Microphone Device Research Articles

Evaluation of a multi-function in-ear device performance in the presence of impulse noise using acoustic test fixtures

Real-time noise exposure monitoring has the potential to assess risk before damage occurs. We present on the development of an in- ear device that (1) provides hearing protection; (2) measures the noise in the ear canal to estimate exposure dose; and (3) monitors distortion product otoacoustic emissions (DPOAE) over time. To validate the device’s ability to measure impulse noise under hearing protection, we measured the response in a field test. A single AR15 was shot multiple times, and four prototypes were used in multiple acoustic test fixtures (ATFs) at different relative distances from the exit of the gun. The ATFs included a G.R.A.S 45 CB in addition to a head simulator designed to detect both air- and bone-conducted sound. We evaluated the impulse peak insertion loss (IPIL) metric of the device inserted by itself or in combination with other hearing protection (“double protection” condition) to assess attenuation of impulsive noises. We also compared the device's microphone measurements to those obtained through the ATF microphones, and to the recordings of external microphones located at or near the ear of each ATF. Results demonstrate the value of testing in-ear devices in acoustic test fixtures during development phases.

Dealing with wind noise on resource constrained acoustic sensors

Single and multiple microphones in small or hand-held devices, i.e., mobile phones, are often single port and without windscreens. In a typical outdoor situation, this configuration can be subject to strong impulsive wind noise events and wind-induced tones. Smart mobile phones can sometimes utilize their significant computational power to employ sophisticated signal processing to reduce or remove wind-associated noise. However, in systems where battery conservation is paramount and computational processing resources are severely limited, alternative strategies must be employed. This presentation will discuss exemplar wind noise data from a single channel, resource-constrained system, methods for wind noise reduction, and results.

AudioGest: Gesture-based Interaction for Virtual Reality using Audio Devices.

Current virtual reality (VR) system takes gesture interaction based on camera, handle and touch screen as one of the mainstream interaction methods, which can provide accurate gesture input for it. However, limited by application forms and the volume of devices, these methods cannot extend the interaction area to such surfaces as walls and tables. To address the above challenge, we propose AudioGest, a portable, plug-and-play system that detects the audio signal generated by finger tapping and sliding on the surface through a set of microphone devices without extensive calibration. First, an audio synthesis-recognition pipeline based on micro-contact dynamics simulation is constructed to generate modal audio synthesis from different materials and physical properties. Then the accuracy and effectiveness of the synthetic audio are verified by mixing the synthetic audio with real audio proportionally as the training sets. Finally, a series of desktop office applications are developed to demonstrate the application potential of AudioGest's scalability and versatility in VR scenarios.

Diagnosis of robotic arm anomalous sounds using unsupervised learning algorithms

In this paper, we develop a technique that integrates acoustics detection with artificial intelligence, empowering robotic arms in production environments with the capacity to monitor, diagnose, and predict anomalies. We combine the deep learning techniques of AI with audio signals collected by an array of microphones. We predict the health status of equipment systems and prevent equipment failure by using this sound data. Our novel approach focuses on converting and analyzing environmental acoustics data. The distinctive contribution of this research lies in its strategy learning and inference. By harnessing large-scale audio data information, we establish AI learning models that automatically parse input data to recognize audio features. We also employ device scheduling operations for comparative detection, endowing the equipment with abnormal diagnosis and prediction capabilities. We utilize commercially available microphone devices for sound collection, voice activity detection, and spectrogram reduction for signal preprocessing and capture before we create a dataset. Finally, we use the Gaussian mixture model (GMM) for unsupervised learning, estimating the probability density of data, and calculating GMM scores of data points for in-depth analysis. In this way, we can judge whether the sound signals collected by the microphone are abnormal.

