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Open-loop control of stepper motors reduces system cost and complexity for positioning systems. To produce a precise current waveform, a novel open-loop control implementation of a hysteresis current controller is presented and compared to the existing pulse-width modulation method. The new method extends the resolution available for a given sampling rate, allowing finer control of the motor current. Applications for precision positioning minimize external disturbances but a real, nonideal, motor introduces vibration, and nonuniform motion. Microstepping of the drive waveform approximates a continuous motion to reduce these effects but they are not fully modeled or understood. The proposed current controller is assembled with a hybrid stepper motor to verify the method experimentally. A differential evolution optimizer is applied with the complete physical system in the loop. To measure vibration, a low cost contact microphone is used, eliminating the requirement for expensive rotational sensors. This method is shown to reduce vibration across different speeds. Comparing the microstep positions of a standard sinusoidal waveform and the waveform optimized to reduce vibration, an improvement in position accuracy is also found.

This paper describes initial steps towards developing a real-time quantitative particulate solids’ (sand) monitoring system forMultiphase flowlines based on acoustic monitoring and machine learning techniques. The concentration and the velocity of thesolids were varied during experimental trials. A conventional contact microphone mounted externally to a production flowline bend was used for recording the emitted acoustic signal. Features extracted from the signal were used as input to Time DelayNeural Network (TDNN) with solids concentration and velocity label to a training set. The TDNN achieved low values ofnormalised root mean square error (NRMSE) for all the models compared to the classical neural network.

Water is a valuable, yet scarce resource in various parts of the globe. As water reserves dry up and rainfall decreases yearly, we are promoting water conservation within the residential setting to protect our wildlife and reduce the price of monthly water bills. In collaboration with Amazon’s Lab126, we are creating an affordable and easy-to-install water monitoring system for renters and homeowners to track their water consumption in units of time. We optimized a 3D printed enclosure to house a piezoelectric contact microphone and detect water flow via the surface vibrations on a pipe. The duration of water flowing correlates with the duration of the piezoelectric microphone’s signal output. The signal is measured in both the amplitude of voltage and frequency. The enclosure underwent iterative testing along three different sections of a sink’s flexible supply hose and at three different flow rates. By using an oscilloscope, we verified our tests produced significant voltage waveforms representing the presence of active water flow. In future work, we will house additional electronics like an Arduino microcontroller and apply Fast-Fourier Transforms to discern the water flow’s particular frequency from the output waveforms.

Automated bowel sound (BS) analysis methods were already well developed by the early 2000s. Accuracy of ~90% had been achieved by several teams using various analytical approaches. Clinical research on BS had revealed their high potential in the non-invasive investigation of irritable bowel syndrome to study gastrointestinal motility and in a surgical setting. This article proposes a novel methodology for the analysis of BS using hybrid convolutional and recursive neural networks. It is one of the first methods of using deep learning to be widely explored. We have developed an experimental pipeline and evaluated our results with a new dataset collected using a device with a dedicated contact microphone. Data have been collected at night-time, which is the most interesting period from a neurogastroenterological point of view. Previous works had ignored this period and instead kept brief records only during the day. Our algorithm can detect bowel sounds with an accuracy &gt, 93%. Moreover, we have achieved a very high specificity (&gt, 97%), crucial in diagnosis. The results have been checked with a medical professional, and they successfully support clinical diagnosis. We have developed a client-server system allowing medical practitioners to upload the recordings from their patients and have them analyzed online. This system is available online. Although BS research is technologically mature, it still lacks a uniform methodology, an international forum for discussion, and an open platform for data exchange, and therefore it is not commonly used. Our server could provide a starting point for establishing a common framework in BS research.

This paper outlines a demonstration of an acoustic piano augmentation that allows for infinite sustain of one or many notes. The result is a natural sounding piano sustain that lasts for an unnatural period of time. Using a tactile shaker, a contact microphone and an amplitude activated FFT-freeze Max patch, this system is easily assembled and creates an infinitely sustaining piano.

