Jo M. Solet – Joanne_Solet@HMS.Harvard.edu
Harvard Medical School, Division of Sleep Medicine
Boston, MA    United States

Popular version of paper 3aNS4 Protecting sleep from noise in the built environment
Presented Thursday, June 10, 2021
180^th ASA Meeting, Acoustics in Focus

Recognition is growing over the need to protect patrons from hearing damage caused by high sound levels in stadiums and concert halls. In parallel, attention must be drawn to the health and safety impacts of lower level sound exposures, which contribute to resident sleep loss in built environments.

Those living in aging or poorly built, multiple occupancy buildings are likely to have substantial exposure to site exterior noise intrusions, as well as to noise produced within their own building envelopes. Sleep disruptive noise is very common in crowded, under-resourced neighborhoods; along with limited access to fresh food, poor air quality, and inadequate access to healthcare, disrupted sleep contributes to known health disparities. Older individuals are especially vulnerable, since as we age the parts of the night spent in the deepest sleep, most protected from disruption by noise, continues to decrease. Unfortunately, noise complaints are too often described as “annoyance” without recognition of potential health impacts.

Many localities have ordinances that define day and night sound level maximums, as measured at property lines; these typically apply to noise nuisance produced on one property and experienced on another, excluding noise produced inside a building, experienced between units. In Cambridge MA, noise intrusion enforcement is complaint-driven only. For local government to address the problem, those who are disturbed by noise emanating from an abutting property must first be aware of their rights, then file a complaint and submit evidence and or/attend a public hearing. This requires sophisticated self-advocacy, as well as time free from other responsibilities. Those carrying multiple jobs, doing shift work or having concerns about language skills or residency status, may not act on their rights even when they are aware of them.

It is well known that anticipating needed noise protections before construction is much easier and more cost-effective than retrofitting. Planning and design review for public housing, for example, should include attention to acoustics. Special care must be taken to consider “site exterior noise” such as auto traffic, commuter rail, overhead air flights, air-handling equipment and heat pumps, even local sirens and trash pick-up. Noise generated from “with-in the building envelope” including by elevators, plumbing, footfalls and other resident activities must also be considered in planning design configurations, and in selecting construction materials and finishes.

Insufficient sleep is known to have multiple negative health impacts, including upon cardiovascular health and diabetes risk, as well as impaired antibody production. Supporting the immune system through sufficient sleep has become especially critical during the Covid-19 crisis, both for directly fighting infection and for supporting adequate vaccine response.

By protecting sleep from disruption by noise, acoustics professionals have an important role to play in supporting public health. To address health disparities and other inequities in our society, we must come together, join forces and contribute to problem-solving beyond academic boundaries. I encourage my colleagues to step up and use science to inform policy. As part of the Division of Sleep Medicine at Harvard Medical School, I welcome your partnership and expertise.

Tae Hong Park – thp1@nyu.edu
New York University
New York, NY 10011

Popular version of paper 2aNS3 A socio-technical model for soundmapping community airplane noise
Presented Wednesday morning, June 9, 2020
180th ASA Meeting, Acoustics in Focus

Airports are noisy. Neighborhoods around airports are noisy. Airports around the world typically rely on theoretical noise models to approximate noise levels around airports. While the models render reasonable noise conditions, when closely looking at geospatial data, a different picture emerges. In Chicago, for example, airplane noise complaints have increased from approximately 15,000 per year in 2009 to 5,500,000 per year in 2017. In urban centers like New York, 10% of persons looking to rent or buy a home in Queens will hear near-constant roar of low-flying planes at their property; and in Flushing, 66% of listings are in “airport noise zones” as of June 2019.

While numbers tell a certain kind of story, they sometimes poorly capture human experiences. In the case of aerial sonic pain, it is perhaps even more difficult to relate to as noise is invisible, odorless, and shapeless. And unless one lives in such a neighborhood, how would one really know? And this is exactly what we were thinking which led us to visit neighborhoods around major airports in Chicago and New York with an open mind (ear?). The experience was shocking (wrote a piece for 140 speakers shortly thereafter!).

Since that first visit, we have been “putting the metal to the pedal” in accelerating the development of a socio-technical sound sensor network called Citygram that would make practicable measuring actual noise levels opposed to theoretical noise levels around airports. The project, launched 10 years ago, has also recently developed into a startup called NOISY to empower communities to track airplane noise around their homes.

Citygram Globe Interface showing sound level bars

Citygram heatmap interface

NOISY is essentially a low-cost, automatic aircraft noise tracking system using state-of-the-art AI and a smart sound sensor network. The NOISY sensor ignores non-airplane sounds such as dog barks, honking sounds, and loud music while identifying airplanes flying near your home, associating it with essential information such time-stamped decibel levels, position, and speed.

One of the key elements, apart from the technological advancements is that the sensors do not archive any audio nor is any audio sent to the cloud: only information such as how loud (decibels) and aircraft probability (0%-100%) is extracted from the audio, thus minimizing privacy concerns.

Theoretical noise models, are just that – models. And not knowing the actual aircraft noise levels that communities experience is problematic, especially in the context of developing meaningful mitigation efforts. That is, “you can’t fix what you can’t measure” and that is precisely what we are aiming to contribute to – a quieter future informed by measured data.