Verification of Mechanical Properties Identification Based on Impulse Excitation Technique and Mobile Device Measurements

The Impulse Excitation Technique (IET) is one of the most useful testing methods for evaluating or calculating some material properties. This can be useful to evaluate and confirm that the material ordered is what was delivered. In the case of unknown materials, where their properties are required by simulation software, this is also a quick way to obtain mechanical properties and thus improve the simulation quality. The main drawback of the method is the requirement for a specialized sensor and acquisition system and a well-trained engineer to prepare the setup and analyze the results. The article evaluates the possibility of using a low-cost solution in the form of a mobile device microphone as a way to obtain data, which after the Fast Fourier Transform (FFT), allows to obtain frequency response graphs and use the IET method procedure to calculate the mechanical properties of the samples. The data obtained by the mobile device are compared with the data obtained by professional sensors and data acquisition systems. The results confirm that for typical homogenous materials, the mobile phone is a cheap and reliable alternative for fast, on-the-go material quality inspections and can be introduced even in small companies and on construction sites. Additionally, this kind of approach does not require specific knowledge of sensing technology, signal treatment, or data analysis and can be performed by any assigned employee, who can receive the quality check information immediately on-site. Additionally, the presented procedure allows data collection and transfer to the cloud for future references and additional information extraction. This element is fundamental for introducing sensing technologies under the Industry 4.0 concept.

Testing a digital self‐interview approach: The virtual memory assistance tool

AbstractTechnology‐mediated interviews are a promising supplement to in‐person interviews for questioning eyewitnesses. We sought to develop and test a virtual self‐administered memory‐elicitation procedure—The virtual memory assistance tool (VMAT). The VMAT is a web‐based memory retrieval tool designed around the principles of the Cognitive Interview. In Experiments 1 and 2, participants (N = 135, N = 127) watched a target video and then received either VMAT or Control instructions, reporting their memory either by Typing into a textbox or Speaking into their device's microphone. In Experiments 3 and 4, participants (N = 89, N = 78) watched a target video and then received either VMAT or Control instructions presented in either Audio or Video format. Our findings suggest that a virtual tool for memory elicitation seems effective independent of Interview Procedures, Response Modalities, and Instruction Modalities and across differing target stimuli (mock‐crime vs. content of a sexual nature).

Java Script Powered Web Based Speech to Text Converter

Java script-powered web-based speech-to-text converter interface is used to access speech recognition, which involves the use of the Web Speech API, which allows web developers to incorporate speech recognition and synthesis capabilities into their web applications. The Web Speech API offers a selection of APIs for recording audio input, identifying speech, and speech to text conversion. We have developed a speech-to-text input method for use with web platforms. The system is provided as a JavaScript with dynamic HTML files, and CSS. Users can use voice commands to create content and articles instead of typing. Speech recognition entails listening to speech through a device's microphone and having a speech recognition service check proper grammar for any sentence or word phrases and use as a translator from one language to other. A result (or series of results) is returned as a text string when a word or phrase is correctly identified, and subsequent actions can be started as a result. The quality of the audio input, background noise, and language support are some of the variables that affect how accurately speech-to-text recognition performed using JavaScript. However, the technology has improved significantly over the years and can provide accurate and reliable results for many use cases. Typically, the device's built-in default speech recognition system—which is present in the majority of contemporary operating systems and may be used to provide voice commands—will be utilized for speech recognition. Think about Dictation on mac OS, Siri on iOS, Cortana on Windows 10, Android Speech, etc.

Multiple-source dynamic range compression in hearing devices: What can we learn from music mixing?

Dynamic range compression (DRC), a form of nonlinear gain control, is widely used in both hearing devices and music production, but in very different ways. In hearing devices, DRC boosts quiet sounds more than loud sounds to match the reduced dynamic range of listeners with hearing loss. Compression is applied to the mixture of sounds captured by the hearing device microphone, so it is known to cause distortion in noisy environments where the level of one sound can affect the gain applied to another sound. In music mixing, DRC can be applied to individual vocal or instrumental tracks or to the overall mixture. It can also be applied to combinations of signals to introduce deliberate distortion effects, for example, by adjusting the level of one track based on the level of another. In a hearing device that could process different sound sources independently, real-life sounds could be mixed like musical tracks, reducing unwanted distortion and enabling new nonlinear techniques to improve intelligibility. This talk will explore multiple-source DRC architectures that are commonly used for music but could also be advantageous for hearing devices.

