4pNS2 – Use of virtual reality in designing and developing sonic environment for dementia care facilities

Arezoo Talebzadeh – arezoo.talebzadeh@UGent.be
Ph.D. Student
Ghent University
Tech Lane Ghent Science Park, 126, B-9052 Gent, Belgium

Popular version of 4pNS2 – Use of virtual reality in designing and developing soundscape for dementia care facilities
Presented in the afternoon of May 26, 2022
182nd ASA Meeting in Denver, Colorado
Click here to read the abstract

Sound is essential in making people aware of their environment; sound also helps in recognizing the time of the day. People with dementia have difficulties understanding and identifying their senses. The sonic environment can help them navigate through the space and realize the time; it can also reduce their agitation and anxiety. Care facilities and nursing homes, and long-term cares (LTC) usually have an unfamiliar acoustic environment for anyone new in the place. A well-designed soundscape can enhance the feeling of safety, elevate the mood and enrich the atmosphere. Designing the soundscape that fosters well-being for a person with dementia is challenging as mental disorders change one’s perception of space. Soundscape is the sonic environment as perceived by a person in context.

This research aims to enhance the soundscape experience during the design and development of care facilities by using Virtual Reality and defining the context during the process.

Walking through the space while hearing the soundscape demonstrates how sound helps spatial orientation and understanding of time. Specific rooms can have a unique sound dedicated to them to help residents find the location. Natural soundscape in the lounge or sounds of coffee brewing in the dining room during breakfast. Birds sound inside residents’ rooms during the morning to elevate their mood and help them start their day.

Sound is not visual (tangible); therefore, it is hard to examine and experience the design before implementation. Virtual Reality is a suitable tool for demonstrating sound augmentation and the outcome. By walking through the space and listening to the augmented sonic environment, caregivers and family members can participate during the design process as they are most familiar with the person with dementia and their interests. This method helps in evaluating the soundscape. People with dementia have a different mental model. Virtual Reality can help feature diverse mental models and sympathize with people with dementia.

2aNS7 – Directional Processing in Assessment of Wind Turbine Noise

Alexander Sutin -asutin@stevens.edu
Hady Salloum – hsalloum@stevens.edu
Alexander  Sedunov- asedunov@steves.edu
Nikolay Sedunov – nsednov@stevens.edu

Stevens Institute of Technology
Sensor Technologies & Applied Research (STAR) Center
Hoboken, NJ  07030
Click here to read the abstract

Popular version of 2aNS7 – Directional Processing in Assessment of Wind Turbine Noise
Presented Tuesday morning,  May25, 2022, 10:50-11:05 AM, Mountain
182nd ASA Meeting, Denver

 

Assessments of Wind Turbine Generator (WTG) noise are required to comply with the US Environmental Agency and local governments and avoid legal action that may result of non-compliant operation. Current methods for WTG noise measurements require the comparison of long-term sound data recorded before and after a WTG installation. These measurements must be conducted during several months for various wind speeds and environmental conditions.

The acoustic measurements conducted for a working WTG are not reliable due to the contamination of the measurements by sources other than the noise from the wind turbines[1]. Such sources of noise include traffic (highway, rail and air), construction, industrial facilities, wind in the trees, social activities, animals, birds , etc.

The goal of our paper is to provide suggestions on how the use of a microphone array could improve the WTG noise assessment by two ways: (1) identifying and attributing noise contribution to specific sources  (2) by emphasizing of acoustic signal from the WTG.

As an example of the microphone array, we consider the sensors developed at Stevens Institute of Technology [2], [3] for low-flying aircraft and drone detection (see Figures 1a and b), these  arrays have between 5 and 10 microphones.

These sensors use a signal processing algorithm based on the correlation between the signals received by the elements of the array to find direction towards sound sources and beamforming to emphasize the acoustic signal coming from specific directions.

As a result, it is possible to identify sounds not originating from the wind turbine and remove the affected time frames from the averaged measurement of noise levels. The Stevens array directivity (see Figure 1c) shows enhancing of the signal using beamforining.

