Project-based learning through sensor characterization in a musical acoustics course.

Project-based learning engages students in practical activities related to course content and has been demonstrated to improve academic performance. Due to its reported benefits, this form of active learning was incorporated with an ongoing research project into an introductory, graduate-level Musical Acoustics course at the Peabody Institute of The Johns Hopkins University. Students applied concepts from the course to characterize a contact sensor with a polymer diaphragm for musical instrument recording. Assignments throughout the semester introduced students to completing a literature review, planning an experiment, collecting and analyzing data, and presenting results. While students were given broad goals to understand the performance of the contact sensor compared to traditional microphones, they were allowed independence in determining the specific methods used. The efficacy of the course framework and research project was assessed with student feedback provided through open-ended prompts and Likert-type survey questions. Overall, the students responded positively to the project-based learning and demonstrated mastery of the course learning objectives. The work provides a possible framework for instructors considering using project-based learning through research in their own course designs.

Considerations and Challenges for Real-World Deployment of an Acoustic-Based COVID-19 Screening System.

Coronavirus disease 2019 (COVID-19) has led to countless deaths and widespread global disruptions. Acoustic-based artificial intelligence (AI) tools could provide a simple, scalable, and prompt method to screen for COVID-19 using easily acquirable physiological sounds. These systems have been demonstrated previously and have shown promise but lack robust analysis of their deployment in real-world settings when faced with diverse recording equipment, noise environments, and test subjects. The primary aim of this work is to begin to understand the impacts of these real-world deployment challenges on the system performance. Using Mel-Frequency Cepstral Coefficients (MFCC) and RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) features extracted from cough, speech, and breathing sounds in a crowdsourced dataset, we present a baseline classification system that obtains an average receiver operating characteristic area under the curve (AUC-ROC) of 0.77 when discriminating between COVID-19 and non-COVID subjects. The classifier performance is then evaluated on four additional datasets, resulting in performance variations between 0.64 and 0.87 AUC-ROC, depending on the sound type. By analyzing subsets of the available recordings, it is noted that the system performance degrades with certain recording devices, noise contamination, and with symptom status. Furthermore, performance degrades when a uniform classification threshold from the training data is subsequently used across all datasets. However, the system performance is robust to confounding factors, such as gender, age group, and the presence of other respiratory conditions. Finally, when analyzing multiple speech recordings from the same subjects, the system achieves promising performance with an AUC-ROC of 0.78, though the classification does appear to be impacted by natural speech variations. Overall, the proposed system, and by extension other acoustic-based diagnostic aids in the literature, could provide comparable accuracy to rapid antigen testing but significant deployment challenges need to be understood and addressed prior to clinical use.

Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors.

Many commercial and prototype devices are available for capturing body sounds that provide important information on the health of the lungs and heart; however, a standardized method to characterize and compare these devices is not agreed upon. Acoustic phantoms are commonly used because they generate repeatable sounds that couple to devices using a material layer that mimics the characteristics of skin. While multiple acoustic phantoms have been presented in literature, it is unclear how design elements, such as the driver type and coupling layer, impact the acoustical characteristics of the phantom and, therefore, the device being measured. Here, a design of experiments approach is used to compare the frequency responses of various phantom constructions. An acoustic phantom that uses a loudspeaker to generate sound and excite a gelatin layer supported by a grid is determined to have a flatter and more uniform frequency response than other possible designs with a sound exciter and plate support. When measured on an optimal acoustic phantom, three devices are shown to have more consistent measurements with added weight and differing positions compared to a non-optimal phantom. Overall, the statistical models developed here provide greater insight into acoustic phantom design for improved device characterization.

Soft CNT-Polymer Composites for High Pressure Sensors.

Carbon−polymer composite-based pressure sensors have many attractive features, including low cost, easy integration, and facile fabrication. Previous studies on carbon−polymer composite sensors focused on very high sensitivities for low pressure ranges (10 s of kPa), which saturate quickly at higher pressures and thus are ill-suited to measure the high pressure ranges found in various applications, including those in underwater (>1 atm, 101 kPa) and industrial environments. Current sensors designed for high pressure environments are often difficult to fabricate, expensive, and, similarly to their low-pressure counterparts, have a narrow sensing range. To address these issues, this work reports the design, synthesis, characterization, and analysis of high-pressure TPU-MWCNT based composite sensors, which detect pressures from 0.5 MPa (4.9 atm) to over 10 MPa (98.7 atm). In this study, the typical approach to improve sensitivity by increasing conductive additive concentration was found to decrease sensor performance at elevated pressures. It is shown that a better approach to elevated pressure sensitivity is to increase sensor response range by decreasing the MWCNT weight percentage, which improves sensing range and resolution. Such sensors can be useful for measuring high pressures in many industrial (e.g., manipulator feedback), automotive (e.g., damping elements, bushings), and underwater (e.g., depth sensors) applications.

Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring.

Acoustic sensors are able to capture more incident energy if their acoustic impedance closely matches the acoustic impedance of the medium being probed, such as skin or wood. Controlling the acoustic impedance of polymers can be achieved by selecting materials with appropriate densities and stiffnesses as well as adding ceramic nanoparticles. This study follows a statistical methodology to examine the impact of polymer type and nanoparticle addition on the fabrication of acoustic sensors with desired acoustic impedances in the range of 1-2.2 MRayls. The proposed method using a design of experiments approach measures sensors with diaphragms of varying impedances when excited with acoustic vibrations traveling through wood, gelatin, and plastic. The sensor diaphragm is subsequently optimized for body sound monitoring, and the sensor's improved body sound coherence and airborne noise rejection are evaluated on an acoustic phantom in simulated noise environments and compared to electronic stethoscopes with onboard noise cancellation. The impedance-matched sensor demonstrates high sensitivity to body sounds, low sensitivity to airborne sound, a frequency response comparable to two state-of-the-art electronic stethoscopes, and the ability to capture lung and heart sounds from a real subject. Due to its small size, use of flexible materials, and rejection of airborne noise, the sensor provides an improved solution for wearable body sound monitoring, as well as sensing from other mediums with acoustic impedances in the range of 1-2.2 MRayls, such as water and wood.

Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope.

Electronic stethoscopes offer several advantages over conventional acoustic stethoscopes, including noise reduction, increased amplification, and ability to store and transmit sounds. However, the acoustical characteristics of electronic and acoustic stethoscopes can differ significantly, introducing a barrier for clinicians to transition to electronic stethoscopes. This work proposes a method to process lung sounds recorded by an electronic stethoscope, such that the sounds are perceived to have been captured by an acoustic stethoscope. The proposed method calculates an electronic-to-acoustic stethoscope filter by measuring the difference between the average frequency responses of an acoustic and an electronic stethoscope to multiple lung sounds. To validate the method, a change detection experiment was conducted with 51 medical professionals to compare filtered electronic, unfiltered electronic, and acoustic stethoscope lung sounds. Participants were asked to detect when transitions occurred in sounds comprising several sections of the three types of recordings. Transitions between the filtered electronic and acoustic stethoscope sections were detected, on average, by chance (sensitivity index equal to zero) and also detected significantly less than transitions between the unfiltered electronic and acoustic stethoscope sections ( ), demonstrating the effectiveness of the method to filter electronic stethoscopes to mimic an acoustic stethoscope. This processing could incentivize clinicians to adopt electronic stethoscopes by providing a means to shift between the sound characteristics of acoustic and electronic stethoscopes in a single device, allowing for a faster transition to new technology and greater appreciation for the electronic sound quality.