Flexible and Transparent Flexoelectric Microphone

AbstractPVDF based flexoelectric polymers imply innumerous applications, for example, in developing flexible and transparent transducers for touch screen users. Here, a prototype flexoelectric microphone is developed and characterized. A straightforward method is adopted to fabricate the microphone device in which a film of copolymer blend is sandwiched between pair of ITO coated transparent electrodes integrated at the stretched rubber substrates. In this study, the phase represented at 1337 cm−1 of FTIR reflection spectra containing 50% α and 50% ß termed as Zeta phase which is correlated to the enhancement in its flexoelectric properties, is investigated. Here, the flexoelectric coefficient (µ33) and sensitivity (S) of 6.5 cm × 5 cm flexible and transparent laminates featuring 0 ≤ φ ≤ 1.0 blends at 100SPL is examined. The study reports the µ33 = 23.5 µC m−1 and S = 15.3 V Pa−1 for φ = 0.5 blend, which is the highest reported values for P (VDF‐TrFE) and P (VDF‐TrFE‐CTFE) polymer blends. This study is helpful in developing the flexible and transparent flexoelectric microphones for potential mobile applications.

Three-Dimensional Simulation Platform for Optimal Designs of the MEMS Microphone

MEMS microphone has a wide range of application prospects in electronic devices such as mobile phones, headphones, and hearing aids due to its small size, low cost, and reliable performance. Research and development of MEMS microphones involves multiple thermo-electro-mechanical couplings among various physical and electrical fields. Unfortunately, there is not an accurate three-dimensional (3D) MEMS microphone simulation platform, which can be applied for the design of chip parameters and packaging characteristics. Herein, based on commercial COMSOL software, we have established a 3D simulation platform for MEMS microphones, which is used to systematically study the influences of geometric structure and physics parameters on the sensitivity and frequency responses of the microphone and consider the influences of packaging characteristics on the performance of the microphone. The simulation results are consistent with those obtained using a lumped element method, which proves the accuracy of the simulation platform. The platform can be used to design and explore new principles or mechanisms of MEMS microphone devices.

Identification of Basic Respiratory Patterns for Disease-related Symptoms Through a Microphone Device

Identification of Basic Respiratory Patterns for Disease-related Symptoms Through a Microphone Device by Amol M Khatkhate 1,* , Varad Raut 1, Madhura Jadhav 2, Shreya Alva 2, Kalpesh Vichare 1, Ameya Nadkarni 1 1 Department of Mechanical Engineering, Rizvi College of Engineering, Mumbai, 400050, India2 Department of Biotechnology Engineering, Rizvi College of Engineering, Mumbai, 400050, India

Double Talk Detection in hands-free mobile communication- A comprehensive survey

In modern teleconferencing systems, echo cancellation is a mandatory requirement. Echoes arise in Loudspeaker Enclosure Microphone devices because of coupling between loudspeaker and microphone signals. To remove these unwanted echoes from microphone signals, Acoustic Echo Cancellers are used. Several techniques exist for acoustic echo cancellation. Acoustic echo cancelling an adaptive FIR/IIR filter can predict the path of echo and cancel it. The adaptive algorithms give good performance when only the far-end signal is present. But when the near-end signal is present, the adaptive filter tends to diverge. Such a situation creates a double talk scenario. The filter adaptation must be prevented during the double talk period. Accurate detection of double talk is quite difficult and computationally complex. Hence the algorithms for detecting Double Talk are not easy to implement. Increased detection accuracy is achieved at the cost of computational complexity. Further, it is desired to cancel out the far end echo signal, thus giving a good quality of near-end speech signal as output. Here, we provide a detailed study of the different algorithms existing for double talk detection, and future direction of research in the field of double talk detection is proposed.

