bluuebunny/crossref_metadata_2025 huggingface dataset:
Invertible neural networks for reconstructing acoustic fields
Authors: Karakonstantis Xenofon , Fernandez-Grande Efren
Published: May 2022

Sound field reconstruction from finite measurement arrays provides a means to interpolate and extrapolate acoustic quantities that describe the field. By assuming a linear projection on a basis that follows a principled source propagation, one can recover accurate estimates of the aforementioned sound fields. However, the recovery of the basis coefficients relies on explicit models of which measurement noise and data incompleteness can profoundly affect the uncertainty of the solution. This work aims to estimate the distribution of the underlying pressure conditioned on the observations of the measured pressure in a room. A framework for approximate inference is adapted for sound field reconstruction by applying generative flow-based models and invertible neural network architectures. In particular, we use conditional normalising flows for fast conditional posterior estimation and uncertainty quantification. The model's evaluation is carried out using experimental data measured with a spherical array and compared to hierarchical Bayes with Markov Chain Monte-Carlo sampling.
Pleasantness assessment of electric vehicle interior sounds using a long short-term memory model
Authors: Doleschal Florian , Verhey Jesko
Published: May 2022

The electrification of the powertrain strongly reduces the sound pressure level in the vehicle interior. However, the absence of a broadband noise originating from the combustion engine unmasks high-frequency tonal components of the electric motor and the gearbox. As they are commonly audible in transient driving conditions, the related psychoacoustic parameters and their influence on the pleasantness have to be considered dynamically. This study presents a sequence-to-one regression model on the basis of a long short-term memory (LSTM) neural network, which models the relation between times series of psychoacoustic parameters and the overall (i.e., single-valued) pleasantness. The advantage of LSTM-based models is that they consider interdependencies between time steps. The data set to be modelled mainly consisted of pleasantness ratings of recorded sounds from the vehicle interior of pure-electric and hybrid vehicles. The data set also includes pleasantness ratings of altered sounds, where synthetic components were added, amplified or attenuated using a sound separation algorithm. The model is highly accurate in predicting the data. Thus, the LSTM model can be used as an automated pleasantness assessment for the interior sound of vehicles with electrified drives.
Applying matched field array processing and machine learning to computational auditory scene analysis and source separation challenges
Authors: McElveen J. K. , Krasny Leonid , Nordlund Scott
Published: May 2022

Matched field processing (MFP) techniques employing physics-based models of acoustic propagation have been successfully and widely applied to underwater target detection and localization, while machine learning (ML) techniques have enabled detection and extraction of patterns in data. Fusing MFP and ML enables the estimation of Green’s Function solutions to the Acoustic Wave Equation for waveguides from data captured in real, reverberant acoustic environments. These Green’s Function estimates can further enable the robust separation of individual sources, even in the presence of multiple loud, interfering, interposed, and competing noise sources. We first introduce MFP and ML and then discuss their application to Computational Auditory Scene Analysis (CASA) and acoustic source separation. Results from a variety of tests using a binaural headset, as well as different wearable and free-standing microphone arrays are then presented to illustrate the effects of the number and placement of sensors on the residual noise floor after separation. Finally, speculations on the similarities between this proprietary approach and the human auditory system’s use of interaural cross-correlation in formulation of acoustic spatial models will be introduced and ideas for further research proposed.
Underwater acoustic source detection using a median based estimator
Authors: Brooker Daniel J.
Published: May 2022

Recently a class of cross spectral density matrix (CSDM) estimators have been introduced based on the use of matrix medians to average signal dyads rather than the more conventional sample mean. Due to the medians robustness against outliers, these new estimators are intended to for use at low SNR or in highly cluttered areas. The median in this case is given a geometric interpretation, as the point which minimizes the sum of the distance to all of the samples. This geometric interpretation has been shown to be quite useful for matched field processing, and in this work it is applied to detection of unknown signals at low SNR. Several estimators are developed corresponding to different choices of non-Euclidean distance between CSDMs. These new estimators are then applied to a relative entropy based detection scheme which detects unknown signals by comparing them to insitu estimated noise and finding individual frequencies which are highly dissimilar from this noise estimate. Using data from the DSNCON-19 experiment the performance of these novel estimators is characterized by their Receiver Operating Characteristic (ROC) curve and compared to the maximum likelihood estimator (MLE) performance. [Work supported by the Office of Naval Research.]
Modeling and learning underwater acoustic channel parameters through deep recursive neural networks
Authors: Zhang Fumin , Zhang Ziqiao , Tong Feng
Published: May 2022

We present a method that employes deep recursive neural networks (RNN) to model channel parameters of underwater acoustic channels. A deep RNN has the capacity to incorporate multiple data streams, such as temperature, wind, wave height that might affect channel property. The RNN is first trained using collected data and synthetic data. The training data is collected by sensors inboard a buoy installed at the target deployment area, and the synthetic data is generated through a simulation program based on physics principles. After the RNN is deployed, a dynamic filter is applied to update the channel parameter estimation based on real-time measurements in operations. We present several candidates for the dynamic filtering algorithms and compare their performances with other channel modeling techniques. It is observed that RNN demonstrates improved performance while sufficient data is available.
Underwater acoustic localization using a modular differentiable model for acoustic wave propagation
Authors: Kari Dariush , Singer Andrew C.
Published: May 2022

