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The purpose of this paper is to propose an alternative approach to improve the performance of microelectromechanical systems (MEMSs) silicon (Si) condenser microphones in terms of operating frequency and sensitivity through the introduction of a secondary material with a contrast of mechanical properties in the corrugated membrane.

Finite element method from COMSOL is used to analyze the MEMS microphones performance consisting of solid mechanic, electrostatic and thermoviscous acoustic interfaces. Hence, the simulated results could described the physical mechanism of the MEMS microphones, especially in the case of microphones with complex geometry. A 2-D model was used to simplify computation by applying axis symmetry condition.

The simulation results have suggested that the operating frequency range of the microphone could be extended to be operated beyond 20 kHz in the audible frequency range. The data showed that the frequency resonance of the microphone using a corrugated Si membrane with SiC as the embedded membrane is increased up to 70 kHz compared with 63 kHz for the plane Si membrane, whereas the microphone’s sensitivity is slightly decreased to −79 from −76 dB. Furthermore, the frequency resonance of a corrugated membrane microphone could be improved from 26 to 70 kHz by embedding the SiC material. Last, the sensitivity and frequency resonance value of the microphones could be modified by adjusting the height of the embedded material.

Based on these theoretical results, the proposed modification highlighted the advantages of simultaneous modifications of frequency and sensitivity that could extend the applications of sound and acoustic detections in the ultrasonic spectrum with an acceptable performance compared with the typical state-of-the-art Si condenser microphones.

This research deals with the production of electronic textiles (e‐textiles) demonstrators. Initially, the research dealt with the creation of 4×5 microphone array on a large area conformal textile substrate. Once the interface electronics were connected to the 4×5 microphone array, this system became an effective acoustic array. Here, a new acoustic eight microphone array design has been designed, fabricated and tested. Changes were made to improve microphone array performance, and to optimize the associated software for data capture and analysis. This new design was based on UC‐Berkeley mote microcomputer technology. The mote‐based system addresses the issue of scaling acoustic arrays, to allow for distributing microphones over large‐areas, and to allow performance comparisons to be made with the original 4×5 microphone acoustic array.

The purpose of this paper is two‐fold. First, to deal with the problem of audio speaker localization and second, to deal with the problem of mobile camera control. The task of speaker localization consists of determining the position of the active speaker and the task of camera control consists of orienting a mobile camera towards that active speaker. These steps represent the main task of speaker tracking, which is the global purpose of the research work.

In this approach, two‐channel‐based estimation of the speaker position is achieved by comparing the signals received by two cardioids microphones, which are placed the one against the other and separated by a fixed distance. The localization technique presented in this paper is inspired from the human ears, which act as two different sound observation points, enabling humans to estimate the direction of the speaking person with a good precision. Concerning the camera control part, the authors have conceived an automatic system for generating the command signals and controlling the rotation of the mobile camera by a stepper motor.

The off‐line experiments of speaker tracking by camera have been done in a small meeting room without echo cancelation. Results show the good performances of the proposed localization methods and a correct tracking by camera.

This new technique can be used for the automatic supervision of smart rooms.

The work described in this paper is original, since it uses only two microphones for the speaker localization.

This paper aims to propose and demonstrate novel microphone sensors based on the frequency delta-sigma modulation (FDSM) technique, which replaces the conventional delta-sigma modulator in the delta-sigma analog-to digital converters. A key of the FDSM technology is to use a voltage-controlled oscillator (VCO) for converting an input analog signal to a 1-bit pulse-density modulated digital signal. High-performance sensors can be realized if the VCO is replaced by an oscillator whose oscillation frequency depends on an external physical parameter.

Microphone sensors are proposed based on FDSM that uses a suspended microstrip disk resonator, where the backside ground plane is replaced by a thin metal diaphragm. A resonant tunneling diode (RTD) oscillator is also used, as the performance of these sensors significantly depends on the oscillation frequency. To demonstrate the basic operation of the proposal, prototype devices were fabricated with an InGaAs/AlAs RTD.

A satisfactory noise shaping property, which is a significant nature of delta-sigma modulation, was demonstrated over three decades for the prototype device. A sound-sensing peak was also clearly observed when applying 1 kHz sound from a speaker.

High-performance ultrasonic microphone sensors can be realized if the sensors are fabricated by using a thin InP substrate with high-frequency oscillator design.

In this study, the authors proposed and experimentally demonstrated novel microphone sensors, which are promising as future ultrasonic sensors that have high dynamic range with wide bandwidth.

A single wafer silicon condenser microphone with a novel single deeply corrugated diaphragm is presented in this paper. The microphone diaphragm with corrugation depth of 100 μm is only 1 mm² in area, while the open‐circuit sensitivity as high as 9.8 mV/Pa under a bias voltage of 6 V has been obtained. The recorded frequency bandwidth is about 20 kHz. The measurements show reasonable agreements with the theoretical predictions.

In this project, we propose and test a new device – wearable sociometric badges containing small microphones – as a low-cost and relatively unobtrusive tool for measuring stress response to group processes. Specifically, we investigate whether voice pitch, measured using the microphone of the sociometric badge, is associated with physiological stress response to group processes.

We collect data in a laboratory setting using participants engaged in two types of small-group interactions: a social interaction and a problem-solving task. We examine the association between voice pitch (measured by fundamental frequency of the participant’s speech) and physiological stress response (measured using salivary cortisol) in these two types of small-group interactions.

We find that in the social task, participants who exhibit a stress response have a statistically significant greater deviation in voice pitch (from their overall average voice pitch) than those who do not exhibit a stress response. In the problem-solving task, participants who exhibit a stress response also have a greater deviation in voice pitch than those who do not exhibit a stress response, however, in this case, the results are only marginally significant. In both tasks, among participants who exhibited a stress response, we find a statistically significant correlation between physiological stress response and deviation in voice pitch.

We conclude that wearable microphones have the potential to serve as cheap and unobtrusive tools for measuring stress response to group processes.

In this paper we describe a novel human machine interface system aimed primarily at those who have experienced loss of extremity motor function. The system enables the control of a wide range of assistive technologies such as wheelchairs, prosthetics, computers and general electrical goods at the ‘flick of a tongue’. This system could benefit a huge sector of people including those who have suffered a spinal cord injury, stroke or quadriplegia.The technology focuses on a unique hands‐free interface whereby users can issue commands simply by performing subtle tongue movements; these tongue motions are continually monitored by a small microphone positioned comfortably within the ear canal. Due to the physiological connections between these regions and the distinctive nature of the signals, these commands can be detected and distinguished allowing a control signal to be issued.This inexpensive device offers significant advantages over existing technologies by providing unobtrusive, hygienic control through natural tongue motion. New software has been implemented, achieving over 97% correct classification across four different tongue movements for seven test subjects. Feasibility of the system as an interface for a variety of devices is demonstrated through simulation studies including controlling a prosthetic manipulator and power wheelchair.

The basic information required by trainers who wish to use video to achieve training outcomes is presented for those who are not experts at video production, do not have the time or the interest to become expert, do not have, and do not wish to develop, expertise in electronics and do not have access to sufficient organisational resources to hire an expert. The essential information needed to make experiences with video as productive, creative and problem‐free as possible is included.

The purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).

The relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.

Variations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.

DAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.