Photoacoustic spectroscopy (PAS) is a powerful technique for selective gas detection; however, its performance in non-resonant configurations is fundamentally constrained by the poor low-frequency response of conventional acoustic detectors. Commercial MEMS microphones, although compact and cost effective, exhibit limited infrasound sensitivity, which restricts the development of truly miniaturised and broadband PAS systems. To address this limitation, we present a novel MEMS fluidic microphone (f-mic) that operates on a thermal sensing principle and is explicitly optimised for the infrasound regime. The sensor demonstrates a constant sensitivity of 32 μV/Pa for frequencies below 20 Hz. A detailed analytical model incorporating frequency-dependent effects is developed to identify and investigate the critical design parameters that influence system performance. The overall system exhibits a band-pass frequency response, enabling broadband operation. Based on these insights, a miniaturised photoacoustic cell is fabricated, ensuring efficient optical coupling and f-mic integration. Experimental validation using a CO2-targeted laser system demonstrates a linear response up to 5000 ppm, a sensitivity of 6 nV/ppm, and a theoretical detection limit of 300 ppb over 100 s, resulting in an NNEA of W cm−1 Hz−0.5. These results establish the f-mic as a robust, scalable solution for non-resonant PAS, effectively overcoming a significant bottleneck in compact gas sensing technologies.

This study presents a waveguide ring-array photoacoustic cell (PAC) module, utilizing MEMS microphones as acoustic sensors and based on the multimicrophone signal enhancement principle, for trace gas detection via photoacoustic spectroscopy (PAS). The vibrational characteristics of the PAC, specifically its superimposed and self-differential operational modes, were first analyzed through simulations. The system's versatility and performance were then rigorously evaluated using two representative light sources─a distributed feedback (DFB) laser and a laser diode (LD)─with ammonia (NH3) and nitrogen dioxide (NO2) as the target gases. Employing the superimposed signal from eight microphones yielded a significant enhancement: the minimum detection limit (MDL) improved by 60-70%, the signal-to-noise ratio (SNR) increased nearly 3-fold, and sensitivity was boosted nearly 8-fold compared to using a single microphone. Remarkably, the system achieved MDL of 1.43 ppm for NH3 and 33 ppb for NO2. Furthermore, the self-differential mode demonstrated exceptional common-mode noise rejection capability. The developed module features a compact design, low fabrication cost, and outstanding analytical performance, offering substantial potential for optimizing existing commercial PAS systems.

The signal-to-noise ratio of phased arrays can be improved by increasing the number of samples (the number of microphones or the number of averages). Increasing the array sized also allows the direct measurement of the source directivity, without moving the array. In this paper we present a module microphone array with data acquisition system, consisting of 1 m x 2m panels with 800 MEMS microphones, each. This modular architecture allows the time-synchronized measurement of an arbitrary number of panels and thus, aperture size and total number of sensors This architecture was used as a 7200 MEMS microphone array with a 6 m x 3 m aperture on an open wind tunnel airframe test at DNW-NWB. A second test used 12.000 MEMS microphones with a 6 m x 5 m aperture on an open wind tunnel test at DNW-LLF Sources identification directivity measurements will be shown. Additional far-field microphones and a classical analog array are employed to validate the beamforming results and directivity analysis.

In the last few years viewing an audio source has become a widely used tool in many fields such as speech recognition and enhancement and especially in noise localization in different environments. Acoustic cameras are widely used as a tool for noise source investigation, however an acoustic image is difficult to achieve in environments with a large amount of noise and reverberation. An effective approach to overcome this is the use of beamforming theory coupled with a microphone array in order to obtain a recording of the desired acoustic signal. The aim of this work is to design and develop a low cost microphone array which can be upgraded with a camera, i.e. acoustic camera, based on digital MEMS microphones and a raspberry pi. Different formations of the microphone array are developed, tested and compared. The details on hardware integration as well as the development environment are discussed in this paper.

This paper presents a comprehensive investigation of sensitivity-determining factors in dual-backplate capacitive MEMS microphones through analytical modeling, finite element analysis (FEM), and experimental validation. The study focuses on three critical design parameters: backplate perforation density, membrane tension, and electrode gap spacing. A lumped parameter model (LPM) and FEM simulations are employed to characterize the dynamic behavior and frequency response of the microphone. Simulation results demonstrate that reducing the backplate hole diameter or hole count amplifies squeeze-film damping, inducing nonlinear effects and anti-resonance dips near the fundamental frequency (f0) while mitigating low-frequency roll-off (<100 Hz). Membrane tension exhibits a nonlinear relationship with sensitivity, stabilizing at high tension (>7000 N/m) but risking pull-in instability at low tension (<1500 N/m). Smaller electrode gaps enhance sensitivity but are constrained by pull-in voltage limitations. The FEM model achieves higher accuracy (≤2 dB error) than LPM in predicting low-frequency response anomalies. This work provides systematic guidelines for optimizing dual-backplate MEMS microphone designs, balancing sensitivity, stability, and manufacturability.

