Search Results (103)

Data collected by sensors plays a critical role in system decision-making. Microphone arrays enable distance measurement and fault localization, which is particularly critical in the radiation environments of nuclear facilities. Acoustic localization based on microphone arrays can effectively fulfill this requirement. This study experimentally evaluates the Total Ionizing Dose (TID) effects of ⁶⁰Co γ-ray radiation on commercial MEMS (micro-electro-mechanical systems) silicon microphones. Five identical microphone units were simultaneously irradiated at a dose rate of 0.0342 Gy(Si)/s while continuously monitoring operating current and spectral response. Experimental results show that the commercial MEMS silicon microphones exhibit an average TID failure threshold of 932.6 ± 62.8 Gy(Si), with a 95% confidence interval of [875.5, 989.7] Gy(Si). Three degradation/failure levels are clearly defined: channel degradation, channel failure, and full system failure. Radiation exposure causes a progressive increase in operating current (up to 6.7 times the initial value), severe spectral distortion, and ultimately complete loss of localization function. This indicated that standard commercial MEMS silicon microphones possess a certain degree of tolerance to TID radiation. Subsequently, an annealing test was performed. However, Post-irradiation annealing restored the operating current but not the acoustic performance, indicating irreversible radiation-induced damage. Full article

In response to the pressing need for miniaturized MEMS microphones in wearable technology and mobile devices, and to surmount the technical limitations inherent in conventional piezoelectric microphones, which typically depend on enlarging chip dimensions or decreasing stiffness to attain low resonance frequencies, this study introduces a novel piezoelectric MEMS microphone (PMM) design predicated on a flexibility-enhanced rotating structure. The proposed design utilizes an aluminum scandium nitride (Al_0.8Sc_0.2N) piezoelectric thin film with 20% scandium doping and incorporates four equivalent sensing units formed by four curved cutting lines centrally located on the chip. This configuration employs a nested arrangement of four cantilever beams to substantially increase vibration compliance, thereby effectively lowering the natural frequency without altering the chip’s external size. Three-dimensional finite element simulations reveal that, relative to traditional triangular cantilever beam architectures, the flexibility-enhanced rotating structure reduces the natural frequency from 15.6 kHz to 13.49 kHz while enhancing sensitivity from −44.6 dB to −40 dB. The device was fabricated via a comprehensive microfabrication process and subsequently characterized within a standardized acoustic testing environment. Experimental results indicate that the microphone attains a sensitivity of −43.84 dB at 1 kHz and exhibits a first resonance frequency of 13.5 kHz, closely aligning with simulation predictions. Furthermore, the signal-to-noise ratio (SNR) reaches 58.3 dB across the full range of human-audible frequencies. By leveraging the flexibility-enhanced rotating structure, this work achieves an optimal compromise between elevated sensitivity and reduced resonance frequency within a compact form factor, thereby offering a viable technical solution for the advancement of high-performance miniature acoustic sensors. Full article

Respiration rate (RR) is a key physiological indicator of health, stress, and thermoregulatory load in dairy cattle, yet continuous RR monitoring under commercial farm conditions remains challenging. In this Technical Note, we present a non-invasive clip-on nose ring device for continuous respiration monitoring based on acoustic recording directly at the nostril. The device integrates a MEMS microphone, embedded electronics, battery, and removable storage in a sealed, mechanically robust housing suitable for real-world barn environments. The system was deployed on five dairy cows under commercial farm conditions, enabling repeated multi-day recordings over several weeks. The respiration rate was extracted offline from raw audio using a deterministic signal-processing pipeline based on multiscale periodicity detection. Algorithm-derived RR estimates were evaluated against manually annotated breath events. Using 10-min rolling median values, the algorithm achieved a mean absolute error (MAE) of 1.47 breaths per minute (bpm), a root mean square error (RMSE) of 1.92 bpm, and a high correlation with reference values (r = 0.98, R² = 0.96). In addition to short-term accuracy, the system enabled stable multi-day monitoring. Group-level analysis across all five animals revealed a clear diurnal respiration pattern over multiple consecutive days, with lower RR during nighttime and higher RR during daytime summer conditions, without signs of a baseline drift. These results demonstrate the feasibility of continuous, long-term respiration monitoring in dairy cattle using an audio-based clip-on nose ring device and provide a practical foundation for longitudinal (multi-day, within-animal) RR assessment under commercial farm conditions, with potential for future extensions towards advanced respiratory health monitoring. While the system demonstrated stable performance under summer farm conditions, validation under extreme heat-stress environments and larger animal cohorts is required for comprehensive population-level assessment. Full article