 

LFADSystem

DARAPicture

ARADirectivityPattern

 

Figure 1: Examples of acoustic arrays capable of direction-finding: a – acoustic system for low flying aircraft detection [2], b –array for unmanned aerial vehicle detection,c – the beam pattern for the latter array shown as relative gain depending on steered direction and frequency.

Previous prolonged deployments have provided examples of noise observation and angular localization from various sources. Figure 3 displays the spectrogram and signal angular output showing a complex situation with passing trains and vehicles.

Figure 2. An example of SRP-PHAT processing shows a complex situation with noise from a cargo train (T) and vehicles (V).

The configuration of the current Stevens system was optimized for low flying aircraft and unmanned aerial vehicle detection and localization. Since the low-frequency noise components from wind turbines are a concern for the WTG assessment, the placement of the micropnes in the arry arrays can be  modified to operate in the appropriate frequency band.

References

[1]       S. Cooper and C. Chan, “Determination of Acoustic Compliance of Wind Farms,” Acoustics, vol. 2, no. 2, pp. 416–450, 2020.

[2]       A. Sedunov, A. Sutin, N. Sedunov, H. Salloum, A. Yakubovskiy, and D. Masters, “Passive acoustic system for tracking low-flying aircraft,” IET Radar, Sonar Navig., vol. 10, no. 9, pp. 1561–1568, 2016.

[3]       A. Sedunov, D. Haddad, H. Salloum, A. Sutin, N. Sedunov. and A. Yakubovskiy, A., “Stevens drone detection acoustic system and experiments in acoustics UAV tracking.”  In 2019 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1-7). IEEE.

 

1pAA1 – Analysis and actions required to ensure raceway noise levels are acceptable to the surrounding community.

Dr. Bonnie Schnitta, bonnie@soundsense.com
SoundSense, LLC
Wainscott, NY

Popular version of 1pAA1 – Actions and mathematical modeling that will bring noise levels from a racetrack or raceway to a level the community will accept
Presented Monday afternoon May 23, 2022
182nd ASA Meeting
Click here to read the abstract

Historically, new and existing racetracks and raceways, encounter conflict between owners, racecar drivers and the surrounding community. Racecar drivers enjoy the thrill of a raceway, but neighboring residents often complain about the noise negatively impacting the quiet enjoyment of their homes. This is true even when the homes are near a major highway or road. Raceways and neighboring communities are attempting to find workable solutions without compromise to the safety and enjoyment of the raceway. The presentation discusses objective information used to assist communities or town boards, nearby neighbors and track owners engage in productive dialogue of the outcome of the possible solution sets. Multiple solution sets are discussed which are typically acceptable to all parties, including various barriers and other innovative noise mitigation plans. The mathematical modeling and analysis of the topography around the track is presented to show how the local terrain can be used to help to achieve the required level of track noise reduction. The information will be presented through the lenses of three case studies. Two studies demonstrate solutions for specific raceways. The other case study is used to further emphasize the importance of incorporating the local terrain into the solution set.

 

 

 

3pSP4 – Imaging Watermelons

Dr. David Joseph Zartman
Zartman Inc., L.L.C.,
zartman.david@gmail.com
Loveland, Colorado

Popular version of 3pSP4 – Imaging watermelons
Presented Wednesday afternoon, May 25, 2022
182nd ASA Meeting, Denver
Click here to read the abstract

When imaging watermelons, everything can be simplified down to measuring a variable called ripeness, which is a measure of the internal medium of the watermelon, rather than looking for internal reflections from any contents such as seeds. The optimal experimental approach acoustically is thus a through measurement, exciting the wave on one side and measuring the result on the other.

Before investigating the acoustic properties, it is useful to examine watermelons’ ripening properties from a material perspective.  As the fruit develops, it starts off very hard and fibrous with a thick skin. Striking an object like this would be similar to hitting a rock, or possibly a stick given the fibrous nature of the internal contents of the watermelon.

