3aNS4 – Protecting Sleep from Noise in the Built Environment  – Jo M. Solet

Protecting Sleep from Noise in the Built Environment 

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
180th 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.

 

 

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2aSC1 – Testing invisible Participants: Conducting Behavioural Science online during the Pandemic – Prof Jennifer Rodd

Testing invisible Participants: Conducting Behavioural Science online during the Pandemic

Prof Jennifer Rodd
Department of Experimental Psychology, University College London
j.rodd@ucl.ac.uk           @jennirodd

In early 2020 many researchers across the world had to close up their labs and head home to help prevent further spread of coronavirus.

If this pandemic had arrived a few years earlier, these restrictions on testing human volunteers in person would have resulted in a near-complete shutdown of behavioural research. Fortunately, the last 10 years have seen rapid advances in the software needed to conduct behavioural research online (e.g., Gorilla, jsPsych) and researchers now have access to well regulated pools of paid participants (e.g., Prolific). This allowed the many researchers who had already switched to online data collection could to continue to collect data throughout the pandemic. In addition, many lab-based researchers, who may have been sceptical about online data collection made the switch to online experiments over the last year. Jo Evershed (Founder CEO of Gorilla Experiment Builder) reports that the number of participants who completed a task online using Gorilla nearly tripled between the first quarter of 2020 and the same time period in 2021.

But this rapid shift to online research is not without problems. Many researchers have well-founded concerns about the lack of experimental control that arises when we cannot directly observe our participants.

Based on 8 years of running behavioural research online, I encourage researchers to embrace online research, but argue that we must carefully adapt our research protocols to maintain high data quality.

I present a general framework for conducting online research. This requires researcher to explicitly specify how moving data collection online might negatively impact their data and undermine their theoretical conclusions.

 

  • Where are participants doing the experiment? Somewhere noisy or distracting? Will this make data noisy or introduce systematic bias?

  • What equipment are participants using? Slow internet connection? Small screen? Headphones or speakers? How might this impact results?

  • Are participants who they say they are? Why might they lie about their age or language background? Does this matter?

  • Can participants cheat on your task? By writing things down as they go, or looking up information on the internet?

I encourage researchers to take a ‘worst case’ approach and assume that some of the data they collect will inevitably be of poor quality. The onus is on us to carefully build in experiment-specific safeguards to ensure that poor quality data can be reliably identified and excluded from our analyses. Sometimes this can be achieved by pre-specifying specific performance criteria on existing tasks, but often it included creating new tasks to provide critical information about our participants and their behaviour. These additional steps must be take prior to data collection, and can be time-consuming, but are vital to maintain the credibility of data obtained using online methods.

 

1aMU2 – Measurements and Analysis of Acoustic Guitars During Various Stages of Their Construction – Mark Rau

Measurements and Analysis of Acoustic Guitars During Various Stages of Their Construction

Mark Rau – mrau@ccrma.stanford.edu
Center for Computer Research in Music and Acoustics (CCRMA), Stanford University
660 Lomita Court
Stanford, California 94305, USA

Popular version of paper ‘1aMU2’ Measurements and Analysis of Acoustic Guitars During Various Stages of Their Construction
Presented Tuesday morning 9:50 – 10:05am, June 08, 2021
180th ASA Meeting, Acoustics in Focus

Stringed instruments have an internal structure which determines how they vibrate and produce sound when driven by the strings. This internal structure is made up of multiple vibrational resonances and is referred to as the resonant structure. Many stringed instrument builders (luthiers) will take acoustic measurements of instruments as they are being built to probe the resonant structure and make changes so that the instrument will sound as intended. However, the resonant structure of the instrument continuously evolves throughout the construction process, so it is unclear at which stage the acoustic measurements should be made.

To address this, we measured the resonant structure of three guitars during their construction. Two guitars are of the Orchestra Model (OM) style and were made by the Santa Cruz Guitar Company. The third is an 000-28 style guitar built by the author. The guitars were measured at multiple stages while being constructed, including: during the bracing of the top, construction of the box, sanding, application of polish, and once fully constructed. The stages of construction of the 000-28 are shown in Figure 2.

Figure 1: The three guitars in their completed state. The left and center guitars are the OMs and the right guitar is the 000-28.

