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Testing the accuracy of smartphones and sound level meter applications for measuring environmental noise

Testing the accuracy of smartphones and sound level meter applications
for measuring environmental noise
Enda Murphy
, Eoin A. King
School of Architecture, Planning & Environmental Policy, Planning Building, Richview, University College Dublin, Dublin 4, Ireland
Acoustics Program and Lab, Department of Mechanical Engineering, University of Hartford, 200 Bloomfield Avenue, West Hartford, CT 06117, USA
article info
Article history:
Received 19 May 2015
Received in revised form 27 November 2015
Accepted 3 December 2015
Noise measurement apps
Environmental noise
Crowd sourced noise monitoring
This paper reports on experimental tests undertaken to assess the capability of noise monitoring
applications to be utilized as an alternative low cost solution to traditional noise monitoring using a
sound level meter. The methodology consisted of testing 100 smartphones in a reverberation room.
Broadband white noise was utilized to test the ability of smartphones to measure noise at background,
50, 70 and 90 dB(A) and these measurements were compared with true noise levels acquired via a cali-
brated sound level meter. Tests were conducted on phones using the Android and iOS platforms. For each
smartphone, tests were completed separately for leading noise monitoring apps culminating in 1472
tests. The results suggest that apps written for the iOS platform are superior to those running on the
Android platform. They show that one of the apps tested SLA Lite is within ±1 dB of true noise levels
across four different reference conditions. The results also show that there is a significant relationship
between phone age and its ability to measure noise accurately. The research has implications for the
future use of smartphones as low cost monitoring and assessment devices for environmental noise.
Ó2015 Elsevier Ltd. All rights reserved.
1. Introduction and context
Smartphones have become a ‘must have’ for the majority of
adult citizens in world’s developed nations. As of October 2014,
64 per cent of US adults own some form of smartphone [1].To
demonstrate the rapidity with which smartphones have infiltrated
the US market, the corresponding figure for the spring of 2011 was
35 per cent [2]. Internationally, more recent research covering 32
countries estimates that 80 per cent of internet users own a
smartphone. Of those, 54 per cent of phones utilize the Android
operating system, 16 per cent operate the iOS and the remaining
come from alternative operating systems such as Windows among
others [3].
The development of smartphone technology and its impact on
environmental noise studies has only recently begun to receive
some attention in the academic literature. There are some studies
which suggest that smartphones are capable of replacing tradi-
tional noise assessment devices such as sound level meters (SLMs)
in the not too distant future. Kanjo [4] has outlined the possibility
of developing a mobile phone platform for measuring noise in
cities and highlights the potential of such avenues for the future.
Similarly, D’Hondt et al. [5] have demonstrated the possibility of
smartphone-based noise apps to be utilized by ordinary citizens
as a form of crowd sourced participatory noise assessment in cities.
Studies such as these suggest that the future of noise assessment,
whether it is in cities or elsewhere, will likely be tied closely to
developments in smartphone and other forms of innovative mobile
technology that are easily and relatively affordably accessed by
ordinary citizens, especially in developed nations. A key challenge
for noise mapping studies, in particular, is determining the accu-
racy of any smartphone based approach and to shed light on the
margin of error that might be associated with the substitution of
smartphones for sound level meters in future real world settings.
The current paper is concerned with trends in the development
of smartphones and associated applications for the measurement
of environmental noise specifically. There are only a small number
of studies which have investigated issues that are relevant to the
current research. Perhaps the most relevant is a recent study by
Kardous and Shaw [6]. They tested the accuracy of 10 iOS and 4
Android apps for measuring noise in occupational settings on 8
smartphones and one tablet. Their research found that the iOS
noise app SoundMeter, developed by Faber Acoustical has the
best agreement in A-weighted sound levels (0.52) with reference
values while three other apps for the iOS were within ±2 dB(A) of
reference values. This led the authors to conclude that devices
running the iOS, in particular, had significant scope to be used as
0003-682X/Ó2015 Elsevier Ltd. All rights reserved.
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Tel.: +1 8607685953.