Model-based algorithms for underwater acoustic localization are highly dependent upon environmental knowledge that is rarely available. Moreover, model-based localization usually involves optimization in high-dimensional spaces to find the best candidate for source localization. On the other hand, data-driven methods deliver poor generalization performance in data starved scenarios. Therefore, in order to improve generalization, we propose a modular, deep learning-based architecture that inherently learns the multipath structure, while learning the environmental properties from the training data. Furthermore, since ReLU-based fully connected networks cannot capture the high-frequency contents of the signals of interest properly, we use sinusoidal activations. Although the model needs an estimate of the carrier frequency as a hyper-parameter, it is observed that it can tolerate deviations about the true frequency. This model maps the source and receiver locations to received signals and is then used in a gradient descent manner exploiting the automatic differentiation toolbox in PyTorch, to find the source location. To evaluate the performance of the algorithm, we use Bellhop to generate data for a given set of environmental parameters. We show that our method outperforms matched-field processing and a deep fully connected network without a modular structure.
Lake experimentation of in-band full-duplex underwater acoustic communications
Authors: Guo Zheng , Song Aijun , Towliat Mohammad , Cimini Leonard , Xia Xiang-Gen
Published: May 2022

In-band full-duplex (IBFD) communications is a promising technique to achieve spectral-efficient communication in the underwater environment. Characteristics of self-interference and methods to suppress the self-interference are crucial to attaining IBFD acoustic communications. In this effort, we analyze the characteristic of the self-interference for the underwater acoustic channel through experimental measurements. Self-interference reduction using physical separation and digital cancellation is investigated under different transmitter-receiver geometries. An iterative IBFD receiver combining joint channel estimation, SI cancellation, and multichannel decision feedback equalizer is developed. The feasibility of coherent IBFD underwater acoustic communications was tested in a lake environment. The self-interference suppression methods and the receiver performance were evaluated using the experimental measurements. IBFD communications was demonstrated at 80 m. With 10 dB local self-interference source level reduction, IBFD communications was extended to the 170 m range. [Work supported by the National Science Foundation.]
Recital halls for K-12, higher education, and community music education: A trio of case studies
Authors: Markham Benjamin E. , Sacks Jonah
Published: May 2022

The recital halls at Deerfield Academy, UMass Boston, and the Brattleboro Music Center each play a critical role at the center of the music education programs at these institutions. Deerfield's 160-seat Elizabeth Wachsman Concert Hall, at the independent high school's Hess Center for the Arts, is both intimate and warmly resonant, and features adjustable curtains at the stage to suit varying repertoire for rehearsal and performance, particularly of Deerfield's student music ensembles. The 150-seat recital hall at UMass Boston's University Hall serves an even wider array of ensembles, from classical choral to jazz, in a building that not only serves the Performing Arts Department but also Chemistry and other general-purpose classrooms; the adjustable curtains there surround the audience on all sides. In Brattleboro, a 331-seat recital hall is the crown jewel of a new community music center that also serves as the home to a Chamber Music Series and a range of ensembles including Juno Orchestra, a concert choir, chorale, and camerata, and the music center's “Educate. Open. Strengthen.” Program. These three rooms all serve critical pedagogical missions, but each with a different focus and a different set of needs and constraints that impact budget, architecture, and acoustical design considerations.
Too loud? Sound absorption and volume as related to music educators’ subjective response to rehearsal room loudness
Authors: Shankar Arjun K. , Conant David A. , Hoover K. Anthony
Published: May 2022

Controlling loudness is important for successful music rehearsal rooms. Volume, sound absorption, and often shaping strongly influence music faculty acceptance regarding room loudness. We assessed various rehearsal rooms, as well as data collected by Ron McKay in his BBN days, regarding the sound absorption, volume, and educators’ subjective responses to loudness within the rooms, in an attempt to better understand correlations among these parameters.
Toward a more versatile venue: The Hahn Recital Hall renovation at the Music Academy of the West, Montecito, CA
Authors: Conant David A.
Published: May 2022

The Music Academy of the West was founded in 1947 by German operatic soprano and lieder singer, Lotte Lehmann and Otto Klemperer and has evolved to be regularly lauded as the “Julliard of the West.” Because their 1970s, 350-seat Recital Hall could no longer properly serve as a suitable recital hall or rehearsal hall, MCH was retained in 2005 to design a full overhaul to ensure the hall could wonderfully serve ensembles ranging from 2 or 3 musicians to the 70-piece Santa Barbara Symphony in rehearsal. It serves now as the home of Marilyn Horne’s Vocal Institute. We present our design process that involved major reshaping, mitigating rail traffic 300 ft away, a new HVAC system, a sensitive re-do of the copious stage (including mitigating practice rooms below), designing novel recital screens, providing variable acoustics and an 11th hour effort to accommodate “Met Live!”.
National Oceanic and Atmospheric Administration (NOAA) and Bureau of Ocean Energy Management (BOEM) minimum recommendations for use of passive acoustic listening systems in offshore wind energy development monitoring and mitigation programs
Authors: Sisson Nicholas B. , Baker Kyle , Daly Jaclyn , Davis Genevieve , Esch Carter , Guan Shane , Scholik-Schlomer Amy , Staaterman Erica , Van Parijs Sofie
Published: May 2022

