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Politics of sensing and listening

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Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
1
Politics of sensing and listening
Dietmar Offenhuber
Northeastern University, Boston, USA
d.offenhuber@northeastern.edu
Sam Auinger
Independent Sound artist
samauinger@me.com
Dietmar Offenhuber is Associate Professor at Northeastern University of Art + Design and Public
Policy. He holds a PhD in Urban Planning from MIT, Master degrees from the MIT Media Lab and
TU Vienna. His research focuses on the relationship between design, technology, and governance.
Dietmar is the author of the award-winning monograph “Waste is Information” (MIT Press), works as
an advisor to the United Nations and published books on the subjects of Urban Data, Accountability
Technologies and Urban Informatics.
Sam Auinger is a sonic thinker, composer, and sound-artist based in Berlin, Germany. With his
collaborator Bruce Odland he investigates the theme of "hearing perspective" through large-scale
public sound installations. Auinger works with city planners and architects and speaks at international
symposiums on urbanism, architecture, media, and the senses. He was visiting professor at the
University of the Arts in Berlin, associate at the Harvard Graduate School of Design and lecturer at
the Art, Culture and Technology program at MIT.
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
2
Politics of sensing and listening
Abstract
Noise sensing is often considered as a low-hanging fruit of smart city applications: measuring loudness
is computationally cheap, transmitting the results requires low bandwidths and the minimum
hardware specification are relatively modest. Noise sensing is considered a solved problem; a black
boxed solution just waiting to be deployed.
In this paper, we contest the view of sound sensing as a trivial problem by focusing on the
manifold public controversies around the practices of measurement, their metrics and protocols,
which frequently turn into embattled proxy sites for these controversies underlying issues. In this
paper, we discuss six sites of such controversies that coalesce around distributed sound sensing with
regards to what is measured, how, and why it is measured. These areas include disputes around the
authority of sensing, the politics of the dBA metric and noise thresholds, the politics of display that
arise when noise measurements are made public, issues of surveillance and accountability, the issue of
the soundscape as a commons, and ethical questions connected to interventions in the soundscape.
Keywords: soundscape, environmental noise, smart cities, participatory sensing,
politics of measurement
Introduction
Sound sensing is one of the low-hanging fruits of smart city applications. Technically, it is relatively
easy to accomplish: measuring loudness is computationally cheap, transmitting the results requires low
bandwidths and the minimum hardware specification are relatively modest. Noise sensing is
considered a solved problem by most urban engineers; a black boxed solution just waiting to be
deployed.
Sound sensing is also pertinent from an application perspective. Noise is a common urban
nuisance with consequences for public health and well-being. As a central dimension of the urban
experience, the sound reflects all kinds of human activities, cultural or economic.
1
At the same time,
there is an urgency to explore new tools and methods. Within a predominantly visually oriented
culture of urbanism, the soundscape is still a neglected topic. It is poorly understood by designers and
policymakers who lack the necessary skills and tools to adequately consider the phenomenon in their
work.
Contrary to these seemingly simple premises, we argue that from a social perspective, sound
sensing is far from being a trivial problem. Since the soundscape is fundamentally entangled with
human activity, its measurement is shaped by political interests. The circumstances of measurement
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
3
often become the site of public controversies. The soundscape does not stop at property lines; creating
“zones of interference” with considerable potential for conflict fueled by a multiplicity of diverging
perspectives and stakeholder interests.
2
Despite this multiplicity of perspectives, the metrics defined in most regulations and consequently
used in most devices are highly reductive. The unifying umbrella of the dBA metric used in most smart
city projects masks conflicting auditory perceptions that erupt in controversies around airport
extension projects, traffic conditions, restaurant outdoor seating spaces, weekly markets, cultural
customs, or the use of leaf blowers and ventilation units.
As a result, protocols of measurement frequently turn into an embattled proxy site of public
controversies. In this paper, we discuss six sites of such controversies that coalesce around distributed
sound sensing with regards to what is measured, how, and why it is measured. First, we will provide an
overview of the whys and hows of soundscape measurement in the smart city context, followed by a
discussion of the potential political conflicts connected to each of these dimensions. We will conclude
comprehensive vision for planning cities and neighborhoods with consideration of the soundscape.
The whys, whats, and hows of sound mapping
Approaches to measurement and categorization are never just technicalities, they embody a perspective
on the world. They give shape to different assumptions about what the problem is, how it should be
measured, and what consequences might arise as a result of the measurement.
