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The MEMS microphone is a representative device among the MEMS family, which has attracted substantial research interest, and those tailored for human voice have earned distinct success in commercialization. Although sustained development persists, challenges such as residual stress, environmental noise, and structural innovation are posed. To collect and summarize the recent advances in this subject, this paper presents a concise review concerning the transduction mechanism, diverse mechanical structure topologies, and effective methods of noise reduction for high-performance MEMS microphones with a dynamic range akin to the audible spectrum, aiming to provide a comprehensive and adequate analysis of this scope.

This article developed and investigates the operation of remote acoustic laser microphones in virtual and practical circuits. Various simulation processes were studied and mainly the circuit of an open optical transmitter and receiver were simulated. Another aspect of the work are the introduction and use of a laser in the invisible light range into the system, the essence of which is explained by not causing inconvenience to the environment. The invisible wave may be ultraviolet or infrared light ranges.

This review collates around 100 papers that developed micro-electro-mechanical system (MEMS) capacitive microphones. As far as we know, this is the first comprehensive archive from academia on this versatile device from 1989 to 2019. These works are tabulated in term of intended application, fabrication method, material, dimension, and performances. This is followed by discussions on diaphragm, backplate and chamber, and performance parameters. This review is beneficial for those who are interested with the evolutions of this acoustic sensor.

Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system’s functioning and health. This method has been particularly useful for non-invasive assessments and is used in various clinical settings, such as OSA, asthma, respiratory infectious diseases, lung function, and detection during either wakefulness or sleep. One of the challenges and limitations of TBS recording is the background noise, including speech sound, movement, and even non-tracheal breathing sounds propagating in the air. The breathing sounds captured from the nose or mouth can interfere with the tracheal breathing sounds, making it difficult to isolate the sounds necessary for accurate diagnostics. In this study, two surface microphones are proposed to accurately record TBS acquired solely from the trachea. The frequency response of each microphone is compared with a reference microphone. Additionally, this study evaluates the tracheal and lung breathing sounds of six participants recorded using the proposed microphones versus a commercial omnidirectional microphone, both in environments with and without background white noise. The proposed microphones demonstrated reduced susceptibility to background noise particularly in the frequency ranges (1800–2199) Hz and (2200–2599) Hz with maximum deviation of 2 dB and 2.1 dB, respectively, compared to 9 dB observed in the commercial microphone. The findings of this study have potential implications for improving the accuracy and reliability of respiratory diagnostics in clinical practice.

AbstractBefore the Perseverance rover landing, the acoustic environment of Mars was unknown. Models predicted that: (1) atmospheric turbulence changes at centimetre scales or smaller at the point where molecular viscosity converts kinetic energy into heat1, (2) the speed of sound varies at the surface with frequency2,3and (3) high-frequency waves are strongly attenuated with distance in CO2(refs. 2–4). However, theoretical models were uncertain because of a lack of experimental data at low pressure and the difficulty to characterize turbulence or attenuation in a closed environment. Here, using Perseverance microphone recordings, we present the first characterization of the acoustic environment on Mars and pressure fluctuations in the audible range and beyond, from 20 Hz to 50 kHz. We find that atmospheric sounds extend measurements of pressure variations down to 1,000 times smaller scales than ever observed before, showing a dissipative regime extending over five orders of magnitude in energy. Using point sources of sound (Ingenuity rotorcraft, laser-induced sparks), we highlight two distinct values for the speed of sound that are about 10 m s−1apart below and above 240 Hz, a unique characteristic of low-pressure CO2-dominated atmosphere. We also provide the acoustic attenuation with distance above 2 kHz, allowing us to explain the large contribution of the CO2vibrational relaxation in the audible range. These results establish a ground truth for the modelling of acoustic processes, which is critical for studies in atmospheres such as those of Mars and Venus. 