Monitoring pathological mechano-acoustic signals emanating from the lungs is critical for timely and cost-effective healthcare delivery. Adventitious lung sounds including crackles, wheezes, rhonchi, bronchial breath sounds, stridor or pleural rub and abnormal breathing patterns function as essential clinical biomarkers for the early identification, accurate diagnosis and monitoring of pulmonary disorders. Here, we present a wearable sensor module comprising of a hermetically encapsulated, high precision accelerometer contact microphone (ACM) which enables both episodic and longitudinal assessment of lung sounds, breathing patterns and respiratory rates using a single integrated sensor. This enhanced ACM sensor leverages a nano-gap transduction mechanism to achieve high sensitivity to weak high frequency vibrations occurring on the surface of the skin due to underlying lung pathologies. The performance of the ACM sensor was compared to recordings from a state-of-art digital stethoscope, and the efficacy of the developed system is demonstrated by conducting an exploratory research study aimed at recording pathological mechano-acoustic signals from hospitalized patients with a chronic obstructive pulmonary disease (COPD) exacerbation, pneumonia, and acute decompensated heart failure. This unobtrusive wearable system can enable both episodic and longitudinal evaluation of lung sounds that allow for the early detection and/or ongoing monitoring of pulmonary disease.

This paper reports on the implementation and characterization of a hermetically-sealed single-proof-mass multi-directional accelerometer contact microphone exhibiting wide operational bandwidth (>10kHz) and low noise (Brownian noise

Teeth gestures become an alternative input modality for different situations and accessibility purposes. In this paper, we present TeethTap, a novel eyes-free and hands-free input technique, which can recognize up to 13 discrete teeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an IMU sensor and a contact microphone behind both ears, which works in tandem to detect jaw movement and sound data, respectively. TeethTap uses a support vector machine to classify gestures from noise by fusing acoustic and motion data, and implements K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW) distance measurement using motion data for gesture classification. A user study with 11 participants demonstrated that TeethTap could recognize 13 gestures with a real-time classification accuracy of 90.9% in a laboratory environment. We further uncovered the accuracy differences on different teeth gestures when having sensors on single vs. both sides. Moreover, we explored the activation gesture under real-world environments, including eating, speaking, walking and jumping. Based on our findings, we further discussed potential applications and practical challenges of integrating TeethTap into future devices., 26th International Conference on Intelligent User Interfaces (IUI '21), April 14--17, 2021, College Station, TX, USA

It is important to monitor eating behavior to maintain health. A technique that can automatically detect chewing and swallowing and provide information on aspects such as chewing position must thus be developed. This paper focuses especially on the sounds made in the mouth while eating and demonstrates a method that uses skin contact microphones to automatically extract the desired information. In addition to conventional left and right channel throat microphones placed under the ear, the proposed technique utilizes sound-related information obtained from upper and lower 2-channel throat microphones and a contact microphone attached to the neck to stabilize and improve the detection of swallowing.

We present the design of an FBG sensor-based contact microphone and compare its speech quality to that of a calibrated measurement microphone using perceptual evaluation of speech quality score and spectrogram analysis in noisy environment.

Background Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. Objective This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. Methods We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. Results The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. Conclusions This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.

Foetal heart rate is an important parameter of the foetal well-being. Foetal phonocardiography is one of the noninvasive methods to detect the foetal heart rate by recording the foetal sounds. This is an alternative for the methods like foetal electrocardiography and foetal doppler. The aim of this paper is to compare the performance of the three sensors – two digital stethoscopes and a contact microphone, for foetal heart sound acquisition. The performance is compared based on the SNR of the data acquired by the sensors. It was found that contact force of the sensor affects the quality of the acquired data and there exists an optimum contact force for the sensors (3N for contact microphone and ~0.5N for stethoscope) at which the SNR of the signal is the highest.

In health care, the continuous breathing monitoring device has not been popular yet, albeit personal daily breathing monitoring is expected to be used for the diagnosis of sleep apnea syndrome, stress management, and depression prevention. With the aim to realize personal and continuous breathing monitoring, this paper proposes a lung movement detection method that uses sound waves. This method has advantages of low cost, use of a small-scale and simple device, and easiness in wearing. The proposed method can sense lung movement only by a vibration speaker and a contact microphone placed on user’s chest and use the measured parameters in the sound signal processing. In this paper, the waveform comparison of the lung movement measured by the proposed method and the displacement of the thoracic diaphragm in breathing was conducted, and it was confirmed that the proposed method could capture lung movement in breathing with a high correlation.