Figure 2: Various stages of the 000-28 construction.

The resonant structure was measured by using a small hammer to impart a force to the instrument, and a laser Doppler vibrometer to measure the resulting vibrations. This provided the frequency and amplitude of each structural resonance as well as how long it would ring once struck.

Figure 3: Vibration measurement setup.

The lowest resonances are the most important, because they fall near the fundamental frequencies of most notes on the guitar, so we tracked how the first three prominent resonances changed. Figure 4 shows the frequency response of the 000-28 with the box constructed and sanded (top right of Fig. 2) and the guitar fully constructed (bottom right of Fig. 2). The lowest three prominent resonances are circled and their structural mode shapes are shown for the guitar box.

Figure 4: Frequency response of the 000-28 box (left) and completed guitar (right). The lowest three prominent resonances are highlighted.

We observed some general trends as the guitar evolves, such as the resonant frequencies and amplitudes decreasing as the guitar nears completion, particularly as the polish is applied. If one is trying to achieve a specific sonic quality from an instrument, we recommend taking measurements before the final sanding and adjusting the amount of sanding based on these observations. Final alterations can be made by carving the braces through the sound hole.

2aAO5 – Tracking natural hydrocarbons gas flow over the course of a year – Alexandra M Padilla

Tracking natural hydrocarbons gas flow over the course of a year

Alexandra M Padilla – apadilla@ccom.unh.edu
Thomas C Weber – weber@ccom.unh.edu
University of New Hampshire
24 Colovos Road
Durham, NH, 03824

Frank Kinnaman – frank_kinnaman@ucsb.edu
David L Valentine – valentine@ucsb.edu
University of California – Santa Barbara
Webb Hall
Santa Barbara, CA, 93106

Popular version of paper 2aAO5

Presented Wednesday morning, June 9, 2021

180th ASA Meeting, Acoustics in Focus

Researchers have been studying the release of methane, a greenhouse gas, in the form of bubbles from different regions of the ocean’s seafloor for decades to understand its impact on global climate change and ocean acidification (Kessler, 2014). One region, the Coal Oil Point (COP) seep field, is a well-studied natural hydrocarbon (e.g., oil droplets and methane gas bubbles) seep site, known for its prolific hydrocarbon activity (Figure 1; Hornafius et al., 1999). Researchers that have studied the COP seep field have observed both spatial and temporal changes in the gas flow in the area, that has been thought to be linked to external processes such as tides (Boles et al., 2001) and offshore oil production from oil rigs within the seep field (Quigley et al., 1999).

In recent years, an oil platform within the COP seep field, known as Platform Holly, has become inactive and decommissioned, and there has been a resurgence in natural hydrocarbon seepage activity in the vicinity of the platform based on anecdotal observations. This led a group  from UNH and UCSB to map the hydrocarbon activity in the COP seep field (Padilla et al., 2019), where we were able to identify a large patch of high seepage activity near Platform Holly (Figure 2). The shut-in at Platform Holly provided us with the opportunity to deploy a long-term acoustic monitoring system to study both the spatial and temporal changes in hydrocarbon gas flow in the region and to assess how it is affected by external processes.

We mounted a split-beam echosounder, at a depth of approximately 8 m  below the sea surface, on one of Platform Holly’s cross beams. The echosounder was programmed to emit an acoustic signal every 10 seconds and has been collecting acoustic data since early September 2019, providing us with more than a year’s worth of acoustic data to process and analyze (Figure 3). The acoustic signal emitted by the echosounder interacts with scatterers in the water column, mostly methane gas bubbles in our case, and measures the target strength of these scatterers. The target strength represents how strong a scatterer scatters sound back towards the echosounder (for more information of acoustics and gas bubbles, see article by Weber, 2016).

The acoustic measurements, shown in Figure 3, indicate that there are temporal changes in the location and the target strength of the hydrocarbons in the region; however, it does not tell us how the amount of gas flow of these hydrocarbons is changing with time. Exploiting the split-beam capability of the echosounder, allowed us to track the position of scatterers in the acoustic data, so we can identify and classify different hydrocarbon structure types (Figure 4) and use the appropriate mathematical equations to convert acoustic measurements into gas flow. This will allow us to track changes in gas flow of hydrocarbons near Platform Holly and learn more about how gas flow is affected by external processing, like tides, storms, and earthquakes.