Speech is produced by a nonlinear, dynamical Vocal Tract (VT) system, and is transmitted through multiple (air, bone and skin conduction) modes, as captured by the air, bone and throat microphones respectively. Speaker specific characteristics that capture this nonlinearity are rarely used as stand-alone features for speaker modeling, and at best have been used in tandem with well known linear spectral features to produce tangible results. This paper proposes Recurrent Plot (RP) embeddings as stand-alone, non-linear speaker-discriminating features. Two datasets, the continuous multimodal TIMIT speech corpus and the consonant-vowel unimodal syllable dataset, are used in this study for conducting closed-set speaker identification experiments. Experiments with unimodal speaker recognition systems show that RP embeddings capture the nonlinear dynamics of the VT system which are unique to every speaker, in all the modes of speech. The Air (A), Bone (B) and Throat (T) microphone systems, trained purely on RP embeddings perform with an accuracy of 95.81%, 98.18% and 99.74%, respectively. Experiments using the joint feature space of combined RP embeddings for bimodal (A–T, A–B, B–T) and trimodal (A–B–T) systems show that the best trimodal system (99.84% accuracy) performs on par with trimodal systems using spectrogram (99.45%) and MFCC (99.98%). The 98.84% performance of the B–T bimodal system shows the efficacy of a speaker recognition system based entirely on alternate (bone and throat) speech, in the absence of the standard (air) speech. The results underscore the significance of the RP embedding, as a nonlinear feature representation of the dynamical VT system that can act independently for speaker recognition. It is envisaged that speech recognition too will benefit from this nonlinear feature.

This study is a survey of sound source localization and detection methods. The study provides a detailed classification of the methods used in the fields of science mentioned above. It classifies sound source localization systems based on criteria found in the literature. Moreover, an analysis of classic methods based on the propagation model and methods based on machine learning and deep learning techniques has been carried out. Attention has been paid to providing the most detailed information on the possibility of using physical phenomena, mathematical relationships, and artificial intelligence to determine sound source localization. Additionally, the article underscores the significance of these methods within both military and civil contexts. The study culminates with a discussion of forthcoming trends in the realms of acoustic detection and localization. The primary objective of this research is to serve as a valuable resource for selecting the most suitable approach within this domain.

The railway industry is currently investing in condition monitoring techniques to be able to compete with other transportation mediums. One of the reasons for this investment is to be able to identify the incipient development of curve squeal in railway systems. The annoying high-pitched tonal noise produced because of curve squeal has necessitated the need for mitigation measures to be taken by railway operators. However, noise from the surroundings and other trains has affected the conventional use of microphones for monitoring curve squeal in tight curves. It is imperative that the railway industry introduce additional sensors to help in the characterization and identification of curve squeal in railway track as the train negotiates the curve. The objective of this research is focused on the evaluation of condition monitoring performances using vibrations obtained from the wheel/rail roller and sound obtained remotely close to the wheel-rail interface to identify and characterize curve squeal. By the completion of the comparative studies, this research has resulted in a number of new findings that illustrate the significant contributions to knowledge. This research presents the application of correlation method to establish a reliable relationship between acoustic and sound for the detection and characterization of curve squeal on the twin disc rig. The sensors used to detect and characterize curve squeal are microphone and two accelerometers installed laterally on the wheel and rail roller rims. The contact conditions taken into consideration are dry contact, wet contact and friction modifier contacts. A MATLAB model was developed to detect and characterize curve squeal. The results of the simulated model showed some disparities between the simulated transition yaw angles and measured transition yaw angles for which curve squeal occurs. Time and frequency domain were employed to extract the features from the sensors. Correlation method was employed to classify the features extracted from the microphone and accelerometer data. The results obtained showed that a negligible or weak correlation coefficient value indicates the development of curve squeal on the twin disc rig in dry contact conditions. A moderate or strong correlation coefficient values is an indication of no curve squeal occur or curve squeal mitigation when contaminants (water and friction modifiers are introduced to the wheel-rail interface). The performance of the Correlation method for determining and classifying fault feature (curve squeal) extracted from the microphone and wheel/rail accelerometers has presented some useful qualities that makes it suitable in a real condition monitoring application system.