Concrete bridge decks are susceptible to subsurface defects such as delamination, caused by aging, corrosion, and environmental stressors, underscoring the need for timely, reliable non-destructive evaluation (NDE). While traditional acoustic methods, such as, hammer or chain drag remain widely used, they suffer from subjectivity, inconsistent impact forces, and limited applicability on overhead or vertical surfaces. This study introduces a novel Smart Acoustic Sounding System (SASS) that modernizes impact sounding through an integrated framework consisting of a broadband electronic chirp excitation source, high-sensitivity MEMS microphones with acoustic shielding, and a tracking camera for automated and location-aware inspections. Advanced signal processing techniques, such as Empirical Mode Decomposition (EMD), Power Spectral Density (PSD), and the Hilbert-Huang Transform (HHT), are employed to filter noise, extract frequency-based features, and support machine learning-based defect classifications. Laboratory testing on a full-scale concrete slab embedded with known artificial defects (e.g., shallow and deep delamination, voids, and honeycombing), as well as a deteriorated concrete beam, confirmed the system’s ability to accurately identify defect zones, particularly shallow delamination with characteristic frequency signatures in the range of 1–3 kHz. The system produced real-time defect maps with minimal human input, demonstrating its potential to improve the accuracy, repeatability, and efficiency of bridge deck inspections and support data-driven maintenance decisions.

This work presents a proof of concept including simulation and experimental validations of acoustic gas sensor prototypes for trace CO2 detection up to 1 ppm. For the detection of lower gas concentrations especially, the dependency of acoustic resonances on the molecular weights and, consequently, the speed of sound of the gas mixture, is exploited. We explored two resonator types: a cylindrical acoustic resonator and a Helmholtz resonator intrinsic to the MEMS microphone’s geometry. Both systems utilized mass flow controllers (MFCs) for precise gas mixing and were also modeled in COMSOL Multiphysics 6.2 to simulate resonance shifts based on thermodynamic properties of binary gas mixtures, in this case, N2-CO2. We performed experimental tracking using Zurich Instruments MFIA, with high-resolution frequency shifts observed in µHz and mHz ranges in both setups. A compact and geometry-independent nature of MEMS-based Helmholtz tracking showed clear potential for scalable sensor designs. Multiple experimental trials confirmed the reproducibility and stability of both configurations, thus providing a robust basis for statistical validation and system reliability assessment. The good simulation experiment agreement, especially in frequency shift trends and gas density, supports the method’s viability for scalable environmental and industrial gas sensing applications. This resonance tracking system offers high sensitivity and flexibility, allowing selective detection of low CO2 concentrations down to 1 ppm. By further exploiting both external and intrinsic acoustic resonances, the system enables highly sensitive, multi-modal sensing with minimal hardware modifications. At microscopic scales, gas detection is influenced by ambient factors like temperature and humidity, which are monitored here in a laboratory setting via NDIR sensors. A key challenge is that different gas mixtures with similar sound speeds can cause indistinguishable frequency shifts. To address this, machine learning-based multivariate gas analysis can be employed. This would, in addition to the acoustic properties of the gases as one of the variables, also consider other gas-specific variables such as absorption, molecular properties, and spectroscopic signatures, reducing cross-sensitivity and improving selectivity. This multivariate sensing approach holds potential for future application and validation with more critical gas species.

Abstract The condition of the milling tool and its cutting edges is crucial for the surface quality of high-performance components. In this paper, we propose a non-intrusive approach to condition monitoring by analyzing the acoustic emissions generated during the milling process, enabling tool condition monitoring without interfering with the milling operation. The acoustic data is recorded using a MEMS microphone and analyzed employing a hybrid machine learning framework. In the first step, the raw acoustic data is transformed to a phase space representation, where the time-series data of each lane is mapped to a rotational angle. Subsequently, the Rayleigh-Ritz autoencoder is applied to the phase space data. To incorporate process-specific knowledge, constraints are defined using trigonometric functions. This approach has demonstrated its effectiveness in detecting progressive tool wear using only acoustic data providing a reliable and non-invasive solution for tool condition monitoring.

Evaluation of sound-based sleep stage prediction in shared sleeping settings.