Applied Acoustics 106 (2016) 16–22
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assessment devices for occupational settings. What is also interest-
ing is that their research found that devices running the Android
operating system were inadequate for the same purpose because
they were ‘built by several different manufacturers and that there
is a lack of conformity for using similar microphones and other
audio components in their devices’ [6, p.192]. The focus of previous
work by Kardous and Shaw was on examining the accuracy of
smartphone apps rather than the smartphones themselves.
Although they did offer some insights about the relationship
between phone model and measurement accuracy, the sample of
phones they used for testing was somewhat limited in scope
(3 iPhone models and 5 Android devices).
Similarly, the work of Nast et al. [7] tested five apps but only
one phone the iPhone 4S thereby essentially controlling for
the phone model in their analysis of noise measurement applica-
tions. Thus, their work provides no insight into the role of the
smartphone hardware in producing accurate noise measurements
or otherwise. Moreover, their tests did not utilize pink noise
and/or white noise thereby limiting the spectral variability of the
testing conditions to specific octave band analysis. Nevertheless,
their results showed that for all apps tested, the results varied
widely from that measured using a Type 1 SLM. The authors con-
cluded that, with the exception of the Sound Meter App by Faber
Acoustical, ‘SLM apps are best used for entertainment purposes,
as they are not accurate as SLMs...[7, 253–254]. Indeed, their
work pointed to large errors and nonlinearities at high sound
levels, drawing into question the utility of apps for occupational
Within the foregoing context, the current paper builds on previ-
ous work which has sought to analyze the suitability of smart-
phones for use as a substitute for traditional SLMs. Whereas
related studies has tended to place focus on the smartphone apps
themselves, this research focusses not only on testing the leading
apps on two leading platforms iOS and Android but we also test
a much wider range of smartphones than has been tested in similar
studies to date. In this regard, we are seeking to identify statisti-
cally significant differences in the ability of different smartphone
models to measure noise accurately or otherwise using the same
app while also assessing the suitability of the apps themselves
and the platform being utilized to host them. The research also
examines the relationship between smartphone age and measure-
ment accuracy.
2. Methods
A representative sample of the most popular smartphones on
the University of Hartford campus was acquired by asking students
to volunteer their device for testing. In total 100 smartphones were
tested; 65 were on the iOS platform while the other 35 were
Android-based. A list of the phone manufacturers and individual
models tested is presented in Table 1. For each iOS-based phone,
four leading apps were tested while three apps were tested for
each Android phone. This discrepancy was due to one app being
taken down from the Google Play store after a small number of
tests had been completed and because of this it was removed from
the testing agenda. For an app to be included in the testing it had to
satisfy certain criteria. These included: (1) being able to report A-
weighted sound levels; (2) being able to report the sound level
as a numeric value and (3) being either free or cost less than
$5.00. While some apps allow for manual calibration of the in-
built microphone prior to measurement, this was not completed
for our experimental tests in order to simulate a typical real world
situation. This conforms to the approach taken for similar testing
studies [6,7].Table 2 provides a full list of the apps tested for our
study 7 in total for the iOS and Android phones, the developer
and version. All of the apps tested met our selection criteria
and all were commercial apps. No tests were conducted on
Windows-based devices given the dominance of iOS and Android
phones of the smartphone market.
For our experimental set up, we used broadband white noise in
a 125 m
ISO 3741 [8] compliant reverberation room. This source
was generated through Brüel & Kj
r’s Pulse Measurement System,
version 18.1 and was played through a Type 4292-L OmniPower
dodecahedron loudspeaker located in the center of the room. The
output voltage was adjusted in Pulse to produce a uniform sound
field at 50 dB(A), 70 dB(A), and then 90 dB(A). These values were
initially confirmed using both a rotating microphone boom fitted
with a diffuse field microphone as well as a calibrated Brüel & Kj
Type 2250 SLM. Background noise was measured on each test day
and was found to be 27 dB(A) in the reverberation room. Testing
was conducted over 10 separate days. The diffuse sound field gen-
erated in the reverberation room meant that the precise location
and size of the smartphone in the room did not influence the
results of the study in any way. However, during measurements,
phones were handheld at shoulder height by the same two individ-
uals for the entire series of testing
; all phone covers were removed
prior to testing to avoid any interference with the microphone. We
collected a single measurement for each app at each test level (back-
ground, 50, 70 and 90 dB(A)). As an experimental precaution, the
room was tested immediately before and after each testing schedule
to ensure that the room acoustics remained consistent across testing
schedules. The adoption of a handheld approach for testing differs
from previous studies which utilized a tripod [6,7]. The reason for
Table 1
Phones and models tested and their frequency.