Offshore wind energy development is rapidly advancing in United States waters to meet state and federal renewable energy goals. With a diverse suite of endangered large whale species and a multitude of other protected marine species inhabiting these same waters, understanding the potential consequences of construction and operation activities is essential to advancing responsible offshore wind development. Passive acoustic monitoring (PAM) represents a newer technology that has become one of several methods of choice for monitoring trends in the soundscape, presence of species, mitigating risk, and monitoring potential behavioral and distributional changes resulting from offshore wind activities. Federal and State regulators, the offshore wind industry, and environmental advocates require detailed information on PAM capabilities and techniques needed to promote efficient, consistent, and meaningful data collection efforts on local and regional scales. We provide capabilities and suggested applications of archival and real-time PAM systems, PAM study design considerations, and data management needs. We also provide key considerations for long-term baseline monitoring and vessel strike risk reduction using PAM. These recommendations provide an initial guide for stakeholders seeking to use PAM systems associated with the rapid expansion of offshore wind development in the United States.
Passive acoustic monitoring during the construction of the Coastal Virginia Offshore Wind project
Authors: Lin Ying-Tsong , Newhall Arthur E. , Miller James H. , Potty Gopu R. , Amaral Jennifer L. , Frankel Adam S. , Mason Tim , Ampela Kristen , Khan Anwar A.
Published: May 2022

A suite of hydrophone arrays was deployed to monitor pile driving sound and seafloor particle motion during construction of two wind turbine towers in the Coastal Virginia Offshore Wind (CVOW) project in May 2020. The primary objective of this passive acoustic monitoring (PAM) work was to assess the effectiveness of bubble curtains for underwater noise mitigation. The arrangement of the PAM tracks also enabled studies of azimuthal and range dependencies of pile driving sound propagation. Measurements of water temperature, salinity and surface heights were made during the monitoring period to assess underwater sound propagation conditions. PAM data showed that the bubble curtain effectively reduced the pile driving noise above 200 Hz, and a significant azimuthal dependency was observed. Statistical analyses of pile driving noise will be presented, along with recommendations for future wind farm construction monitoring, especially on marine mammal acoustic monitoring during construction. [Work supported by the Bureau of Ocean Energy Management]
Research priorities for sound and vibration effects on fishes and aquatic invertebrates from offshore wind energy development
Authors: Thomsen Frank , Popper Arthur N. , Williams Kathryn , Hice-Dunton Lyndie , Jenkins Edward , Higgs Dennis M. , Krebs Justin , Mooney Aran , Rice Aaron , Roberts Louise , Vigness-Raposa Kathy , Zeddies David
Published: May 2022

There are substantial knowledge gaps regarding both the bioacoustics and the responses of animals to sounds associated with pre-construction, construction, and operations of offshore wind (OSW) energy development. A workgroup of the 2020 State of the Science Workshop on Wildlife and Offshore Wind Energy recommended priority studies for the next five years to help stakeholders better understand potential cumulative biological impacts of sound and vibration to fishes and aquatic invertebrates as the OSW industry develops. The workgroup identified seven short-term priorities that include a mix of primary research and coordination efforts. Key research needs include the examination of animal displacement and other behavioral responses to sound, as well as hearing sensitivity studies related to particle motion, substrate vibration, and sound pressure. Other needs include: identification of priority taxa on which to focus research; standardization of methods; development of a long-term highly instrumented field site; and examination of sound mitigation options for fishes and aquatic invertebrates. Effective assessment of potential cumulative impacts of sound and vibration on fishes and aquatic invertebrates is currently precluded by these and other knowledge gaps. Filling critical gaps in knowledge will improve our understanding of possible sound-related impacts of OSW energy development to populations and ecosystems.
Weathering the storm: Impacts of hurricane-induced noise on the probability of detecting cetaceans
Authors: Tripathy Aditi , Miksis-Olds Jennifer , Lyons Anthony P.
Published: May 2022