Hearing loss impacts millions globally, affecting their ability to perceive and interpret sounds clearly in noisy environments. Traditional hearing aids amplify all sounds, often resulting in discomfort and audio distortion. This paper presents the design and development of a real-time digital hearing aid system equipped with noise filtering and volume protection features for individuals with hearing disorders. Unlike conventional hearing aids that amplify all surrounding sounds indiscriminately, the proposed system intelligently filters background noise and dynamically regulates output volume to prevent sudden loud sounds from causing discomfort or further damage. The device is built around the ESP32 microcontroller and utilizes an INMP441 digital MEMS microphone for sound capture, along with a PAM8403 amplifier for audio output. The system processes the audio in real-time and transmits it to a speaker with optimized gain and clarity. Designed to be compact, power-efficient, and affordable, this hearing aid offers a practical and enhanced auditory experience for users in various acoustic environments

This paper presents a novel framework addressing the fundamental challenge of concurrent real-time audio acquisition and motor control in resource-constrained mobile robotics. The ESP32-based system integrates a digital MEMS microphone with rover mobility through a unified Bluetooth protocol. Key innovations include (1) a dual-thread architecture enabling non-blocking concurrent operation, (2) an adaptive eight-bit compression algorithm optimizing bandwidth while preserving audio quality, and (3) a mathematical model for real-time resource allocation. A comprehensive empirical evaluation demonstrates consistent control latency below 150 ms with 90–95% audio packet delivery rates across varied environments. The framework enables mobile acoustic sensing applications while maintaining responsive motor control, validated through comprehensive testing in 40–85 dB acoustic environments at distances up to 10 m. A performance analysis demonstrates the feasibility of high-fidelity mobile acoustic sensing on embedded platforms, opening new possibilities for environmental monitoring, surveillance, and autonomous acoustic exploration systems.

Characterization and Modeling of an AlN Piezoelectric Fly-Inspired In-Plane Directional MEMS Microphone Packaged With an Integrated Circuit Amplifier

In recent years, using sound as a source of information in environmental monitoring systems has become increasingly important. Thanks to the development of Internet of Things (IoT) and artificial intelligence (AI) technologies, it has become possible to create distributed, intelligent acoustic systems used in medicine, industry, cities, and the natural environment. The article presents an overview of modern methods of acquiring and analysing sound data, from MEMS sensors and microphones, signal processing, and feature extraction to machine learning algorithms. The analysis of many works shows how diverse the approach to acoustic analysis can be, depending on the purpose, context, and environmental constraints. Technical challenges, privacy issues, and possible directions for further development, such as integration with multimodal monitoring systems or edge processing, are also discussed. The article is cross-sectional and can be a starting point for further research on intelligent acoustic monitoring in systems based on AI and IoT.

A Hybrid MEMS Microphone Combining Piezoelectric and Capacitive Transduction Mechanisms

Abstract The mechanisms of sound generation of broadband noise of small rotors are investigated for different flight conditions in an experimental study. A novel microphone array with 512 MEMS microphones is used to investigate the sound emission. Beamforming algorithms are applied to localize the sound generation areas of the broadband noise on the rotor blades. In addition, the optical flow measurement techniques particle image velocimetry and background-oriented schlieren are used to investigate the aerodynamic mechanisms on which the noise is based. The experiments are supplemented by simulations using a panel method. The experiments show that trailing edge noise at 80% of the rotor radius is the dominant sound source mechanism for broadband noise in the frequency range 1–8 kHz in hover. In forward flight, the noise emission depends strongly on the flight condition. When the rotor is tilted against the incoming flow (decelerating maneuver), a C-shaped sound source area occurs in the front of the rotor disk, which can be attributed to interactions of blade tip vortices in the rotor wake with the subsequent rotor blades. In addition, two areas of sound generation on the advancing and retreating rotor side are detected when the rotor is tilted backwards by up to $${10^\circ }$$ 10 ∘ . These are caused by the interaction with the super vortices that form on both sides of the rotor during forward flight.

Abstract An amplitude-modulated laser can be used to generate false, yet coherent acoustic signals on the outputs of MEMS microphones. While this vulnerability has ramifications on the security of cyber-physical systems that trust these microphones, the physical explanation of this effect remained a mystery. Without an understanding of the physical phenomena contributing to this signal injection, it is difficult to design effective and reliable defenses. In this work, we show the degree to which the mechanisms of thermoelastic bending, thermal diffusion, and photocurrent generation are used to inject signals into MEMS microphones. We provide models for each of these mechanisms, develop a procedure to empirically determine their relative contributions, and highlight the effects on eight commercial MEMS microphones. We accomplish this with a precise setup to isolate each mechanism using several laser wavelengths and a vacuum chamber. The results indicate that the injected signal on the microphone is dependent on the wavelength of the incoming light. Shorter wavelengths (such as a 450 nm blue laser) exploit photoacoustic effects, and the periodic heating and expansion of air is the dominant factor in seven of eight sample microphones. Longer wavelengths (such as a 904 nm infrared laser) exploit photoelectric effects on the sensitive ASIC, generating signals that are between 2x and 100x stronger than photoacoustic signals in six of eight sample microphones. This understanding of the physical causality of laser signal injection leads to recommendations for future laser-resistant microphone designs. These include adding light-blocking structures at the system or device level, improving to glob top application, and adding simple light or temperature sensors for injection detection. Based on the fundamental causality, we also suggest potential vulnerabilities within other sensors with similar characteristics to MEMS microphones, such as conventional microphones, ultrasonic sensors, and inertial sensors.