Wind-induced noise remains a critical engineering challenge for MEMS microphones in compact consumer electronics such as smartphones, where spatial constraints limit conventional noise control solutions. This study experimentally investigates the suppression of flow-induced wind noise by a straight tube serving as the front cavity of a microphone, using a precision measurement microphone for data acquisition. Controlled experiments were conducted in both a flow duct for parametric isolation and an anechoic chamber for real-world validation. Results demonstrate a strong diameter-dependent effect: for a 1 mm diameter, increasing tube length significantly reduces noise power spectral density and steepens high-frequency roll-off via enhanced internal viscous and thermal dissipation. This effect weakens for a 2 mm diameter and becomes negligible for a 3 mm diameter, where noise is dominated by external flow excitation at the tube inlet rather than internal propagation. Therefore, extending tube length is an effective noise control strategy only for small-diameter cavities. Furthermore, while increased wind speed and oblique incidence elevate PSD, a longer tube reduces this sensitivity. Because acoustic transmission loss—including potential effects like aperture diffraction and impedance mismatch—was not measured, any resulting improvement in the effective signal-to-noise ratio is strictly presented as a hypothesis requiring future electroacoustic validation. The consistent findings across both experimental environments provide clear design guidance: for compact MEMS microphone systems in portable devices, elongating the front cavity is a viable passive noise control method only when the cavity diameter is sufficiently small (<2 mm). This offers a practical, space-efficient alternative to traditional windscreen-based approaches in portable devices. Full article

This study investigates the integration of audio features with CO₂ sensor data to enhance occupancy detection accuracy in naturally ventilated office environments. Accurate occupancy detection is pivotal for smart building energy management, yet CO₂-based methods cannot provide fast enough response times and are sensitive to air circulation changes due to internal convection. In this article we propose a combination of CO₂ sensors and audio features from MEMS microphones to improve the occupancy detection accuracy and improve the response times. We use a Random Forest classifier and evaluate the results across two scenarios: CO₂-only and CO₂ combined with audio features. Results show that incorporating the audio features into the occupancy detection algorithms yields a significant increase in detection accuracy and speed, especially when the environment is subject to frequent air circulation changes due to internal convection, like the opening and closing of windows and doors. Combining the CO₂ and audio sensing offers a promising, cost-effective approach to occupancy detection in smart buildings, yet more research on advanced audio processing and feature selection is necessary. Full article

Pressure pipelines are widely used in the energy and transportation fields for conveying natural gas, water, etc. Under complex and harsh conditions with long-term operation, this easily leads to leakage, threatening the safe and stable operation of transportation systems. Although acoustic sensors support non-destructive leakage detection, their accuracy is restricted by noise interference and minor leakage uncertainties, and existing systems lack a targeted integration design for pipeline scenarios. To address this, the micro-electromechanical system (MEMS) is specifically designed as an MEMS microphone array integrated system (MEMS-MAIS), which is applied for pipeline leakage detection through data fusion at different levels. First, a dedicated MEMS microphone array system is designed to realize high-sensitivity collection of leakage acoustic data. In addition, the integrated feature extraction and feature-level fusion modules are proposed to retain effective information, and a decision-level fusion module is incorporated to improve the reliability of leakage detection results. To verify the designed system, an experiential platform is established with several microphone data. The results indicate that the proposed MEMS-MAIS exhibits excellent anti-interference performance and leakage detection accuracy of 94.67%. It provides a reliable integrated system solution for pipeline leakage detection and verifying high engineering application value. Full article