Hurricanes Dorian (Category 5; 2019), Florence (Category 4; 2018), and Humberto (Category 3; 2019) impacted the soundscape as observed at the Atlantic Deepwater Ecosystem Observatory Network (ADEON) locations in the US Mid- and South Atlantic Outer Continental Shelf. Passive acoustic data obtained from bottom-mounted hydrophones at the ADEON locations were examined to assess changes in ambient sound before, during, and afterhurricane presence. Ambient sound level increased up to 25 dB in the 0.1–7 kHz band which could impact the detectability of cetaceans vocalizing at those frequencies. The probability of detection of fin whales, minke whales, and pilot whales was estimated using empirical ambient sound levels, modelled propagation loss, and pre-defined detection parameters. Detection area, as defined by range of minimum detectability of the cetacean from the receiver, was estimated before, during, and after hurricane presence at each ADEON location. Detection area changed considerably during hurricane presence with site-specific impacts for each of the cetaceans, which may affect estimates of their abundance from passive acoustic recordings. [Study concept, oversight, and funding were provided by ONR Award N00014-16-1-2594 and BOEM under Contract No. M16PC00003, in partnership with ONR and NOAA. Funding for ship time was provided under separate contracts by ONR, Code 32.]
Geoacoustic inversion for gassy sediment parameters using reflection and scattering of acoustic signals
Authors: Katsnelson Boris , Uzhansky Ernst , Katsman Regina , Lun'kov Andrey , Ivakin Anatoliy N.
Published: May 2022

Results of experimental study and theoretical modeling of acoustic propagation and reverberation in Lake Kinneret (Israel) with gassy sediments are presented. The presence of methane bubbles in sediments significantly influences reflection and scattering of sound signals from the bottom, which in turn allows estimating properties of sediment using acoustic sensing. Experiments were carried out using R/V Hermona with sound source located at a 7 m depth radiating 1 s-long 0.3–7 kHz LFM sweeps with intervals from 1 to 20 s. Acoustic pressure time series were received on a single hydrophone at ∼1 m from the source and two vertical arrays fixed in the lake center at either 40 m or 10 m from each other. Analysis of both monostatic and bistatic experimental data (including long range propagation) was aimed to estimate acoustic characteristics of the bottom and then to infer the related gas content and its spatial and temporal variability. The sound speed in sediments at different locations (with different depth of water layer, maximal depth 40 m) was estimated to be ∼170–250 m/s that corresponds to gas volume concentrations ∼1%, which is in accordance with direct measurements made using non-acoustic probes. [Work was supported by BSF grant 2018150.]
Tools for climate solutions: Developing techniques for marine carbon dioxide removal measurement, reporting and validation
Authors: Freeman Simon
Published: May 2022

Climate change impacts and mitigation strategies will define our interaction with the oceans this century. Marine carbon sequestration could facilitate the enormous scaling necessary for gigaton-level carbon dioxide (CO2) removal: at least 10 GT/y by 2050 and 20 GT/y by 2100, required just to limit anthropogenic warming to below 2°C. Many proposed marine CO2 removal techniques involve the distributed capture of carbon, i.e., accelerating the biological carbon pump (e.g., iron ocean fertilization or artificial upwelling) or shifting the dissolution equilibrium of CO2 (e.g., ocean alkalinity enhancement). However, technology that enables rapid, inexpensive, persistent and accurate measurement and validation of drawn-down CO2 at the sequestration time- and regional ocean spatial-scales necessary to quantify carbon capture does not exist today. The accuracy and wholeness of these future techniques will be important for assigning financial value to marine CO2 removal processes in a carbon market, as well as enabling thorough evaluation of environmental impacts and a comprehensive understanding of ocean carbon dynamics. I will discuss ARPA-E’s interest in carbon sensing approaches, including passive and active acoustic techniques, which could rapidly quantify ocean carbon flux at scale and introduce powerful tools to address the challenges of mitigating climate impacts at sea.
Ultrasonic implantable wireless system for deep-tissue oxygenation monitoring
Authors: Sonmezoglu Soner
Published: May 2022

Continuous real-time monitoring of physiological parameters can yield insights into the underlying aspects of many diseases and guide diagnostic and therapeutic decisions in surgeries and for critical care patients. Tissue oxygenation is one of the key physiological parameters and a critical determinant of organ function. Existing systems for monitoring deep-tissue oxygenation are limited by a few factors, including the need for wired connections, the inability to provide real-time data or operation restricted to surface tissues. We demonstrate the first minimally invasive ultrasonic wireless system to monitor deep-tissue O2 that avoids all these drawbacks. The system is composed of a millimeter-sized O2 sensor implant that communicates bi-directionally and digitally with an external transceiver outside the body, enabling deep-tissue O2 monitoring for surgical or critical care indications. The implant is based on integrated circuit and microsystems technologies that enable extreme miniaturization with advanced performance. Furthermore, the system includes a novel wireless power/data transmission strategy that enables the operation of the implant at centimeter-scale depths (up to 10 cm) in tissue. Overall, this technology represents a new class of monitoring and diagnostic system particularly suitable for organ monitoring, as well as other surgical or critical care situations.
Toward an ultrasonic Internet of medical things
Authors: Melodia Tommaso , Guida Raffaele , Santagati Enrico , Demirors Emrecan , Uvaydov Daniel , Johari Pedram , Jimenez Jorge
Published: May 2022