In this paper, we propose Auracle, a wearable earpiece that can automatically recognize eating behavior. More specifically, in free-living conditions, we can recognize when and for how long a person is eating. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the bone and tissue of the head. This audio data is then processed by a custom analog/digital circuit board. To ensure reliable (yet comfortable) contact between microphone and skin, all hardware components are incorporated into a 3D-printed behind-the-head framework. We collected field data with 14 participants for 32 hours in free-living conditions and additional eating data with 10 participants for 2 hours in a laboratory setting. We achieved accuracy exceeding 92.8% and F1 score exceeding 77.5% for eating detection. Moreover, Auracle successfully detected 20-24 eating episodes (depending on the metrics) out of 26 in free-living conditions. We demonstrate that our custom device could sense, process, and classify audio data in real time. Additionally, we estimate Auracle can last 28.1 hours with a 110 mAh battery while communicating its observations of eating behavior to a smartphone over Bluetooth.

Background: Selected adult patients with obstructive sleep apnoea-hypopnea syndrome (OSAHS) may benefit from surgical intervention. At present, the dominant technique for the diagnosis of the anatomical locus of upper airway obstruction during sleep is drug-induced sedation endoscopy (DISE). This technique is widespread, but has generated controversy related to its artificiality. Methodology: We present a technique for the acquisition of high-quality and consistent recordings of snoring sounds, suitable for advanced acoustic analysis and correlation with the findings of DISE. Results: Analysis of the generated recordings demonstrates distinct frequency-domain characteristics of snoring sounds generated by distinct anatomical subsites, for example the soft palate, or tongue base. Conclusions: Supplemental information may potentially be obtained from the waveform of snoring sounds produced by the patient; however, no robust quantitative “assay” has been developed or been clinically validated. This may in part be related to inconsistencies or insufficient quality of the sound data acquired. This novel technique may circumvent these difficulties and permit more advanced analysis techniques with potential clinical applicability.

We report on a novel wafer-level-packaged accelerometer contact microphone to pick up acoustic vibrations orthogonal to its contact surface without responding to airborne acoustic emissions. The design uses a high-bandwidth low-noise unidirectional capacitive accelerometer with a deformable frame suspending a proof-mass, designed to have larger displacement at the edge to allow for the implementation of shock stops. The sense electrodes use a nano-gap to provide high electrical sensitivity with high stiffness design and nano-gap damping electrodes enable near-critical damping in a low vacuum encapsulation to achieve a high bandwidth above 14 kHz. We demonstrate that the miniature accelerometer contact microphone placed on the throat can record human speech and heart sounds with high fidelity while rejecting surrounding noises.

Head-and-chest resonance is a concept often mentioned in vocal pedagogy. For a long time, the grasp of the characteristics of head voice and chest voice highly rely on personal feeling, which is also due to the scarcity of research windows. With a contact microphone directly capturing the speech signal transmitted from laryngeal to the body surface, the resonance characteristics of the head and chest can be approximated. This study found that different points in the head and chest have different resonance characteristics over different pitches, vowels and in between modal register and falsetto register. While in general, the head and the chest can be clearly distinguished by the resonance characteristics. The head has a higher response to high(close) vowels, and the chest has a higher response to low(open) vowels, which has a strong correlation with the first formant of the vowel.

Mechano-acoustic signals emanating from the heart and lungs contain valuable information about the cardiopulmonary system. Unobtrusive wearable sensors capable of monitoring these signals longitudinally can detect early pathological signatures and titrate care accordingly. Here, we present a wearable, hermetically-sealed high-precision vibration sensor that combines the characteristics of an accelerometer and a contact microphone to acquire wideband mechano-acoustic physiological signals, and enable simultaneous monitoring of multiple health factors associated with the cardiopulmonary system including heart and respiratory rate, heart sounds, lung sounds, and body motion and position of an individual. The encapsulated accelerometer contact microphone (ACM) utilizes nano-gap transducers to achieve extraordinary sensitivity in a wide bandwidth (DC-12 kHz) with high dynamic range. The sensors were used to obtain health factors of six control subjects with varying body mass index, and their feasibility in detection of weak mechano-acoustic signals such as pathological heart sounds and shallow breathing patterns is evaluated on patients with preexisting conditions.