3
Prefacing the
subsequent discussion, we provide a brief overview of sound mapping strategies with regards to their
underlying whys, whats, and hows.
With respect to the whys, the motivations, we distinguish two groups. The first and largest group
employs sound mapping for the purpose of environmental noise assessment. Typically, this involves
audio sensors that collect sound pressure values without recording audio or analyzing more complex
aspects of the soundscape. The second group uses the soundscape as a source of information; for
sensing auditory signatures, rhythms, and events. Applications in this group may be focused on
capturing the soundscape itself but are often also motivated by non-auditory phenomena that can be
registered through sound. These can include the detection of events, the presence of particular
animals, or the detection and localization of gunshots
4
or aggressive speech.
5
Apart from this broad distinction, the conditions and the context of sensing are relevant. In this
respect, we can differentiate sensing applications along three dimensions:
1. The first dimension concerns the question who is conducting the measurement - a public
authority or a self-selected group of volunteers. Traditionally, noise measurements are
conducted by public authorities under strictly controlled conditions, usually limited to a small
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
4
number of locations. This practice is challenged by initiatives that take advantage of the
sensing capabilities of ubiquitous technologies such as smartphones to address this limitation
and cover areas not covered by official sensors.
2. The second aspect differentiates whether quantitative or qualitative aspects of the soundscape
are the focus of the sound mapping effort. Traditional measures are sparse and quantitative,
but with the rise in cheap computing power, increasingly complex qualitative characteristics
move into the focus of attention.
3. The last dimension finally distinguishes whether sound is directly measured by microphones
or indirectly inferred from the subjective perceptions of citizens expressed in surveys, on social
media, or in citizen complaint data
6
.
Table 1 Overview/Summary
Goals
Noise abatement
Implications of identifying affected areas (property
values)
Sound as a source of
information
Sonic Commons, Surveillance, and Control
Methods
Authoritative or participatory
Who is allowed to measure, who can be trusted?
Public authorities vs. Activists
Accountability and Transparency
Quantitative or qualitative
What is the appropriate protocol of measurement?
Different outcomes support the interests of different
parties.
Direct or indirect
How should subjective perceptions be compared?
What are their implicit biases?
Sound mapping - six areas of contention
Each of the modalities described brings its own challenges and controversies; some examples are listed
in Table 1. In the following section, we will discuss the political implications of these approaches,
organized in six areas. The non-exhaustive list includes the following areas of existing and potential
conflicts connected to sound-mapping and sensing:
1. The authority of sensing
2. The politics of metrics and thresholds
3. The politics of display
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
5
4. Surveillance and accountability
5. Sonic commons and the right to emit
6. Interventions in the soundscape
In each of these areas, concerns about health, aesthetic norms, and social perceptions are deeply
intertwined and impossible to separate. In analogy to the role of sanitation in public policy during the
19th century, which commingled fear of contagious diseases, the aesthetics of urban space, and the
stigmatization of social groups, a similar concept of an “aural hygiene” encompasses concerns for
health and well-being with ideas of normative social orders.
7
The authority of sensing
Current EU regulation requires states to produce maps of noise exposure every five years.
8
Despite the
considerable level of detail represented, however, the creation of most of these maps involves very little
measurement. Most cities maintain only a small number of noise sensors, from which noise maps are
derived using complex simulation models. Such models have been developed since the 1950s and have
reached a considerable level of sophistication - considering architecture, topography, and frequency
distributions.
9
Citizen initiatives have challenged such official models as incomplete and questioned the siting
decisions of official sensing nodes. Professional calibrated sensing equipment, however, remains
unaffordable for most private citizens. Using smartphones or low-quality sensors as a substitute,
participatory sensing initiatives aim to provide a more fine-grained picture and capture the soundscape
in the sites of their daily life.
10
These initiatives have sparked controversies around the authority over
collecting noise data. While the initiatives argue for a democratization of data collection, they receive
pushback regarding the accuracy of the uncalibrated equipment and doubts that volunteers may only
report data that supports their concerns. Nevertheless, studies find participatory sensing to be
sufficiently accurate if done correctly.
11
The politics of metrics and thresholds
The quantification of noise emissions follows a number of conventions informed by findings from
physics, acoustics, human medicine and psychology. The metric had to be both “objective” and easily
implementable. The commonly used A-weighted dB measure considers that the loudness of a signal
registered by the human ear depends on its frequency and volume, following the shape of the Fletcher-
Munson curve developed at the Bell Laboratories.