Wireless networked systems of “smart” miniaturized and electronically controlled implantable or wearable medical sensors and actuators will be the basis of many innovative and potentially revolutionary therapies and applications. The main obstacle in realizing this vision of smart networked implants is posed by the dielectric nature of the human body, which strongly attenuates radio-frequency electromagnetic waves used in traditional wireless technologies such as Bluetooth or WiFi. This talk will give an overview of our work exploring a radically different approach, i.e., establishing wireless networked systems in human tissues that transfer data and energy through acoustic waves at ultrasonic frequencies. We will start off by discussing applications of networked implantable medical systems.We will then analyze fundamental aspects of ultrasonic propagation in human tissues and their impact on the design of wireless networking protocols at different layers of the networking protocol stack. We will then review our work on designing and prototyping ultrasonically rechargeable and connected Internet-of-Things platforms through a closed-loop combination of mathematical modeling, simulation, and experimental evaluation.
Coupling and maintaining link efficiency ultrasonics in transcutaneous energy transfer systems
Authors: Leadbetter Jeffrey , Vihvelin Hugo , Kotiya Akhilesh , Zaenker Daniel , Norman Jeremy , Brown Jeremy , Adamson Robert B.
Published: May 2022

We present on the development of a 12.8 mm diameter ultrasonic transcutaneous energy transfer system for powering implanted hearing devices. The system was based on two custom 8mm diameter PMN-PT 1-3 composite transducers operating at 1.25MHz and featured three significant innovations. First, to ensure long-term biocompatibility and reliability of the implanted portion of the link and to ensure robust attachment to bone, the implanted transducer was designed into a 12.8 mm diameter, 3.5 mm thick hermetically sealed titanium package in an easy-to-implant form factor. The transducer was designed to deliver sound efficiently through the casing walls using a mass-spring acoustic matching technique. Second, for the external unit a “dry” acoustic coupling system based on a silicone pressure sensitive adhesive was designed that combined efficient acoustic coupling, robust adhesion to skin and that was comfortable to wear. The external unit was aligned with the internal unit using a magnetic alignment system. Third, we developed and demonstrated an efficient Class E transmit amplifier and receive electronics that incorporated compensation for changes in acoustic separation between the transducer to maintain robust, high efficiency power transmission. The system achieved 33% DC-to-DC electrical conversion efficiency through 5mm of water and 19% DC-to-DC efficiency through 5mm of porcine tissue.
Charging of devices for healthcare applications, using ultrasound wireless power
Authors: Makin Inder Raj S. , Jabs Harry , Mast T. Douglas , Radziemski Leon
Published: May 2022

Current wireless powering is frequently accomplished through inductive fields. An alternative for wireless powering systems is via ultrasound. Potential advantages are deeper penetration, delivery through metals, no electronic interference, and beam steerability to correct for misalignment. These characteristics enable ultrasound-based recharging of batteries located within a hermetically sealed enclosure, e.g., an implantable pulse generator (IPG). Another growth area for wireless power delivery is to support healthcare operations, when digital devices used for healthcare delivery need regular charging. Ultrasound Wireless Powering (UWP) is convenient, avoids cable clutter, and is safe. Planar ultrasound transducers operating between 1 and 3 MHz provide a practical compromise between useful directivity and effective propagation range. This talk will describe the development and evaluation of a UWP recharging system operating at 1 MHz transferring up to 100 mA at nominally 4 V, to charge a Li-Ion battery 10–20 mm through tissue. For potential neurostimulator applications, the receiver module was packaged within 1.2 cc inside a 17-cc titanium-IPG. For digital device charging with UWP, benchtop experiments will demonstrate characteristics to power smartphones comparable to the Qi-standard, version 1.0, which is an industry standard for characterizing charging with inductive fields. [Work partially supported by the NIH/NIBIB R43EB019225.]
Acoustic vibrations for versatile wireless power transfer
Authors: Tseng Victor Farm-Guoo , Radice Joshua J. , Lazarus Nathan , Bedair Sarah
Published: May 2022

We present an overview of prior/recent work done at the US Army Combat Capabilities Command—Army Research Laboratory on using acoustic waves for wireless power transfer (WPT). In the past we have demonstrated through-air ultrasonic phased array power beaming, through-metal longer distance transfer using Lamb waves, and power/data transfer to embedded sensor nodes. Acoustic WPT has the advantage of not being shielded by metal, and can achieve higher efficiencies than inductive WPT at larger distances. Part of our recent work is on developing a through-metal acoustic WPT based wireless UAV recharging platform. For the first time, we have demonstrated a self-detachable acoustic WPT receiver, using the switchable magnetic force from electro-permanent-magnets to attach a receiver transducer onto a steel platform to receive power (a transmitter is bonded on the other side of the platform), without the need of any liquid couplant. The magnetic force enhances the mechanical mode coupling, while allowing the receiver to attach/detach freely from the platform. AC-AC power efficiency up to 63% was achieved, and with the appropriate power electronics and matching network, an overall DC-DC efficiency of 44% was achieved when charging a 3-cell LiPo UAV battery with 4.6 W, with higher power levels achievable.
A remote lab exercise to introduce students to the fundamentals of bioacoustics using smartphones and R
Authors: Clink Dena J. , Comella Isabel , Zafar Maryam
Published: May 2022