As the watermelon ripens, this solid fiber starts to contain more and more liquid, which also sweetens over time. This process continues and transforms the fruit from something too fibrous and bitter to something juicy and sweet. Most people have their own preference for exactly how crunchy versus sweet they personally prefer. The skin also thins throughout this process. As the fibers continue to be broken down beyond optimal ripeness, the fruit becomes mostly fluid, possibly overly sweet, and with a very thin skin.  Striking the fruit at this stage would be similar to hitting some sort of water balloon. While the sweet juice sounds like a positive, the overall texture at the stage is usually not considered desirable.

In review, as watermelons ripen, they transform from something extremely solid to something more resembling a liquid filled water balloon. These are the under-ripe and over-ripe conditions; thus, the personal ideal exists somewhere between the two. Some choose to focus on the crunchy earlier stage at the cost of some of the sweetness, possibly also preferable to anyone struggling with blood sugar issues, in contrast to those preferring to maximize the sweet juicy nature of the later stages at the cost of crunchy texture.

The common form of acoustic measurement in this situation is to simply strike the surface of the watermelon with a finger knuckle and listen to the sound. More accuracy is possible by feeling with fingertips on the opposite side of the watermelon when it is struck. Both young and old fruit do not have much response, one being too hard, getting an immediate sharp response and being more painful on the impacting finger. The other is more liquid and thus is more difficult to sonically excite. A young watermelon may make a sound described as a hard ‘tink’, while an old one could be described more as a soft ‘phlub’. In between, it is possible to feel the fibers in the liquid vibrating for a period of time, creating a sound more like a ‘toong’. A shorter resonance, ‘tong’, indicates younger fruit, while more difficulty getting a sound through, ‘tung’, indicates older.

An optimal watermelon can thus be chosen by feeling or hearing the resonant properties of the fruit when it is struck and choosing to preference.

2aPAa – Three-dimensional wavefront modeling of secondary sonic booms

Dr. Joe Salamone – joe.salamone@boom.aero

Boom Supersonic
12876 East Adam Circle
Centennial, CO 80112

Popular version of 2aPAan- Three-dimensional wavefront modeling of secondary sonic booms
Presented Tuesday morning, May 24, 2022
182nd ASA Meeting, Denver, Colorado
Click here to read the abstract

A sonic boom is the impulsive sound heard resulting from a vehicle flying faster than the speed of sound.  The origin of this impulsive sound is the localized shock structure close to the vehicle due to regions of compression and expansion of the air (Figure 1) which manifest as pressure disturbances.  The leading shock at the vehicle typically forms a cone that circumferentially spreads around its nose.  A commonly used formula that relates the interior cone angle to the supersonic vehicle’s Mach number is:  cone angle = asin(1/Mach).  Thus, the cone angle gets larger with decreasing supersonic Mach number and vice versa.

 

The sonic boom propagates along acoustic ray paths, and these paths can refract based on temperature gradients and wind speed gradients.  A fundamental premise is the ray path will always bend towards the slower speed of sound.  The initial ray direction is normal to the Mach cone, with some additional influence for its initial direction due to the presence of wind at the vehicle’s flight altitude.  A depiction of the Mach cone compared to the ray cone was presented by Plotkin (2008) shown in Figure 2.  The Mach cone exists at an instance in time, travelling with the supersonic vehicle, while the specific locations that comprise the Mach cone surface represent the pressure disturbances that propagate along ray paths.

Figure 2 – Notional comparison between the supersonic Mach cone and its corresponding ray cone]

Work presented here examines the shape of the Mach cone when propagated significantly large distances away from the vehicle in three-dimensional, realistic atmospheric conditions.  Also recognize the work here only depicts where the sonic boom could travel and not what its amplitude could be at the Earth’s surface.  Figure 3 shows that as the vehicle travels it is constantly generating new portions of the Mach cone, while the existing portions of the Mach cone all propagate at the local (effective) speed of sound.