12
Since the ear is less sensitive to very low or very
high frequencies, these frequencies are penalized in the model. The A-weighted loudness and its
various derived time-dependent metrics
13
are useful to describe the average noise exposure in a directly
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
6
exposed environment and provide actionable evidence for noise reduction measures. It also addresses
the audibility of a place by describing the noise-floor that must be exceeded by a signal to be audible.
A basic limitation of the dBA metric is connected to its original purpose for optimizing speech
recognition in telephony under relatively quiet conditions. In other words, it reduces noise impacts to
what is heard by the ear. However, humans perceive sound not only with their ears - the entire body
hears, is filled with cavities that resonate in their own frequencies. We feel bass in our stomachs and
high-pitched sounds in our craniums. In a noisy urban environment with a large proportion of low-
frequency components, the dBA no longer serves as an adequate metric to estimate the impact of
environmental noise on health and wellbeing besides loudness, the frequency distribution of noise
needs to be taken into account.
14
Low-frequency noise has an especially negative effect not captured by
the A-weighted metric.
15
An experiment with a sensor network in Los Angeles revealed a large amount
of low-frequency noise that goes unnoticed in the standard metrics.
16
NoiseScore, a Boston-based
participatory sensing project involves a Smartphone App that differentiates between different
frequency bands and contextualizes the findings with citizen perceptions captured by community
surveys.
17
Apart from frequencies, thresholds of magnitude, duration, and temporal distribution of
exposure are equally contestable.
Based on a reductive model of noise, noise metrics do not address the annoyance and disruptions
based on sound characteristics other than loudness and frequency. They do not capture location and
situation specific aspects of the soundscape including the rhythms of social, economic and cultural
interrelationships. From a human perspective, noise is a complex phenomenon that is influenced by
physiological and cultural issues; its perception is formatted by personal knowledge and individual
history.
The politics of display
By under-reporting harmful and disruptive low-frequency bands, the characteristics of the dBA metric
can favor groups with an interest in reported noise values to stay below thresholds. This group may not
only include public authorities but ironically also the affected communities. Conducting participatory
sensing experiments with communities located around the Heathrow Airport, artist and researcher
Christian Nold found that communities may be interested in investigating community noise, but are
often reluctant to have the results publicly displayed or publicized.
18
This observation is consistent with other examples from the field of environmental justice,
where the interests of communities affected by pollution may be aligned with a polluter they
economically depend upon. In the case of noise, communities may choose to publicly downplay
exposure to avoid the stigmatization of living in a tainted environment, and may justifiably fear
possible penalties in life insurance policies or negative impacts for their property values.
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
7
In this sense, official noise maps can have the perverse consequence of actually becoming a
driver of gentrification and reinforce spatial inequalities by introducing the “sonic capital” of a place as
an economic factor.
19
The public display of noise data has potentially unintended effects that ought to
be considered in discussions around the publication of sensor data on open data portals. The concept
of data sovereignty may apply here, demanding that affected communities have a say in how data
concerning their group is collected and used.
Surveillance and accountability
Concerns about privacy and surveillance are among the most discussed issues pertaining to smart
cities.
20
projects deploy a wide array of sensors mounted on light poles, in kiosks, park benches, and
other urban infrastructure. Such sensors may, for example, collect Bluetooth and WiFi addresses of
mobile devices, which allows an estimation of pedestrian activity throughout the day, but can also be
used to identify individuals. To address privacy concerns and save bandwidth, many sensors analyze
images and audio onboard without saving or transmitting the footage. However, privacy concerns
arise also from the combination of different sensor readings, for example through matching with
Bluetooth addresses tied to an individual.
21
This is a particular concern in noise sensing applications that go beyond the simple quantification
of noise levels and record or interpret sound samples. Among the burgeoning field of smart policing,
offered system claims to be able to detect aggressive speech patterns to enable law enforcement to
detect violence and aggression.
22
In combination with other existing applications such as face
recognition systems, license plate readers, or Bluetooth sniffers, the claimed anonymity of data can
disappear. Issues of privacy protection, the accountable management of collected data, or the
governmentality of social control through surveillance are intensely discussed.
23
What is less discussed
is the relationship between the perceived breach of privacy of a particular sensory modality and it's
actual surveillance potential. Sound plays a special role in this regard: even applications that only
measure long-term averages of environmental noise are subject to intense public controversy, while
other kinds of environmental sensors or the more potent combination of various data sources do not
receive such scrutiny.