The present work is focused on a method for the classification of voices of patients affected by the Parkinson disease (PD). In particular a binary discrimination method between PD patients and healthy subjects has been implemented. To better classify the PD patients, an additional binary method has been implemented to discriminate PD patients and non-PD patients with different voice pathologies. This project aims to produce predictive models that can be implemented in a wearable device. It processes the patient’s voice and works as a screening tool and a therapy-effectiveness estimator for both clinicians and patients. In particular, voice features such as Jitter, Shimmer, HNR and CPPS, are extracted from recorded material using both an microphone in air and a throat contact microphone. Such parameters are then processed using classification algorithms that give the best model to predict the clinical parameters used as class identifiers, such as Hoehn Yahr scale and Disease Duration (DD).

The increasingly popular usage of wearable devices provides users with the ability to continuously track their health conditions or physical activities. Such system is however vulnerable to user spoofing, in which a user distributes his/her device to other users such that the data collected from these users could be claimed to be his/her own. Thus, it is critical to identify the user for many wearable devices, allowing the sensing data to be labeled properly. In this paper, we propose a user identification system by leveraging the users whispered sound to mitigate user spoofing for wearable devices. Our system exploits the contact microphone placed into contact with the body to capture the users whispered sound for user identification. Given the captured acoustic data, our system first identifies frames which contain whispered events. Our system then calculates acoustic features from the identified whispered frames to determine whether the voice is collected when the microphone is on the body. Moreover, to make our system robust, we assign different quality weights to the whispers phonemes by considering their consistency (i.e., intra users differences) and distinctiveness (i.e., inter users differences) simultaneously. Our experiments demonstrate that our system is robust and accurate across various scenarios.

In this paper, we present a lightweight, on-body, wireless system designed for monitoring real-world, ambulatory voice characteristics. The system has the potential to provide important assessments of voice and speech disorders and the impact of environmental sound levels as individuals go about their daily life. The system's transmitter is positioned on the neck and synchronously streams dual-channel sensor data from an on-board MEMS microphone and a high-bandwidth accelerometer, which acts as a noise-robust and confidential contact microphone. These data are recorded to a receiver that can store the data locally and stream a real-time feed to a computer. We also report on the design considerations of this novel system and discuss progress leading up to the latest iteration, especially of the transmitter components on a flexible circuit. Pilot data are shown from an in-field, ambulatory recording during an individual's daily activities that included settings in quiet and with naturalistic ambient noise.

OBJECTIVE: To examine whether there were any associations between high resolution cervical auscultation (HRCA) acoustic signals recorded by a contact microphone and swallowing kinematic events during pharyngeal swallow as assessed by a videofluoroscopic examination. DESIGN: Prospective pilot study SETTING: University teaching hospital, University research laboratories PARTICIPANTS: 35 patients with stroke who have suspected dysphagia (26 males + 9 females; age = 65.8 ± 11.2). METHODS: Videofluoroscopic recordings of one hundred liquid swallows from 35 stroke patients were analyzed, and a variety of HRCA signal features to characterize each swallow were calculated. MAIN OUTCOME MEASURE(S): Percent of signal feature maxima (peak) occurring within 0.1 seconds of swallow kinematic event identified from videofluoroscopic recording RESULTS: Maxima of HRCA signal features, such as standard deviation, skewness, kurtosis, centroid frequency, bandwidth, and wave entropy, were associated with hyoid elevation, laryngeal vestibule closure, and upper esophageal sphincter opening, and the contact of the base of the tongue and posterior pharyngeal wall. CONCLUSIONS: Although the kinematic source of HRCA acoustic signals has yet to be fully elucidated, these results indicate a strong relationship between these HRCA signals and several swallow kinematic events. There is a potential for HRCA to be developed for diagnostic and rehabilitative clinical management of dysphagia.