Animals communicate through a variety of modalities including visual, auditory and chemical. Acoustic signals are particularly well suited for studying the evolution of animal communication due to the relative ease in which sounds can be recorded and analyzed. Therefore, a thorough overview of acoustic communication is a fundamental aspect of any animal behavior course. The purpose of this lab exercise is to introduce students to bioacoustics, or the study of animal sounds and their habitats. For the field component, students use their smartphones to collect focal recordings of target animals as well as collect acoustic data that will be used to investigate variation in soundscapes at different times (e.g. dawn and dusk) and/or different locations (e.g., urban versus rural). The computer lab component utilizes the R programming environment to import sound files and visualize differences in acoustic data using both spectrograms and principal component analysis biplots. The computer lab exercises are meant for undergraduate students with relatively little coding background but could be easily adapted for students of different skill levels. All materials needed to use and modify the course materials are openly available on GitHub, which should remove any financial barriers for students or instructors.
Affordable and customizable high-resolution scanning system for measurement of acoustic fields
Authors: Cushing Colby W. , Parker Samuel D. , Kang Janghoon , Venegas Gabriel R. , Wilson Preston S. , Haberman Michael R.
Published: May 2022

Spatial scans of acoustic fields are often required to characterize acoustic system behavior. Automated positioning systems allow for higher spatial resolution, more efficient use of time, and increased repeatability and precision during data collection than is possible manually. Unfortunately, commercial systems can be expensive, difficult to customize after purchase, and can be very cumbersome to manipulate. The present work describes an affordable and customizable scanning system that can be operated through MATLAB using an Arduino microcontroller and constructed with OpenBuilds hardware. We will outline the integration of an oscilloscope and function generator with the motion control system to enable a completely automated measurement system. Further, we will discuss potential pitfalls one may encounter when creating a similar system and highlight best practices that allow you to avoid those problems at the system design stage. This work will discuss similar systems created to characterize underwater acoustic metamaterials (doi:10.1121/10.0009161), quantify performance of an additively manufactured acoustic diffuser (doi:10.1121/1.5147148), define pressure field responses due to inclusion of an acoustic metasurface used for acoustic holography (doi:10.26153/tsw/13999), and analyze acoustic properties of cylindrical sediment samples containing infauna (doi:10.1121/2.0000744). [Work sponsored by ARLUT, ONR, Sandia, and Samsung.]
Low cost acoustic transducer calibration system
Authors: Brown David A. , Bachand Corey , Souza Austin , Di Bona Isabella , Kanaley Jesse
Published: May 2022

An acoustic calibration system was developed primarily for underwater acoustic transducers, although applicable for a variety of acoustic applications. Historically, such systems have been large, highly customized, and very expensive. With the advent of compact cost-effective digital PC-based oscilloscopes and data acquisitions systems (e.g., devices from TiePie, Cleverscope, and Digilent), a development that began as a senior capstone-design project has recently been demonstrated. Our system is based on the TiePie Digital Oscilloscope, a PC based signal generation and acquisition systems, a power amplifier, optional preamplifier and stepper motor with controller for radiation patterns. More specifically, the receive sensor signals are acquired with a TiePie Handyscope HS6 DIFF (1000MS/s, USB 3.0 oscilloscope, with four differential analogue channels) and the transmit signal (signal generation, transmit voltage and current acquisition) utilizes a TiePie HS5-540XM 5 (500 MHz, 1-channel generator, 2-channel acquisition) measuring system. The calibration and data display is controlled with MATLAB and controlled through a graphical user interface (GUI). Examples of Transmit Pressure Response per Volt (TR/V or TVR), Free Field Voltage Sensitivity (FFVS), and Beam Pattern measurements will be provided. [Work supported by BTech Acoustics LLC.]
Leveraging audio hardware for underwater acoustics
Authors: Blanford Thomas E. , Garrett Luke , Park J. Daniel , Brown Daniel C.
Published: May 2022

Both active and passive underwater acoustic systems frequently use purpose built hardware that is designed for a particular application. Transducers, electronics, and data acquisition systems for field experiments, therefore, are often expensive, tailored to limited frequency bands, or packaged for integration with a specific platform. Reliance on custom hardware, however, can make initial experimental investigations of new sensing paradigms cost prohibitive. The expense also creates an entry barrier to experimental work for students and researchers outside the underwater acoustics community. Audio hardware, however, is widely available, easily integrated using commercial data acquisition tools, and is often of relatively low cost. This presentation will describe two efforts to use audio hardware for inexpensive experimental investigations of underwater acoustics topics. The first, AirSAS, uses audio transducers and electronics to investigate synthetic aperture sonar problems in air that are analogous to those underwater. The second, the Citizen Scientist Hydrophone, integrates underwater transducers with consumer audio electronics to make an inexpensive, multipurpose passive sensing device. Challenges and limitations of using commercial audio hardware for underwater acoustics, especially as they relate to accuracy and data quality, will be discussed.
Location-aware synthesis of walkable urban soundscapes in room-centered immersive virtual reality system
Authors: Huang Mincong , Braasch Jonas
Published: May 2022