For more information on:
Citygram please visit: https://citygramsound.com
NOISY please visit: https://www.getnoisy.io

Mylan Cook – mylan.cook@gmail.com
Eric Todd – eric.w.todd@gmail.com
Kent L. Gee – kentgee@byu.edu
Mark K. Transtrum – mkt24@byu.edu
Brigham Young University
N283 ESC
Provo, UT 84602

David S. Woolworth – dwoolworth@rwaconsultants.net
Roland, Woolworth & Associates, LLC
356 CR 102
Oxford, MS 38655

Popular version of paper 2aNSb7 Detecting instances of focused crowd involvement at recreational events
Presented Tuesday morning, December 03, 2019
178th ASA Meeting, San Diego, CA
Read the article in Proceedings of Meetings on Acoustics

Audio processing is often used to deal with a single person’s voice, but how do things change when dealing with an entire crowd? While it is relatively easy for a person to judge whether a crowd is booing or cheering, teaching a computer to differentiate between different crowd responses is a challenging problem. Of particular interest herein is the challenge of determining when a crowd is making a concentrated, unified, or focused effort. This research has applications in rewarding crowds, sales, and riot prevention.

Previous work has gone into studying crowds at basketball games using Machine Learning techniques such as K-means clustering. Using spectral sound levels—the loudness at different frequencies—K-means automatically divides our sound samples into different groups, separating levels of crowd noise from levels of band noise or PA system noise.

Figure 1 shows the representative frequency-dependent spectral sound levels for these different groupings.  Using other features common to audio signal processing allows us to automatically divide signals into other sub-groups, one of which appears to correspond to the aforementioned focused crowd effort.

Video 1 presents a graphical representation of some of these features, and how they fluctuate with time; the colors in the video show the different sub-groups found, and by examination, the purple sub-group is found to consist primarily of audio that demonstrate the most focused crowd effort.

The purpose of this investigation is to determine if a similar process can be followed to find focused crowd efforts in another type of crowd, namely that at a Mardi Gras parade, as recorded from a microphone mounted on a float. There are some challenges here, arising from differences in frequency between crowds and because the audio from the Mardi Gras crowd shows very little variation—essentially the crowd is cheering the entire time, and so changes in crowd behavior get buried in the crowd’s clamorous cacophony.

There is still something we can do, however. Within the high-involvement basketball data sub-group we find two audio features—flux, which is the change in energy over time, and slope, which marks how quickly the energy increases as frequency increases—with very large numerical values. By setting a threshold for these values, we can mark all the Mardi Gras data that exceeds these values as likely to exhibit focused crowd involvement.

Video 2 presents a graphical representation of the Mardi Gras parade crowd noise, where audio segments which surpass the threshold and so are likely to contain a concentrated crowd effort are shown in green, and all other segments are shown in red. While validation is ongoing, these results show promise for being able to automatically identify focused crowd involvement in different types of crowds.

Nikolaos Kournoutos – nk1y17@soton.ac.uk
Jordan Cheer – J.Cheer@soton.ac.uk
Institute of Sound and Vibration Research,
University of Southampton
University Rd
Southampton, UK SO17 1BJ

Popular version of paper 3pNS3 “Design and realisation of a directional electric vehicle warning sound system“
Presented 1:45pm, December 4, 2019
178th ASA Meeting, San Diego, CA
Read the article in Proceedings of Meetings on Acoustics

Electric cars are rather quiet when compared to their internal combustion counterparts, and this has sparked concern regarding the hazards this might impose on pedestrians and other vulnerable road users. As a result, regulations are coming into effect necessitating the adoption of artificial warning sounds by all electric cars. At the same time however, there have been numerous critics of this decision, citing the resulting increase in noise pollution it might bring about, along with all of its negative side effects.

Researchers have developed systems that are capable of focusing the emitted warning sounds at specific directions, in order to avoid any unnecessary emissions to the surrounding environments. Loudspeaker array based systems have been successful in that regard, managing to target individual pedestrians with the emitted warning sounds. However, the high manufacturing and maintenance costs have kept such solutions from being widely adopted.

In this project, we suggest a directional sound system, which instead of loudspeakers, utilizes an array of structural actuators. These actuators are capable of transmitting vibrations to the structure upon which they are attached, and cause it to radiate sound – effectively using the structure itself as a loudspeaker cone. Like with loudspeakers, one can control the phase and amplitude of each actuator in the array, so that the resulting vibration of the structure radiates sound towards a desired direction.

The first validation of the proposed system was performed using an actuator array attached on a simple rectangular panel. Measurements taken in an anechoic chamber indicate that the structural actuator array is indeed capable of directional sound radiation, within a frequency range defined by the physical characteristics of the vibrating structure.

Picture 1: The prototype used for evaluation consisted of a rectangular aluminum panel, and an array of six actuators attached to it.

The next step was to test how the system performs when implemented in an actual car. The geometry and different materials used in the components of a car mean that the performance of the system greatly depends on where the array is placed. We found that for the warning sounds we used in our tests, the best position for our array was the front bumper, which ensured good forward directivity, and reasonable sound beam steering capabilities.

Picture 2: The actuator array attached to a car for testing in a semi-anechoic environment.

Picture 3: Examples of the directivity achieved for different steering settings, when the actuator array is attached to the front bumper of the car. MATLAB Handle Graphics

Overall, results of our research show that a system based on structural actuators can generate controllable directional sound fields. More importantly, such a solution would be easier to implement on cars as it is more durable and requires no modifications. Wide adoption of such a system could ensure that electric cars can safely project an auditory warning without causing unnecessary noise pollution to the environment.