This work estimates the uncertainty contributions of speech level parameters measured with a contact-sensor-based device and a headworn microphone. Four contributions are considered: (1) instrumental uncertainty, related to device calibration; (2) method repeatability and (3) reproducibility, estimated through repeated measurements without and with device repositioning, respectively; (4) source reproducibility, due to the variability of human speech. To ascertain changes in speech production, differences between measures should be at least higher than the expanded uncertainty. In the case of device repositioning, the expanded uncertainty combines contributions (1), (3), and (4). When the device is not repositioned, it combines contributions (2) and (4).

Contact microphones, especially throat microphones, have been developed to be used in environments with high background noises to improve the speech intelligibility in communication. They pick up vibrations from the surface they are attached to and are therefore less sensitive to sound and noise from the air. Comparison of the speech intelligibility with other types of microphones have previously been done by letting test persons examine the communication devices in question. This study examines the possibility to make use of the STI-method instead and therefore make a comparison faster, more cost-efficient and customizable. The thought is that if the relationship between speech signals and vibrations were known, it could be used to transform the STI test signal to vibrations and then use the STImethod to estimate the speech intelligibility for the chosen contact microphone. This study, containing 22 men and women, evaluated the vibrations at the most suitable locations on the head for contact microphones and compared it with the speech signal of the same test person. Frequency responses were calculated for all locations of the head and a more detailed study showed that the frequency response of the neck may be approximated as a second order lowpass filter with a cut-off frequency of about 300 Hz that attenuates speech signals with higher frequencies. Experiments were also done to measure the STI value of a throat microphone with the known relationship. However, the results pointed out several problems that needs to be addressed before a STI method can be performed successfully. The results from this study may also be used to deeper study the relationships between different vibrations resulting from speech signals and suggestions on how the performance of contact microphones may be improved are given.

The study of the sound of impact has an extensive history with seminal studies done by Rayleigh. Rayleigh studied the subsequent vibrations of an impacting elastic sphere on a solid surface formulating a preliminary, analytical theory for rigid body radiation based on energy considerations. For a spherical particle incident on a non-wetted substrate, the sound pressure level can be related to the particle’s impulsive acceleration. Impact onto wetted substrates is less understood for the cases of hydrophobic surfaces. We investigate the impact of visco-elastic water beads (12.5 mm, 17.5 mm diameter) from heights 35–400 cm upon hydrophilic and hydrophobic liquid pools (0.15–0.45 ml). A hydrophobic surface is obtained by coating the metallic surface of a contact microphone with Soft99 Glaco Mirror Coat Zero water repellant. Sound production is measured using both contact and air microphones, which are synchronized with a high-speed camera (Phantom V2512) for visualization. In the hydrophobic case at low heights, the rebound of the water bead results in the attachment and lift-off of the liquid. The collapse of entrained gas cavities corresponds to an oscillatory acoustic signature upon lift-off. At higher impact heights, the liquid pool is atomized with signatures of drop break-up.