This work, situated at Rensselaer’s Collaborative-Research Augmented Immersive Virtual Environment Laboratory (CRAIVE-Lab), demonstrates a system that utilizes the facility’s panoramic display and multichannel wave field synthesis loudspeaker array to simulate navigable human-scale urban environments with automatically generated virtual soundscapes. The system positions the CRAIVE-Lab’s virtual footprint within the Unity game engine and provides it with the capability to move within virtual space. With geo-location input, the system uses ArcGIS to extract geospatial features, suchas urban topologies and building extrusions. The same input is also used to retrieve real-time weather data from open-source databases (i.e., OpenWeather). Based upon the extracted information, the system updates acoustic signatures of the virtual surroundings by performing a multi-channelray-tracing analysis at a fixed time frame. The resulting signatures are then used to generate environmental noise profiles and process auto-generatedvirtual sound sources present in the environment using wave field synthesis and an extension of multiple audio datasets typically used for model training in urban sound classification (i.e., UrbanSound8K). We present the results as part of an in situ audiovisual experience where users can stand in the CRAIVE-Lab’s physical enclosure and walk about the virtual landscape in which they are immersed.
Generative design of acoustic metamaterials conditioned on desired transmission characteristics
Authors: Gurbuz Caglar , Marburg Steffen
Published: May 2022

Metamaterials gain increasing interest in acoustics due to their sound insulation abilities. The special arrangement of unit cells allows them to influence the way sound propagates in acoustic media. To date, a system-based approach is mainly adopted in the design process. This approach is accompanied with two drawbacks: Firstly, the design relies primarily on the expertise of specialists making the current process prone to subjective assessments. Secondly, the relevant target criteria can only be evaluated at the end of design phase. This is accompanied with substantial time and resource expenses and is even amplified when large scale simulations or physical experiments are involved. It is the aim of this contribution to introduce a design assistant which supports specialists in the design of acoustic metamaterials. The proposed assistant system aims at a criteria-centric design scheme. By this means, target criteria, which are only accessible at the final stages, can be considered early in the design process. Therefore, a conditional generative adversarial network is implemented. This network proposes cell candidates conditioned on the desired transmission characteristics for the metamaterial. To validate our method, simulations with the finite element method are performed. The findings of this investigation suggest an important role for generative adversarial networks in the design process of acoustic metamaterials. As such, the proposed framework allows to design cell candidates for problem-specific applications.
Acoustic metamaterial inverse design based on machine learning using Gauss–Bayesian models
Authors: Yang Jing , Xu Zi-Xiang , Chen An , Liang Bin , Cheng Jian-chun
Published: May 2022

The emergence of acoustic metamaterials (AMs) provides distinct response to acoustic waves unattainable in nature. However, conventional AMs design has relied on extensive experimentation and a trial-and-error approach requiring significant computational resource. Here, we introduce an inverse-design method based on machine learning using Gauss–Bayesian (GB) models and implement it on low-frequency broadband sound attenuating devices. By utilizing the superiority of GB model on multi-parameter design and optimization efficiency, we successively customize ventilated microperforated sound-absorbing structure, sound absorbers with coherently coupled weak resonances (CCWRs) and ventilated meta-silencers with acoustic consecutive Fano resonances (ACFRs). Under the judiciously adaptive acquisition function and Matérn kernel, the designed GB frameworks with small samples are available to seek the optimal solution established on above physical effects by the cycle of training and prediction with high accuracy and efficiency. Good agreements between numerical and experimental results further verify the effectiveness of the proposed method. With the advantage of low cost, high efficiency and flexibility, our GB-based machine learning method paves the way for tremendous opportunities in future design of various metamaterial-based applications, as well as far-reaching implications for acoustics-related fields.
Acoustic metamaterial design using Conditional Wasserstein Generative Adversarial Networks
Authors: Lai Peter , Amirkulova Feruza
Published: May 2022

This talk presents a method for generating planar configurations of scatterers with a reduced total scattering cross section (TSCS) by means of generative modeling and deep learning. The TSCS minimization via repeated forward modeling techniques, trial-error methods, and traditional optimization methods requires considerable computer resources and time. However, similar or better results can be achieved more efficiently by training a deep learning model to generate such optimized configurations producing low scattering effect. In this work, the Conditional Wasserstein Generative Adversarial Networks (cWGAN) is combined with Convolutional Neural Networks (CNN) to create the generative modeling architecture [1]. The generative model is implemented with a conditional proponent to allow the TSCS targeted design generation and is enhanced with the coordinate convolution (CordConv) layer to improve the model’s spatial recognition of cylinder configurations. The cWGAN model [1] is capable of generating images of 2D configurations of scatterers that exhibit low scattering. The method is demonstrated by giving examples of generating 2-cylinder and 4-cylinder planar configurations with minimal TSCS. [1]. P. Lai, F. Amirkulova, and P. Gerstoft. “Conditional Wasserstein generative adversarial networks applied to acoustic metamaterial design,” J. Acoust. Soc. Am. 150(6), 4362–4374 (2021).
Designing acoustofluidic devices using a simplified physics-informed machine learning approach
Authors: Raymond Samuel J. , Collins David , Willams John
Published: May 2022