Sonic commons and the right to emit
Sound mapping approaches that go beyond average noise levels and aggressive speech patterns and
instead use machine learning for a detailed analysis of the soundscape and its wide range of qualitative
phenomena are technically possible even if currently not implemented. Such approaches could allow
narrowing the gap between seemingly objective quantitative noise measures and the multifaceted,
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
8
subjective perceptions of the auditory environment. These approaches would allow characterizing the
atmosphere of a place, reflecting the properties that are meaningful or disruptive for inhabitants.
Along the same lines, such techniques would also allow to identify mobile and stationary sources
based on their auditory signatures and quantify their relative contributions to environmental noise
exposure. Knowing how much a motorcycle or a helicopter contributes to noise pollution could
inform a public discourse on the sonic commons, a perspective that understands the soundscape as a
fundamentally limited resource under the custodianship of its occupants. As Bruce Odland and Sam
Auinger explain, the Sonic Commons is any shared acoustic space where many people can hear the
results of each other’s activities and limit the shared auditory space and its communicative possibilities
through their actions. They remind us that “certain things like air, water and humane sonic
environments should be considered basic human rights.”
24
On a more subtle level, the sonic commons also reflects the power relationships in a given society.
Explicitly, this is manifest in signals and soundmarks such as police sirens, the sound of church bells or
prayer calls, and implicitly in the keynote sounds of a place, to use Murray Schaefer’s terminology for
the dominant auditory background texture.
25
While it is thinkable and perhaps desirable to computationally capture and describe the
soundscape in all its nuances and assemble these individual signals into a coherent picture, such an
approach also hold potential dangers. If unsupervised, machine learning algorithms that characterize
the soundscapes of places and atmospheres will likely reproduce the same implicit biases and racial
prejudices that have been observed in sentiment analysis of texts or the classification of search results.
26
Interventions in the soundscape
As a final point, sound mapping cannot be separated from active interventions into the soundscape
which it would likely inform. Sound is already used to shape and control public space and exclude
certain groups or animals. Examples are ultrasonic deterrents for dogs or mice, but also insidious
devices such as the Mosquito, emitting high-pitched sounds to keep teenagers away from public spaces
who can still hear these frequencies.
27
Teenagers, in turn, have appropriated the same principle and use
17kHz ringtones for their phones which their teachers cannot hear.
28
Train stations in Copenhagen
and Hamburg play classical music at high volume on their premises, reportedly based on the
assumption that this would keep away junkies, while keep travelers moving.
29
Beyond such direct and intentional interventions, however, almost every intervention in urban
space changes the soundscape intentionally or unintentionally: every construction project, every group
of trees planted, each choice of facade materials or architectural geometry. Such changes are part of a
feedback loop, they lead not only to direct acoustic consequences but also to adaptations in the social
use of the space. It can be assumed that crude tactics of intervention can be made more effective if they
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
9
are informed by a real-time analysis of the soundscape and the feedback-effects instigated by sonic
interventions.
Conclusion
At the present moment, most official noisemaps are still the result of simulations rather than
measurements, and therefore signal a granularity and objectivity that is not justified by underlying
observations. Participatory and distributed sensing methods by “smart cities” and “smart citizens”
promise ways to overcome this issue. However, as the proliferation of ubiquitous technologies that can
be used for sensing the soundscape generate more data, it should not be expected that this new wealth
of data will settle the underlying disputes.
To understand these disputes, it is crucial to not only consider external aspects, such as how
technologies are deployed or who has access to them, but also internal aspects concerning the choice of
sensing metrics and communication protocols embedded in these devices. Noise metrics and sensor
characteristics often appear black-boxed in the sense of a technology that has become accepted as
uncontroversial.
30
While the first generation of citizen sensing projects has emphasized the
democratization of measurement while often uncritically accepting the data generated by sensors of
dubious quality. More recent initiatives, however, explicitly focus on questions of data quality and the
appropriate methods of measurement.
Mapping the soundscape requires a broader discussion between engineers and , that not only
includes scientific abstractions of auditory phenomena, but also the rich layers of meaning and tracing
their cultural, personal, economic, and, ultimately, power relationships. “Nothing essential happens in
the absence of noise,” Jaques Attali observes: “Among birds a tool for marking territorial boundaries,
noise is inscribed from the start within the panoply of power. Equivalent to the articulation of a space,
it indicates the limits of a territory and the way to make oneself heard within it, how to survive by
drawing one's sustenance from it. And since noise is the source of power, power has always listened to
it with fascination.”