In this work, a speaker recognition approach using a contact microphone is developed and presented. The contact passive element is constructed from a piezoelectric material. In this context, the position of the piezoelectric transducer on the individual’s neck may greatly affect the quality of the collected signal and consequently the information extracted from it. Thus, the multilayered medium in which the sound propagates before being detected by the transducer is modeled. The best location on the individual’ neck to place a particular transducer element is determined by implementing Monte Carlo simulation techniques and consequently, the simulation results are verified using real experiments. The recognition is based on the signal generated from the vocal cords’ vibrations when an individual is speaking and not on the vocal signal at the output of the lips that is influenced by the resonances in the vocal tract. Therefore, due to the varying nature of the collected signal, the analysis was performed by applying the Short Term Fourier Transform technique to decompose the signal into its frequency components. These frequencies represent the vocal folds’ vibrations (50-1000 Hz). The features in terms of frequencies’ interval are extracted from the resulting spectrogram. Then, a 1-D vector is formed for identification purposes. The identification of the speaker is performed using two evaluation criteria, namely, the correlation similarity measure and the Principal Component Analysis (PCA) in conjunction with the Euclidean distance. The results show that a high percentage of recognition is achieved and the performance is much better than many existing techniques in the literature.; Dans ce travail, une approche de reconnaissance de l’orateur en utilisant un microphone de contact est développée et présentée. L'élément passif de contact est construit à partir d'un matériau piézoélectrique. La position du transducteur piézoélectrique sur le cou de l'individu peut affecter grandement la qualité du signal recueilli et par conséquent les informations qui en sont extraites. Ainsi, le milieu multicouche dans lequel les vibrations des cordes vocales se propagent avant d'être détectées par le transducteur est modélisé. Le meilleur emplacement sur le cou de l’individu pour attacher un élément transducteur particulier est déterminé en mettant en œuvre des techniques de simulation Monte Carlo et, par conséquent, les résultats de la simulation sont vérifiés en utilisant des expériences réelles. La reconnaissance est basée sur le signal généré par les vibrations des cordes vocales lorsqu'un individu parle et non sur le signal vocal à la sortie des lèvres qui est influencé par les résonances dans le conduit vocal. Par conséquent, en raison de la nature variable du signal recueilli, l'analyse a été effectuée en appliquant la technique de transformation de Fourier à court terme pour décomposer le signal en ses composantes de fréquence. Ces fréquences représentent les vibrations des cordes vocales (50-1000 Hz). Les caractéristiques en termes d'intervalle de fréquences sont extraites du spectrogramme résultant. Ensuite, un vecteur 1-D est formé à des fins d'identification. L'identification de l’orateur est effectuée en utilisant deux critères d'évaluation qui sont la mesure de la similarité de corrélation et l'analyse en composantes principales (ACP) en conjonction avec la distance euclidienne. Les résultats montrent qu'un pourcentage élevé de reconnaissance est atteint et que la performance est bien meilleure que de nombreuses techniques existantes dans la littérature.

This paper deals with the definition of calibration procedures that allow the traceability of parameters estimated by contact-microphone based vocal analyzers to be ensured. The parameters of interest are sound pressure level, fundamental frequency and voicing time percentage, which are commonly used for diagnostic purposes. The uncertainty of these parameters is estimated taking the contributions related to the defined calibration procedures into account and identifying the main influence quantities. A procedure is also proposed that is conceived to validate the results obtained at the end of a monitoring session performed with a vocal analyzer. Experimental results are reported that refer to a device developed by the authors, which uses an electret condenser microphone as the contact microphone.

This work deals with the duration of voicing and silence periods of continuous speech in rooms with very different reverberation times (RTs). Measurements were conducted using the Ambulatory Phonation Monitoring (APM) 3200 (Kaypentax, Montvale, NJ) and Voice-Care devices (developed at the Politecnico di Torino, Italy), both of which have a contact microphone placed on the base of the neck to detect skin vibrations during phonation. Six university professors and 22 university students made short laboratory monologs in which they explained something that they knew well to a listener 6 m away. Seven students also described a map with the intention of correctly explaining directions to a listener who drew the path on a blank chart. Longer speech samples were made by 25 primary school teachers in classrooms. A tendency to increase the voicing periods as the RT increased was on average observed for the university professors, the school teachers, and the university students who described a map. These students also showed longer silence periods than the students who made short monologs. The recognized trends concerned voice professionals or subjects who were highly motivated to make themselves understood in a perturbed speaking situation. Nonparametric statistical tests, which were applied to detect the differences in distributions of voicing and silence periods, have basically supported the findings.

Comprehensive evaluation of results obtained using acoustic and contact microphones in screening for laryngeal disorders through analysis of sustained phonation is the main objective of this study. Aiming to obtain a versatile characterization of voice samples recorded using microphones of both types, 14 different sets of features are extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We propose a new, data dependent random forests-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest is also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the linear predictive coefficients (LPC) and linear predictive cosine transform coefficients (LPCTC) exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for the classification. The proposed data dependent random forest significantly outperformed the traditional random forest.