Figure 3 – Mach cone construction from ray paths that originate from vehicle positions along its trajectory]

A computational example of an extended Mach cone is shown in Figure 4 where the vehicle is flying at Mach 1.15.  Note the atmospheric refraction of the ray paths result in the lower portions of the Mach cone not reaching the Earth’s surface.  And likewise, the upper portions of Mach cone warp back towards the Earth’s surface.  Thus, the Mach cone no longer resembles a cone but is a more complicated shape.

Figure 4 – Computational example of a Mach cone for a vehicle traveling at Mach 1.15]

Another computational example is presented in Figure 5, where the vehicle is flying at Mach 1.7.  Note the increase in Mach number creates a shallower initial Mach cone and portions of the Mach cone reach the Earth’s surface.  Additionally, the outer fringes of the Mach cone above and below the vehicle that do reach the Earth’s surface result in primary, direct secondary and indirect secondary sonic booms as indicated in Figure 5.  However, some portions of the Mach cone centered above and below the vehicle eventually refract at an extremely high altitude in the thermosphere.  Thus, those portions of the Mach cone, when they reach the Earth’s surface, would be inaudible due to their significantly longer propagation distances.

Figure 5 – Computational example of a Mach cone for a vehicle traveling at Mach 1.7]

3aPPb1 – Spectral Processing Deficits Appear to Underlie Developmental Language Disorders

Susan Nittrouer, snittrouer@phhp.ufl.edu
Joanna H. Lowenstein
Kayla Tellez
Priscilla O’Hara
Donal G. Sinex

Popular version of 3aPPb1 Spectral processing deficits appear to underlie developmental language disorders
Presented Wednesday morning, May 25, 2022
182nd ASA Meeting
Click here to read the abstract

The Problem

Sophisticated oral and written language skills are essential to academic and occupational success in our modern, technically based society. Unfortunately, as many as twenty percent of children encounter difficulties learning language, a condition termed Developmental Language Disorder (DLD). This work was undertaken to try to uncover the root of these problems.

Brief Background

For 50 years, scientists have hypothesized that auditory problems are at the root of the challenges encountered by children with DLD. The idea is that children with DLD simply cannot recognize the acoustic structure in speech signals that underlies linguistic forms. Work in this area, however, has been fraught with controversy, and at present, no agreed-upon explanation exists.

What we did

We believe that children with DLD likely have problems processing the acoustic speech signal. In our work we changed three components of our approach from earlier work.

  1. Auditory problems are likely worst at young ages and disrupt language learning at the initial stages. The auditory problems may eventually resolve, but children may be left with language deficits. We looked across ages 7-10 years for evidence of auditory problems that might be more severe in younger than older
  2. The critical auditory problems may involve spectral (frequency), rather than temporal structure, as commonly manipulated. The spectral structure of speech signals is most responsible for defining linguistic We tested children on their ability to detect both temporal and spectral structure.

Watch video here.

  1. Word-internal elements, known as phonological units (or simply phonemes), may be disproportionately affected by auditory problems, rather than vocabulary or syntactic We tested all three kinds of skills: vocabulary, syntax, and phonology, with a focus on phonological skills. We expected to find the strongest effects of auditory problems on those phonological skills.

What we found

  1. Younger children with DLD showed more severe auditory problems than older children with
  2. Problems detecting spectral structure were more severe for children with DLD and lasted longer across age than problems detecting temporal
  3. Problems with spectral structure were most strongly related to children’s awareness of phonological units, rather than lexical or syntactic

Significance

These findings should serve to refocus research efforts on different kinds of acoustic structure than those examined previously, as well as on specific language deficits. DLD puts children at serious risk for problems in school that can masquerade as other disorders, such as attention deficit or reading problems. Underlying conditions – including premature birth and frequent ear infections in infancy – can cause the kinds of auditory problems identified in the work reported here, and unfortunately, children living in poverty face healthcare inequities that put them at risk for those medical problems. This work is one more step in efforts to achieve equity in educational outcomes.