Draft Denver LLP

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 the paper 4pNS2
Presented in the afternoon of May 26, 2022
182nd ASA Meeting in Denver, Colorado

 

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

Popular version of paper  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 paper 1pAA1
Presented Monday afternoon May 23, 2022
182nd ASA Meeting

 

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 paper 3pSP4
Presented Wednesday afternoon, May 25, 2022
182nd ASA Meeting, Denver

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.