Snoring, a common sleep-disordered breathing phenomenon, impairs sleep quality for both the sufferer and any bed partner. While mild snoring primarily disrupts sleep continuity, severe cases often indicate obstructive sleep apnea (OSA), a disorder affecting 9–17% of the global population, linked to significant comorbidities and socioeconomic burden (see Introduction for supporting data). Here, we propose a low-cost, real-time snoring detection and intervention system that integrates a multiple-spectrum deep learning framework with an Internet of Things (IoT)-enabled smart pillow. The modified Parallel Convolutional Spatiotemporal Network (PCSN) combines three parallel convolutional neural network (CNN) branches processing Constant-Q Transform (CQT), Synchrosqueezing Wavelet Transform (SWT), and Hilbert–Huang Transform (HHT) features with a Long Short-Term Memory (LSTM) network to capture spatial and temporal characteristics of sounds associated with snoring. The smart pillow prototype incorporates two Micro-Electro-Mechanical System (MEMS) microphones, an ESP8266 off-shelf board, a speaker, and two vibration motors for real-time audio acquisition, cloud-based processing via Arduino cloud, and closed-loop haptic/audio feedback that encourages positional changes without fully awakening the snorers. Experiments demonstrated that the modified PCSN model achieves 98.33% accuracy, 99.29% sensitivity, 98.34% specificity, 98.3% recall, and 98.32% F1-score, outperforming existing systems. Hardware costs are under USD 8 and a smartphone app provides authorized users with real-time visualization and secure data access. This solution offers a cost-effective and accurate approach for home-based OSA screening and intervention. Full article

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 $6\times {10}^{-6}$ 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. Full article

This paper describes a real-time system for recognizing voice commands for resource-constrained embedded devices, specifically a PIC microcontroller. While most existing speech ordering support solutions rely on high-performance processing platforms or cloud computation, the system described here performs fully embedded low-power processing locally on the device. Sound is captured through a low-cost MEMS microphone, segmented into short audio frames, and time domain features are extracted (i.e., Zero-Crossing Rate (ZCR) and Short-Time Energy (STE)). These features were chosen for low power and computational efficiency and the ability to be processed in real time on a microcontroller. For the purposes of this experimental system, a small vocabulary of four command words (i.e., “ON”, “OFF”, “LEFT”, and “RIGHT”) were used to simulate real sound-ordering interfaces. The main contribution is demonstrated in the clever combination of low-complex, lightweight signal-processing techniques with embedded neural network inference, completing a classification cycle in real time (under 50 ms). It was demonstrated that the classification accuracy was over 90% using confusion matrices and timing analysis of the classifier’s performance across vocabularies with varying levels of complexity. This method is very applicable to IoT and portable embedded applications, offering a low-latency classification alternative to more complex and resource intensive classification architectures. Full article

Sensor faults can produce incorrect data and disrupt the operation of entire systems. In critical environments, such as healthcare, industrial automation, or autonomous platforms, these faults can lead to serious consequences if not detected early. This study explores how faults in MEMS microphones can be classified using lightweight ML models suitable for devices with limited resources. A dataset was created for this work, including both real faults (normal, clipping, stuck, and spikes) caused by issues like acoustic overload and undervoltage, and synthetic faults (drift and bias). The goal was to simulate a range of fault behaviors, from clear malfunctions to more subtle signal changes. Convolutional Neural Networks (CNNs) and hybrid models that use CNNs for feature extraction with classifiers like Decision Trees, Random Forest, MLP, Extremely Randomized Trees, and XGBoost, were evaluated based on accuracy, F1-score, inference time, and model size towards real-time use in embedded systems. Experiments showed that using 2-s windows improved accuracy and F1-scores. These findings help design ML solutions for sensor fault classification in resource-limited embedded systems and IoT applications. Full article

Predictive maintenance helps reduce operational costs and improve machine reliability by anticipating failures. However, existing solutions are often too expensive or complex for small rotating machinery such as fans or low-power motors. This work presents a low-cost, IoT-based monitoring system using an ESP32 microcontroller combined with MEMS sensors (an accelerometer and a microphone). The system continuously collects vibration and acoustic signals, which are then processed using RMS and FFT techniques. Machine learning algorithms, such as anomaly detection or basic classification, are used to identify deviations from normal operation. A working prototype was tested under various fault conditions, including imbalance and wear. The system successfully identified abnormal states through signal deviations in both time and frequency domains, with over ~73% detection accuracy. The proposed solution is cost-effective, simple to implement, and well-suited for educational or industrial environments. It demonstrates the potential of embedded systems and basic signal analysis for scalable predictive maintenance applications. Full article

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. Full article

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. Full article

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. Full article

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. Full article