We present FingOrbits, a wearable interaction technique using synchronized thumb movements. A thumb-mounted ring with an inertial measurement unit and a contact microphone are used to capture thumb movements when rubbing against the other fingers. Spectral information of the movements are extracted and fed into a classification backend that facilitates gesture discrimination. FingOrbits enables up to 12 different gestures through detecting three rates of movement against each of the four fingers. Through a user study with 10 participants (7 novices, 3 experts), we demonstrate that FingOrbits can distinguish up to 12 thumb gestures with an accuracy of 89% to 99% rendering the approach applicable for practical applications.

The current standard method for diagnosing bladder outlet obstruction in patients with Lower Urinary Tract Symptoms (LUTS) is a pressure-flow study. Electronic perineal auscultation, i.e. recording sound with a contact microphone behind the scrotum during voiding, might be an easy-to-use non-invasive alternative...

The article discusses experimental studies results of acoustic characteristics of three sensors: a contact microphone, accelerometer and stethoscopic microphone sensor. These sensors are designed for use in an electronic stethoscope, which allow to evaluate their acoustic efficiency. It was found that the most effective electro-acoustic sensors are contact microphone and accelerometer. The analysis of the main factors determining the efficiency of the studied sensors was carried.

In recent years, the growing interest in the recognition of voice disorders as occupational diseases has required screening methods adaptable to the clinical requirements, capable to extend the collection of baseline data. In this framework, the use of smartphones has gained increasing interest, thanks to advancements in digital technology, which made them suitable for recording and analyzing acoustic signals. Two smartphone applications, based on the Voice Care® technology, have been developed for long-term monitoring of voice activity when combined with a cheap contact microphone embedded in a collar. The applications have been tested in laboratory and used for the monitoring of teachers at kindergarten, primary school, and university. Vocal Holter App allows the selection of short and long term monitoring mode, and three different clusters of vocal parameters related to intensity, intonation, and load, respectively. Most of the results are based on the distributions of occurrences of vocal parameters. ...

An important aspect in Human-Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot's shell. In this work, we introduce a novel approach, where the audio acquired from contact microphones located in the robot's shell is processed using machine learning techniques to distinguish between different types of touches. The system is able to determine when the robot is touched (touch detection), and to ascertain the kind of touch performed among a set of possibilities: stroke, tap, slap, and tickle (touch classification). This proposal is cost-effective since just a few microphones are able to cover the whole robot's shell since a single microphone is enough to cover each solid part of the robot. Besides, it is easy to install and configure as it just requires a contact surface to attach the microphone to the robot's shell and plug it into the robot's computer. Results show the high accuracy scores in touch gesture recognition. The testing phase revealed that Logistic Model Trees achieved the best performance, with an F-score of 0.81. The dataset was built with information from 25 participants performing a total of 1981 touch gestures. The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU. Publicado

This longitudinal work explores the relationships between three analyses used for assessing teachers’ voice use: the voice monitoring during lessons that describes the teachers’ Vocal Behavior (VB), the perceptual assessment of voice by speech-language pathologists and the estimation of objective parameters from vocalizations to define teachers’ Vocal Performance (VP). About 30 Italian teachers from secondary schools were involved at the beginning and at the end of a school year. In each period, teachers’ vocal activity was monitored using the Voice Care device, which acquires the voice signal through a contact microphone fixed at the neck to estimate sound pressure level, fundamental frequency, and voicing time percentage. Once in each period, two speech-language pathologists performed a perceptual assessment of teachers’ voice using the GIRBAS-scale. On that occasion, teachers vocalized a sustained vowel standing in front of a sound level meter in a quiet room. Jitter, Shimmer and other parameters were ...