Brand Number
iPhone (4, 4s, 5, 5s, 5c, 6, +) 65
Galaxy (Note 2, Note 3, s3, s3 slim, s3 mini, s4, s4 active, s5, 24
Google (Nexus 5) 2
HTC (One, One Mini 2, M8) 4
LG (VS870, g2) 2
Motorola (Droid 2, Droid MAXX, Moto X 2nd gen.) 3
Total 100
Table 2
Smartphone apps selected for testing.
Name Developer
Web Link
Sound Level
Analyzer Lite
(iOS) version 1.3
Toon, LLC
version 1.1
Fabien Lefebvre
Decibel Meter Pro
(iOS) version
Audio (0.99)
UE SPL (iOS) version
Logitech Inc.
Sound Meter
version 1.6
Smart Tools co.
Noise Meter
version 2.1
Decibel Pro
version 1.4.22
BSB Mobile
Solutions Tools
For all tests, there was one individual testing in the reverberation room and one
located outside operating the Pulse system for all tests.
E. Murphy, E.A. King / Applied Acoustics 106 (2016) 16–22 17
this is that we were keen to attempt to simulate how phones would
actually be utilized by the general public in a laboratory setting. Each
phone was tested at background, 50 dB(A), 70 dB(A), and 90 dB(A)
levels. Unlike other studies which tested smartphones for weighted
and unweighted sound levels, we focussed only on the ability of
phones to measure A-weighted sound level measurements given
our interest in the capability of the devices for measuring environ-
mental noise.
When recruiting students with smartphones, they were also
asked a series of questions about their phone prior to testing taking
place. Questions were asked about the precise make and model of
the phone,
the operating system
and the age of the phone in one of
five categories
. This allowed for additional analysis of the test data
with respect to these specific variables.
For data analysis, we performed ANOVA and t-tests to assess
the difference in mean values associated with each platform
(iOS/Android), across apps and phone models. In addition,
descriptive statistics were utilized to determine operating system,
app and phone performance while standard boxplot analysis was
used to assess the variability in measurement scores across apps
and phone models. In order to isolate the impact of certain
variables on measurement outcome, sequential regression analysis
was also undertaken. Sequential regression is utilized to determine
the impact of independent variables on smartphone measurement
differential from reference values and allows the user to enter
variables or sets of variables into the regression equation
after other variables have been controlled for as a separate block.
This allows the researcher to determine if such variables are
contributing significantly to the prediction of the measurement
3. Results
Table 3 shows descriptive statistics of the mean difference
between measured values using smartphones and the pre-
specified reference values. It can be seen that at the 50 and 70 dB
(A) reference conditions the mean difference in app measurement
from reference conditions is 2.09 and 1.33 respectively while at the
other reference conditions the measurement results are more vari-
able. Indeed, the results show that the apps are less efficient at
measuring at background and high noise levels; the applications
over measure the true noise level by 5.33 dB(A) at background
and underestimate it by 3.57 dB(A) at 90 dB(A). However, at noise
levels between background and 90 dB(A) they do an adequate job
of measuring to within an acceptable degree of error which is typ-
ically ±2 dB(A). The fact that the measurement apps do a poorer job
of accurately measuring at high noise levels is a concern given that
environmental noise at higher levels is the key area of concern
from a public health perspective.
To explore the data variability, a scatterplot comparing mea-
sured values with pre-specified reference conditions 27 dB(A)
background, 50 dB(A), 70 dB(A), 90 dB(A) was completed and is
shown in Fig. 1. It demonstrates the extent of variation in mea-
sured versus reference values across the full range of measure-
ments. The high degree of variation between measured and
reference scores suggests that the reliability of smartphones for
measuring environmental noise depends to a significant degree
on having a relatively large number of sample data points rather
than a few isolated measurements.