BlinkListener

Eye blink detection plays a key role in many real-life applications such as Human-Computer Interaction (HCI), drowsy driving prevention and eye disease detection. Although traditional camera-based techniques are promising, multiple issues hinder their wide adoption including the privacy concern, strict lighting condition and line-of-sight (LoS) requirements. On the other hand, wireless sensing without a need for dedicated sensors gains a tremendous amount of attention in recent years. Among the wireless signals utilized for sensing, acoustic signals show a unique potential for fine-grained sensing owing to their low propagation speed in the air. Another trend favoring acoustic sensing is the wide availability of speakers and microphones in commodity devices. Promising progress has been achieved in fine-grained human motion sensing such as breathing using acoustic signals. However, it is still very challenging to employ acoustic signals for eye blink detection due to the unique characteristics of eye blink (i.e., subtle, sparse and aperiodic) and severe interference (i.e., from the human target himself and surrounding objects). We find that even the very subtle involuntary head movement induced by breathing can severely interfere with eye blink detection. In this work, for the first time, we propose a system called BlinkListener to sense the subtle eye blink motion using acoustic signals in a contact-free manner. We first quantitatively model the relationship between signal variation and the subtle movements caused by eye blink and interference. Then, we propose a novel method that exploits the "harmful" interference to maximize the subtle signal variation induced by eye blinks. We implement BlinkListener on both a research-purpose platform (Bela) and a commodity smartphone (iPhone 5c). Experiment results show that BlinkListener can achieve robust performance with a median detection accuracy of 95%. Our system can achieve high accuracies when the smartphone is held in hand, the target wears glasses/sunglasses and in the presence of strong interference with people moving around.

Scanning the Voice of Your Fingerprint With Everyday Surfaces

Due to the premise of uniqueness and acceptance, fingerprint has been the most adopted biometric technologies in high-impact applications (e.g., smartphone security, monetary transactions and international-border verification). Although there are an array of commercial fingerprint scanners across different sensing modalities including optical, capacitive, thermal and ultrasonic, existing fingerprint technologies are vulnerable to spoofing attacks via fake-finger in Kang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2003. In this paper, we investigate a new dimension of fingerprint sensing based on the friction-excited sonic wave (in simpler words, ”voice of fingerprint”) from a user swiping his fingertip on everyday surfaces. Specifically, we develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SonicPrint</i> to leverage the intrinsic fingerprint ridge information in sonic wave for user identification. First, the complex ambient noise is isolated from the sonic wave using background isolation and adaptive segmentation models. Afterward, a series of multi-level friction descriptors that highlight the target fingerprint information is extracted. These descriptors are fed to a specially designed ensemble classifier for user identification. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SonicPrint</i> is practical as it leverages in-built microphones in smart devices, requiring no hardware modifications. As the first exploratory study, our experimental results with 31 participants over three different swipe actions on 12 different types of materials show up to a 98 percent identification accuracy.

Optimizing Communication in Schools and Other Settings During COVID-19

Optimizing Communication in Schools and Other Settings During COVID-19

Secure Your Voice

Voice control has attracted extensive attention recently as it is a prospective User Interface (UI) to substitute for conventional touch control on smart devices. Voice assistants have become increasingly popular in our daily lives, especially for those people who are visually impaired. However, the inherently insecure nature of voice biometrics means that voice assistants are vulnerable to spoofing attacks as evidenced by security experts. To secure the commands for voice assistants, in this paper, we present a liveness detection system that provides continuous speaker verification on smart devices. The basic aim is to match the voice received by the smart device's microphone with the oral airflow of the user when speaking a command. The airflow is captured by an auxiliary commercial off-the-shelf airflow sensor. Specifically, we establish a theoretical model to depict the relationship between the oral airflow pressure and the phonemes in users' speech. The system estimates a series of pressures from the speech according to the theoretical model, and then calculates the consistency between the estimated pressure signal and the actual pressure signal measured by the airflow sensor to determine whether a command is a genuine "live" voice or an artificially generated one. We evaluate the system with 26 participants and 30 different voice commands. The evaluation showed that our system achieves an overall accuracy of 97.25% with an Equal Error Rate (EER) of 2.08%.