The safe positioning of particles within an acoustofluidic device is critical in biomedical and biological applications. Relating the design of acoustofluidic device walls and the internal acoustic field is a complex, nonlinear problem. The field of Physics-Informed Machine Learning (PIML) offers a number of potential approaches to simplify the design of these devices. One such PIML approach is learning from synthetic data. With large scientific data sets with rich spatial-temporal data and high-performance computing providing large amounts of data to be inferred and interpreted, the task of PIML is to ensure that these predictions and inferences are enforced by, and conform to the limits imposed by physical laws. The tools employed in PIML can include large, deep neural networks, Bayesian modeling, and deep reinforcement learning with sophisticated simulations of the environment. In this work, we show a simplified version of PIML using a combination of a small fully connected neural network and a 2D meshfree simulator of acoustic devices to predict the boundary shape for an acoustically actuated device. We will discuss the real-world results and applications, as well as the current limitations of this approach and the path ahead to scale and include more complexity for more applications and designs.
Reinforcement learning for acoustic metamaterial design
Authors: Shah Tristan A. , Amirkulova Feruza
Published: May 2022

This talk presents our findings and methodologies in applying reinforcement learning to the design of acoustic metamaterials [1]. Our reinforcement learning agents are capable of discovering configurations of cylindrical scatterers in water which minimize the scattering of an acoustic plane wave. These agents are successful in the optimization of two different parametric designs, i.e., radii and positions of each scatterer. The resultant designs produced by reinforcement learning algorithms such as double deep Q-learning network and deep deterministic policy gradient algorithms are comparable and in some cases superior to those produced by the gradient-based optimization solver such as fmincon. However, significant computational resources are required to train reinforcement learning models to completion. This poses a significant challenge when attempting to increase the complexity of a design; as training times increase to unfeasible levels. Methods to counteract this challenge are discussed in this talk. These methods include utilization of the Julia programming language as well as multiprocessing and multithreaded programming. Together they create a synergy which reduces training times by orders of magnitude. [1] T. Shah, L. Zhuo, P. Lai, A. Rosa-Moreno, F. Amirkulova, and P. Gerstoft, “Reinforcement learning applied to metamaterial design,” J. Acoust. Soc. Am. 150(1), 321–338 (2021).
Feasibility study for the hearing assessment of classical music student using Otoacoustic Emissions
Authors: Dance Stephen , Ballestero Eric
Published: May 2022

Since the introduction and enforcement of the Control of Noise at Work Regulations in 2008 continuing research has been undertaken on five thousand students of the Royal Academy of Music to understand their hearing acuity. Standard audiometric screening methods were employed for both entry and exit testing. It was established that the music students obtained results that in over 50% of cases presented negative hearing losses as compared against the Health and Safety Executive categories. This presented an interesting problem which has now been addressed in an attempt to understand the hearing acuity of classical musicians using an otoacoustic emission based methodology. A feasibility study was undertaken in July 2021 of 120 musicians using Hearing Coach software for the analysis and Path Medical OAE instrument to undertake the measurements. The results presented show similar trends and thus provide reassurance that OAE can be used to assess the hearing of music students.
Neural markers of improved auditory selective attention following neurofeedback training
Authors: Shim Hwan , Kim Subong , Lee Jae Hee , Gibbs Leah , Rush Karsyn , Choi Inyong
Published: May 2022

We designed a neurofeedback training paradigm to enhance the attentional modulation of cortical auditory evoked responses. Two concurrent speech streams—a female voice repeating “Up” five times and a male voice repeating “Down” four times in three secondsi—were played from left and right loudspeakers, respectively. Subjects were instructed to attend one of those streams by a pre-stimulus visual cue (e.g., “Target: Up”). Attention was decoded from single-trial EEG signals. Subjects received neurofeedback (i.e., a visual object on the computer screen moves upward if the EEG decoder determines the “up” stream was attended; or vice versa). Subjects repeated this training for four sessions across four weeks. During the last session, compared to the first, subjects exhibited strengthened alpha oscillation in the right parietal cortex while they attended right and ignored left, indicating that spatial inhibitory processing to suppress sounds left was enhanced. The temporal cortex exhibited greater attentional modulation of beta oscillation after the four weeks of training, indicating enhanced neural activity to predict the target. The strength of attentional modulation on cortical evoked responses to sounds was also improved. These results prove that neurofeedback training effectively enhances the cortical processing for auditory selective attention.