31
Endnotes
(
1
) Barry Blesser and Linda-Ruth Salter, Spaces Speak, Are You Listening?: Experiencing Aural
Architecture (The MIT Press, 2009).
(
2
) Gascia Ouzounian and Sarah A. Lappin, “Soundspace: A Manifesto,” Architecture and Culture 2,
no. 3 (November 1, 2014): 30516, https://doi.org/10.2752/205078214X14107818390559.
(
3
) Geoffrey C. Bowker and Susan Leigh Star, Sorting Things Out (MIT Press, 1999); Rob Kitchin,
The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (SAGE
Publications Ltd, 2014).
(
4
) See the shotspotter system or outdoor detection https://www.shotspotter.com ; the amberbox system for
indoor detection https://amberbox.com
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
10
(
5
) See the company website https://www.soundintel.com
(
6
) Yu Zheng et al., “Diagnosing New York City’s Noises with Ubiquitous Data,” in Proceedings of
the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM,
2014), 715725.
(
7
) Samuel Llano, The Sacred in Madrid’s Soundscape: Toward an Aural Hygiene, 18561907, 2016,
https://doi.org/10.1057/978-1-137-60020-2_1.
(
8
) See the EU Environmental Noise Directive 2002/49/EC
(
9
) Richard H Bolt, Leo L Beranek, and Robert B Newman, “Handbook of Acoustic Noise Control.
Volume I. Physical Acoustics” (Defense Technical Information Center, December 1952); Enda
Murphy and Eoin King, Environmental Noise Pollution: Noise Mapping, Public Health, and
Policy (Elsevier, 2014); Naveen Garg and Sagar Maji, “A Critical Review of Principal Traffic
Noise Models: Strategies and Implications,” Environmental Impact Assessment Review 46, no.
Supplement C (April 1, 2014): 6881, https://doi.org/10.1016/j.eiar.2014.02.001; Alfredo
Calixto, Fabiano B Diniz, and Paulo H T Zannin, “The Statistical Modeling of Road Traffic
Noise in an Urban Setting,” Cities 20, no. 1 (2003): 2329; Campbell Steele, “A Critical Review
of Some Traffic Noise Prediction Models,” Applied Acoustics 62, no. 3 (March 1, 2001): 27187,
https://doi.org/10.1016/S0003-682X(00)00030-X.
(
10
) Nicolas Maisonneuve et al., “NoiseTube: Measuring and Mapping Noise Pollution with Mobile
Phones,” in Information Technologies in Environmental Engineering (Springer, 2009), 215228;
Erica Walker, “Noise Score,” 2015, http://noisescore.com/.
(
11
) Ellie D’Hondt, Matthias Stevens, and An Jacobs, “Participatory Noise Mapping Works! An
Evaluation of Participatory Sensing as an Alternative to Standard Techniques for Environmental
Monitoring,” Pervasive and Mobile Computing 9, no. 5 (2013): 681694; Enda Murphy and
Eoin A. King, “Testing the Accuracy of Smartphones and Sound Level Meter Applications for
Measuring Environmental Noise,” Applied Acoustics 106 (May 1, 2016): 1622,
https://doi.org/10.1016/j.apacoust.2015.12.012.
(
12
) Harvey Fletcher and Wilden A Munson, “Loudness, Its Definition, Measurement and
Calculation,” Bell System Technical Journal 12, no. 4 (1933): 377430.
(
13
) Including the day-night level (Ldn) and the Day-evening-night level (Lden); the Community Noise
Equivalent Level (CNEL), the Energy equivalent noise level (Leq). For reference, see
https://www.eea.europa.eu/help/glossary/eea-glossary
(
14
) Erica D. Walker et al., “Spatial and Temporal Determinants of A-Weighted and Frequency
Specific Sound LevelsAn Elastic Net Approach,” Environmental Research 159 (November 1,
2017): 49199, https://doi.org/10.1016/j.envres.2017.08.034.