Variable tap-length non-parametric variable step-size NLMS adaptive filtering algorithm for acoustic echo cancellation

In teleconferencing and communication systems, the use of Loudspeaker Enclosure Microphone device leads to undesired echoes. To reduce these echoes, an acoustic echo canceller is used. In this paper, we propose a Variable Tap-length Non-Parametric Variable Step-Size NLMS (VT-NPVSS-NLMS) algorithm based on adaptive filtering for acoustic echo cancellation. The step-size is adjusted without the need for tuning too many parameters, using only the square of the average autocorrelation of a priori and a posteriori estimates of error. Moreover, the tap-length is varied to facilitate a high convergence speed and a small bias in tap-length from the optimum length. Hence, such a combination of the variable step-size algorithm with a variable tap-length provides faster convergence and reduced steady-state mean square error. The performance of the proposed algorithm is evaluated in terms of steady-state and transient mean square error. The advantages of the proposed algorithm, as compared to other adaptive algorithms, are presented using simulation results. From the results, we infer that for the VT-NPVSS-NLMS, the convergence speed is increased and the steady-state mean square error is reduced when compared with the conventional NLMS and the variable-step-size NLMS algorithm with a fixed tap-length.

Personal Assistant Based Home Automation using Raspberry PI

A desired technology in 21st century for home automation is an intelligent system. An objective of any automated system is to reduce human labor, efforts, time and errors. Commonly Home Automation is a system where various home appliances like air conditioner, TV, Refrigerator, etc are capable to communicate with each other and they can be remotely controlled. Here is presented a paper which discuss a home automation system through virtual personal assistant’s capability of speech recognition and through raspberry pi to automate home devices such as air cooler, lights, fans, TV etc. This system is a combination of various different technologies such as voice recognition, voice analysis and voice processing. A device microphone is used to receive voice requests while the speaker gives voice output.. The system also uses Artificial intelligence techniques which can solve various human queries through web search and can also control the home appliances.

GDPR Bypass by Design? Transient Processing of Data Under the GDPR

Be it facial detection scanners placed in airports and supermarkets, microphones in mobile and home devices, or imaging systems in autonomous vehicles on the streets, pervasive computing technologies introduce evermore sensors into our daily environment. Simultaneously, increased awareness of data protection has led engineers to more frequently employ privacy enhancing technologies in their products. In search of privacy-friendly applications controllers are discovering that minimizing the processed data by default might not only be in compliance with the data minimization principle: depending on the concrete design of the systems it could exclude their activities from the GDPR altogether, as its scope is restricted to the processing of personal data. In this article we examine some of these “transient data processing” technologies. By doing so, we critically analyze current and upcoming technologies and their applications in light of the GDPR and discuss the legal and societal implications of their advent.

GDPR bypass by design? Transient processing of data under the GDPR

Be it facial detection scanners placed in airports and supermarkets, microphones in mobile and home devices, or imaging systems in autonomous vehicles on the streets, pervasive computing technologies introduce evermore sensors into our daily environment. Simultaneously, increased awareness of data protection has led engineers to more frequently employ privacy enhancing technologies in their products. In search of privacy-friendly applications controllers are discovering that minimizing the processed data by default might not only be in compliance with the data minimization principle: depending on the concrete design of the systems it could exclude their activities from the GDPR altogether, as its scope is restricted to the processing of personal data. In this article we examine some of these “transient data processing” technologies. By doing so, we critically analyze current and upcoming technologies and their applications in light of the GDPR and discuss the legal and societal implications of their advent.