Turning specifically to an analysis of the relationship between
measurement accuracy and the phone platform being utilized, an
independent samples t-test was performed to examine whether a
significant difference existed between the mean measured values
across the iOS and Android platforms at each of the four reference
The results are presented in Table 4. They demonstrate
a significant difference in mean scores for the two platforms for all
but one reference condition 70 dB(A). With the exception of the
70 dB(A) reference condition, the results show that Android devices
have a mean value which is closer to the true noise level for all other
reference conditions. However, these results come with a caveat
because they also demonstrate that Android devices are associated
with higher standard deviation values relative to the iOS indicating
poorer reliability in terms of measurement consistency.
A further interesting issue to investigate is the relationship
between the phone manufacturer and measured noise values.
Table 5 shows the mean difference from reference values by phone
brand. The results show that the best performing phone brand is
HTC with only a 0.33 difference from the true noise level. Samsung
is the next best followed closely by Apple. Table 5 also shows a
break out of the results for each manufacturer by reference condi-
tion. It shows that at the background reference condition the HTC
phone performs best (the mean difference from reference is
2.06 dB(A)) while at the 50 dB(A) reference condition the Google
phone performs best. The test results also show that at the 70
and 90 dB(A) reference conditions the iPhone (1.52 dB(A)) and
the HTC phone (1.61 dB(A)) respectively perform best. Thus, the
HTC phone performs best at two of the four reference conditions.
The wide variation in results for Android phones is interesting
because it demonstrates that the phone brand being utilized for
measurement has a significant bearing on its ability to measure
noise accurately when the same app is being used. This implies that
considerable variation exists in the quality of the hardware
Table 3
Descriptive statistics showing smartphones testing results by reference condition (dB
Reference (dB(A)) NMean difference
from reference
S.D. S.E. Range
Background (27) 368 5.33 9.64 0.50 48.0
50 368 2.09 6.50 0.34 54.2
70 368 1.33 6.27 0.33 56.5
90 368 3.57 6.99 0.36 51.0
Fig. 1. Scatterplot of reference versus measured noise values using smartphones.
If students did not know the exact model, it was identified prior to testing.
iOS or Android.
These were: <6 months, 6–12 months, 1–1.5 years, 1.5–2 years, 2+ years.
For all t-tests, the data was first test for homoscedasticity using Levene’s test and
the appropriate p-value was taken depending on the result.
18 E. Murphy, E.A. King / Applied Acoustics 106 (2016) 16–22
components among Android phones. More specifically, it points to a
high degree of variation in the quality of MEMS microphone compo-
nents used in different devices. However, the results of a sequential
regression to examine the effect of phone manufacturer on the abil-
ity of a smartphone to measure noise accurately was not statistically
significant (p= 0.68) when other factors were controlled for, such as
phone age, platform and the app being utilized. Thus, further testing
is needed to examine more extensively the relationship between
phone brand and ability to measure noise accurately.
Turning our attention to specific phone apps, the results found
that the best app was on the iOS platform (SLA Lite) with the sec-
ond best app associated with the Android platform (Sound Meter).
Overall, the testing regime showed that iOS apps over measured
true noise levels by an average of 2.93 dB(A) (N= 1052) while apps
on the Android platform under measured noise levels an average of
2.79 dB(A) (N= 420). While this suggests that apps on the Android
platform were slightly more successful at measuring true noise
levels, the high standard deviation value associated with Android
apps (SD = 9.58 dB(A)) highlights the greater degree of variability
associated with measurement apps on that platform; in short, apps
on the iOS (SD = 6.81 dB(A)) were more consistent and less erratic
in terms of their measurement values. Thus, while the results show
that Android devices have mean values closer to true noise levels at
most reference conditions, the best performing and most consis-
tent apps in terms of measurement reliability are on the iOS
A detailed breakdown of the differential between measurement
values for individual apps and reference conditions for all tests is
provided in Table 6. With regard to the performance of specific
apps, the best performer in this regard was SLA Lite. Across the four
reference values, the app had an average under measurement of
only 0.37 dB(A) and was consistently within 1 dB(A) of the true
noise level at each reference condition which compares very
favourably with SLMs. Moreover, the standard deviation associated
with measurements using SLA Lite was small (1.41) highlighting
the consistency of the app in terms of its measurement accuracy.