(
15
) H G Leventhall, “Low Frequency Noise and Annoyance,” Noise Health 6, no. 23 (April 2004):
5972; Erica D. Walker et al., “Cardiovascular and Stress Responses to Short-Term Noise
ExposuresA Panel Study in Healthy Males,” Environmental Research 150 (October 1, 2016):
39197, https://doi.org/10.1016/j.envres.2016.06.016; Christos Baliatsas et al., “Health Effects
from Low-Frequency Noise and Infrasound in the General Population: Is It Time to Listen? A
Systematic Review of Observational Studies,” The Science of the Total Environment 557558
(July 1, 2016): 16369, https://doi.org/10.1016/j.scitotenv.2016.03.065.
(
16
) Dietmar Offenhuber et al., “Los Angeles Noise ArrayPlanning and Design Lessons from a
Noise Sensing Network,” Environment and Planning B: Urban Analytics and City Science,
August 8, 2018, 2399808318792901, https://doi.org/10.1177/2399808318792901.
(
17
) Walker, “Noise Score.”
(
18
) Christian Nold, “Device Studies of Participatory Sensing: Ontological Politics and Design
Interventions” (UCL (University College London), 2017).
(
19
) Yukio King, “Sound Urban Planning: Community Development Durch Klangorientierte
Stadtplanung” (Master’s Thesis, University of the Arts Berlin, 2008).
(
20
) Rob Kitchin, “The Ethics of Smart Cities and Urban Science,” Phil. Trans. R. Soc. A 374, no.
2083 (December 28, 2016): 20160115, https://doi.org/10.1098/rsta.2016.0115; Orit Halpern
et al., “Test-Bed Urbanism,” Public Culture 25, no. 2 70 (2013): 272306; Adam Greenfield and
Nurri Kim, Against the Smart City (The City Is Here for You to Use) (New York, NY: Do
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
11
projects, 2013); Alberto Vanolo, “Smartmentality: The Smart City as Disciplinary Strategy,”
Urban Stud. 51, no. 5 (April 2014): 883898; A Bartoli et al., “Security and Privacy in Your
Smart City,” in Proceedings of the Barcelona Smart Cities Congress, vol. 292, 2011.
(
21
) Saskia Naafs, “‘Living Laboratories’: The Dutch Cities Amassing Data on Oblivious Residents,”
The Guardian, March 2018.
(
22
) W Zajdel et al., “CASSANDRA: Audio-Video Sensor Fusion for Aggression Detection,” in 2007
IEEE Conference on Advanced Video and Signal Based Surveillance, 2007, 200205.
(
23
) Vanolo, “Smartmentality: The Smart City as Disciplinary Strategy”; Greenfield and Kim, Against
the Smart City (The City Is Here for You to Use).
(
24
) Bruce Odland and Sam Auinger, “Reflections on the Sonic Commons,” Leonardo Music Journal
(December 1, 2009): 6368, https://doi.org/10.1162/lmj.2009.19.63.
(
25
) R. Murray Schafer, The Tuning of the World, 1st ed. (Random House Inc (T), 1977).
(
26
) Svetlana Kiritchenko and Saif M Mohammad, “Examining Gender and Race Bias in Two
Hundred Sentiment Analysis Systems,” May 2018.
(
27
) See the controversy around the Mosquito mounted outside a public library in the UK.
https://you.38degrees.org.uk/petitions/remove-mosquito-device-audio-weapon-from-milford-haven-
library
(
28
) See https://metro.co.uk/2006/05/24/pupils-perform-alarming-feat-155361/
(
29
) Mads Schmidt, “We Asked Drug Addicts to Rate the Music at Copenhagen Central,” Vice,
August 7, 2014, https://www.vice.com/en_us/article/kwppae/copenhagen-central-station-is-
annoying-addicts-with-marching-music.
(
30
) M. Callon and B. Latour, “Unscrewing the Big Leviathan: How Actors Macro-Structure Reality
and How Sociologists Help Them to Do So,” Advances in Social Theory and Methodology:
Toward an Integration of Micro-and Macro-Sociologies, 1981, 277303.
(
31
) Jacques Attali and Susan McClary, Noise: The Political Economy of Music (Theory and History
of Literature, Vol. 16) (Univ Of Minnesota Press, 1985).
Offenhuber, Dietmar, and Sam Auinger. 2019. “Politics of Sensing and Listening.” In Architecture and the Smart
City, edited by Sergio M. Figueiredo, Sukanya Krishnamurthy, and Torsten Schroeder. London: Routledge.
12
References
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Frequency Noise and Infrasound in the General Population: Is It Time to Listen? A
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Thesis
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