In the field of machining technology the vibration of the system machine, tool and workpiece during processing is the limiting factor of productivity. Therefore the process monitoring of vibration today plays an important role for the real time monitoring of machining processes, as well as for the optimization of simulation models. For monitoring workpiece vibrations, different kinds of strategies are in use. As the piezoelectric acceleration sensors were already field-tested at the department of machining technology (ISF), the use of Contact Emission-Sensors, which are customarily used for stringed instrument tuning, could pose an effective alternative strategy. Proving the possibility of using those sensors, could simplify the future choice of the process monitoring strategy for different machining operations and has cost saving potentials, due to an abbreviated measurement chain. Within this work the workpiece vibration during a 5-axis milling process were detected simultaneously by two monitoring strategies. In these experiments, the detection of vibration by a piezoelectric acceleration sensor was chosen as the reference strategy. The second technique deployed was the measurement of the contact-emission with a common contact microphone. On a milling machine for five-axis simultaneous machining, aluminium shafts were fixed in a three-jaw chuck one-sided and processed with an end mill. The geometry of the milling pockets was varied in the different processing sessions, as well as the feed parameters, in order to obtain both stable and unstable processing. The vibration measurements resulting from the two monitoring strategies were compared in form of time signal and frequency spectrum as well as in the combined form of a 3-dimensional waterfall diagram. Monitoring structure borne noise is an easy, cost-efficient alternative to measures with an acceleration sensor. Delivering reliable results concerning the vibration frequencies, which are multiples of the spindle rotation frequency, this method could be applied for process monitoring. The experiments have shown that the location of the Contact Emission-Sensors has major impact on the quality of results. To implement the Contact Emission-Sensors for this kind of measurements, the sensor must be positioned close to the place of vibration origin taking into account both damping characteristics of all components involved and the way of workpiece clamping.

We propose a method for gesture recognition that utilizes active acoustic sensing, which transmits acoustic signals to a target, and recognizes the target's state by analyzing the response. In this study, the user wore a contact speaker that transmitted ultrasonic sweep signals to the user's body and a contact microphone that detected the ultrasound propagated through the body. The propagation characteristics of the ultrasound changed depending on the user movements. We utilized these changes to recognize the user's gestures. The novel point of our method is that the user's gestures can be acquired not only from the apparent movement but also from the user's internal state, such as muscle activity, since ultrasound is transmitted to the user's internal body and body surface. We implemented a prototype device and investigated the performance of the proposed method in 21 contexts with 10 subjects. The evaluation results confirmed that the recognition accuracy was 91.6 % on average when we trained/tested using the same data set.

The Springboard is a do-it-yourself instrument made from readily available materials: An amplified soundboard makes the vibrations of coil springs and a variety of other small and nonprecious objects audible. Thanks to a simple piezo contact microphone, the Springboard's humble constitution belies the richness of its sounds, a signature of the author's activities in live and recorded works across many art disciplines.

We present a lightweight technique with which creators can prototype force-sensitive objects by attaching a pair of piezoelectric elements: one a vibration speaker and one a contact microphone. The key idea behind our technique is that touch force, in addition to the way the object is touched, can also be observed as different resonant frequency spectra. We also show that recognition of a touch and estimation of the touch force can be implemented by using the combination of support vector classification (SVC) and support vector regression (SVR). An experiment with an additional pressure sensor revealed that our technique might perform well in estimating touch force. We also show a tool for machine learning based on our technique that uses an animated guide, allowing creators to give the system both the training data and the labels for training machine learning needed for dealing with continuous-valued output such as SVR.

This paper describes a new type of contact vibration sensor made by bonding a piezoelectric polyvinylidene fluoride (PVDF) film to a curved frame structure. The concave surface of the film is bonded to a rubber piece having a front contact face. Vibration is transmitted from this face through the rubber to the surface of the PVDF film. Pressure normal to the surface of the film is converted to circumferential strain, and an electric field is induced by the piezoelectric effect. The frequency response of the device was measured using an accelerometer mounted between the rubber face and a rigid vibration exciter plate. Sensitivity (voltage per unit displacement) was deduced from the device output and measured acceleration. The sensitivity was flat from 16 Hz to 3 kHz, peaking at 6 kHz due to a structural resonance. A contact vibration sensor theory has been developed, which accounts for the effect of the radiation medium. It has been found that the imaginary part of the radiation impedance has an effect equivalent to the addition of mass to the curved PVDF film structure, which reduces the resonance frequency by about one order. Calculations predicting performance against human tissue (stethoscope or contact microphone) show results similar to data measured against the metal vibrator. This implies that an accelerometer can be used for calibrating a stethoscope or contact microphone. The observed arterial pulse waveform from the new PVDF sensor showed more low-frequency content than a conventional electronic stethoscope