Inspired by the auditory systems of small animals, such as spiders, the tachinid fly, Ormia ochracea, and mosquitoes, a novel low-noise, flow-sensing capacitive MEMS microphone capable of sensing acoustic particle velocity is introduced. Unlike conventional microphones that have a diaphragm for sensing sound pressure, this design consists of a thin, porous, movable structure that is intended to be driven by viscous forces as a result of the sound-induced flow. This viscous force then rotates the movable structure around a middle central hinge and creates a change in capacitance caused by a relative motion between neighboring beams. The whole structure is made of one layer of silicon using a silicon-on-insulator (SOI) wafer using photolithography technology with a device layer thickness of 5 μm. The movable part has dimensions of 0.7 mm × 1.2 mm and is placed above a cavity inside the bulk silicon that facilitates the flow of sound particles. This microphone responds to flow (a vector) rather than pressure (a scalar). Ultimately, experimental results demonstrate a sensitivity of approximately 5 mV/Pa, a noise floor between 10-4 and 10-5 Pa/Hz, and directivity ratios reaching up to 77 at 2000 Hz, underscoring its potential for high-performance acoustic applications.

The human voice is directional by nature, and its directivity has been the subject of extensive study. Accurate data on voice directivity are fundamental in any work associating natural human speech and its interaction with architectural spaces. In this research, we present and analyze results obtained using a novel high-resolution measurement setup. The system features an array of 180 MEMS microphones arranged on a horizontal circle with a 3-m diameter, enabling measurement of sound sources with a 2° resolution in the azimuthal plane. Test subjects were positioned at the center of the array, with the location of their mouths precisely calibrated using a multi-camera and laser alignment system. Data were collected from 24 talkers (5 female, 19 male) articulating fluent English speech. The analysis, conducted across different frequency bands, explores variations between sexes and compares findings with previous studies. We discuss the relevance of the resolution provided by the system and examine its performance within the context of existing literature. Potential directions for future work are also outlined.

Inspired by the hearing mechanism of the fly Ormia Ochracea, we demonstrated a MEMS diaphragm capable of pressure and pressure gradient sensing in two orthogonal directions. The diaphragm is circular and suspended by multiple spring arms along its circumference. Each spring contains a piezoelectric film on its top surface. The structure has many orthogonal vibration modes. These modes are used for the concurrent measurement of sound pressure and pressure gradient along two orthogonal axes by way of summing and subtracting signals generated by the various sensing ports. Vibration modes of the MEMS are measured by applying broadband chirp voltage waveforms to a piezoelectric sensing port while observing the resulting motion using a scanning laser Doppler vibrometer (LDV). The device was then packaged for acoustic measurements by integrating each of several ports with a high input impedance Integrated Circuit Amplifier (ICA). Acoustical sensitivity and directivity were measured in a walk-in anechoic chamber. Compared with previous fly-inspired microphones, the presented prototype is the first with three measurands (p0, dp/dx, and dp/dy) derived simultaneously from a single diaphragm.

Enabling computing systems to detect the objects that people hold and interact with provides valuable contextual information that has the potential to support a wide variety of mobile applications. However, existing approaches either directly instrument users' hands, which can reduce tactile sensation, or are limited in the types of objects and interactions they can detect. This work introduces HandSAW, a wireless wrist-worn device incorporating a Surface Acoustic Wave (SAW) sensor with enhanced bandwidth and signal-to-noise ratio while rejecting through-air sounds. The device features a sealed mass-spring diaphragm positioned on top of the sound port of a MEMS microphone, enabling it to capture SAWs generated by objects and through touch interaction events. This custom-designed wearable platform, paired with a real-time ML pipeline, can distinguish 20 passive object events with &gt;99% per-user accuracy and a 91.6% unseen-user accuracy, as validated through a 16-participant user study. For devices that do not emit SAWs, our active tags enable HandSAW to detect those objects and transmit encoded data using ultrasonic signals. Ultimately, HandSAW provides an easy-to-implement, robust, and cost-effective means for enabling user-object interaction and activity detection.