Figure 1. Video of methane gas bubbles rising through the ocean’s water column within the COP seep field.

 

Figure 2. a) Acoustic map of natural hydrocarbon activity within the COP seep field (Padilla et al., 2019). b) Zoomed in acoustic map near Platform Holly. c) Image of Platform Holly.

 

Figure 3. Acoustic observations of hydrocarbon activity (ranges between 10-140 m) west of Platform Holly as a function of range from the echosounder and time. Warm and cool colors represent high and low target strength, which correspond, roughly, to high and low seepage activity, respectively.

 

Figure 4. a) Acoustic observations of hydrocarbon activity. b) Acoustic classification map of different hydrocarbon structure types.

1pAB6 – Oscillatory whistles – the ups and downs of identifying species in passive acoustic recordings – Julie N. Oswald

Oscillatory whistles – the ups and downs of identifying species in passive acoustic recordings

Julie N. Oswald – jno@st-andrews.ac.uk
Sam F. Walmsley – sjfw@st-andrews.ac.uk
Scottish Oceans Institute
School of Biology
University of St Andrews, UK

Caroline Casey – cbcasey@ucsc.edu
Selene Fregosi – selene.fregosi@gmail.com
Brandon Southall – brandon.southall@sea-inc.net
SEA Inc.,
9099 Soquel Drive,
Aptos, CA 95003

Vincent M. Janik – vj@st-andrews.ac.uk
Scottish Oceans Institute
School of Biology
University of St Andrews, UK

Popular version of paper 1pAB6 Oscillatory whistles—The ups and downs of identifying species in passive acoustic recordings

Presented Tuesday afternoon, June 8, 2021

180th ASA Meeting, Acoustics in Focus

 

Many dolphin species communicate using whistles. Because whistles are produced so frequently and travel well under water, they are the focus of a wide range of passive acoustic studies. A challenge inherent to this type of work is that many acoustic recordings do not have associated visual observations and so species in the recordings must be identified based on the sounds that they make.

Acoustic species identification can be challenging for several reasons. First, the frequency contours of dolphin whistles are variable, and each species produces many different whistle types. Also, whistles often exhibit significant overlap in their characteristics between species. Traditionally, acoustic species classifiers use variables measured from all whistles, regardless of what type they are. An assumption of this approach is that there are underlying features in every whistle that provide information about species identity. In human terms, we can tell a human scream or grunt from those of a chimpanzee because they sound different. But is this the case for dolphin whistles? Can a dolphin tell whether a whistle it hears is produced by another species? If so, is species information carried in all whistles?

To investigate these questions, we analyzed whistles produced by short- and long-beaked common dolphins in the Southern California Bight. Our previous work has shown that the whistles of these closely related species overlap significantly in time and frequency characteristics measured from all whistles, so we hypothesized that species information may be carried in the shape of specific whistle contours rather than by general characteristics of all whistles. We used artificial neural networks to organize whistles into categories, or whistle types. Most of the resulting whistle types were produced by both species (we called these shared whistle types), but each species also had distinctive whistle types that only they produced (we called these species-specific whistle types). Almost half of the species-specific whistles produced by short-beaked common dolphins had oscillations in their contours, while oscillations were very rare for both long-beaked common dolphins and shared whistle types. This clear difference between species in the use of one specific whistle shape suggests that whistle type is important for species identification.

We further tested the role of species-specific whistle types in acoustic species identification by creating three different classifiers for the two species – one using all whistles, one using only whistles from shared whistle types and one using only whistles from species-specific whistle types. The classifier that used whistles from species-specific whistle types performed significantly better than the other two classifiers, demonstrating that species-specific whistle types collectively carry more species information than other whistle types, and the assumption that all whistles carry species information is not correct.

The results of this study show that we should re-evaluate our approach to acoustic species identification. Instead of measuring variables from whistles regardless of type, we should focus on identifying species-specific whistle types and creating classifiers based on those whistles alone. This new focus on species-specific whistle types would pave the way for more accurate tools for identifying species in passive acoustic recordings.