA and ARM. The experiments were carried out in a normal indoor environment and the results confirmed that the system can perform auditory and visual tracking in real-time.

Design Exploration and Performance Strategies towards Power-Efficient FPGA-Based Architectures for Sound Source Localization

Many applications rely on MEMS microphone arrays for locating sound sources prior to their execution. Those applications not only are executed under real-time constraints but also are often embedded on low-power devices. These environments become challenging when increasing the number of microphones or requiring dynamic responses. Field-Programmable Gate Arrays (FPGAs) are usually chosen due to their flexibility and computational power. This work intends to guide the design of reconfigurable acoustic beamforming architectures, which are not only able to accurately determine the sound Direction-Of-Arrival (DoA) but also capable to satisfy the most demanding applications in terms of power efficiency. Design considerations of the required operations performing the sound location are discussed and analysed in order to facilitate the elaboration of reconfigurable acoustic beamforming architectures. Performance strategies are proposed and evaluated based on the characteristics of the presented architecture. This power-efficient architecture is compared to a different architecture prioritizing performance in order to reveal the unavoidable design trade-offs.