Despite the ability of the app to measure accurately, one of the
main drawbacks is its inability to log data over a specified time
period; upgrading to SLA for a fee of 4.99 does enable data logging
but in a way that is not very end user-friendly.
Turning to the Android platform, the most accurate app was
Sound Meter which under measured noise by 1.93 dB(A), under
the typically acceptable error threshold of ±2 dB(A). However,
across all reference levels it can be seen that the average differen-
tial from the true noise level is between 3 and 4 dB(A). It can be
seen also that despite the mean values for Android apps holding
up well when compared to true noise levels, the standard deviation
values associated with most Android apps are typically a lot higher
than those associated with iOS apps. This suggests a lack of mea-
surement consistency for Android apps when compared to corre-
sponding apps for the iOS.
The boxplot in Fig. 2 shows a visual breakdown of the distribu-
tion of the difference between reference and measured data by
noise measurement application while Fig. 3 shows a similar visual
breakdown but for each specific reference condition background,
50, 70, and 90 dB(A). It can be seen that, with the exception of dB
Meter Pro, the applications with the lowest degree of variability
are all on the iOS platform with those on the Android platform
associated with more varied data distributions. Indeed, apps such
as SLA Lite and SPLnFFT, in particular, have data ranges which
are considerably narrower than other apps indicating that those
apps are more consistent in terms of their ability to measure envi-
ronmental noise accurately. The more detailed breakdown by
specific reference condition shows that the highest degree of vari-
ability lies at the background refence condition and also shows
that Android apps are associated with a higher degree of variability
Table 4
Relationship between measurement accuracy and the phone platform.
Platform NMean Mean difference Std. deviation Std. error mean tp-Value
B’grd (27 dB(A)) iOS 263 35.436 11.35 7.57 0.46 10.93 0.00
Android 105 24.219 9.50 0.92
50 dB(A) iOS 263 53.407 5.31 3.70 0.22 5.45 0.00
Android 105 48.291 9.71 0.94
70 dB(A) iOS 263 71.253 0.67 4.67 0.28 0.71 0.48
Android 105 70.856 9.13 0.89
90 dB(A) iOS 263 87.994 5.50 5.84 0.36 7.29 0.00
Android 105 82.491 8.02 0.78
Asterisks denotes significant at the 0.05 alpha level.
Table 5
Mean difference from reference conditions and phone manufacturer.
NMean Standard deviation Range
iPhone Background (27) 263 8.57 7.57 30.50
50 263 3.61 3.70 20.00
70 263 1.52 4.68 26.00
90 263 2.01 5.84 29.50
Total 1052 2.92 6.80 51.00
Galaxy Background (27) 72 4.49 8.38 32.20
50 72 0.96 7.30 36.80
70 72 2.42 7.02 39.00
90 72 7.32 7.65 49.50
Total 288 2.10 8.40 55.50
Google Background (27) 6 3.15 5.34 11.80
50 6 16.85 13.80 39.20
70 6 7.45 14.00 34.80
90 6 13.05 8.69 25.40
Total 24 10.12 11.62 39.50
HTC Background (27) 12 2.06 9.42 35.10
50 12 4.14 7.78 27.50
70 12 5.00 6.75 22.40
90 12 1.61 6.95 21.90
Total 48 0.33 8.32 42.70
LG Background (27) 6 16.15 5.96 15.50
50 6 6.43 5.62 14.30
70 6 6.27 5.12 14.50
90 6 4.08 4.65 11.20
Total 24 6.19 8.85 33.90
Motorola Background (27) 9 7.94 5.36 12.80
50 9 15.14 5.78 14.60
70 9 15.27 5.75 14.80
90 9 15.48 5.90 15.20
Total 36 13.45 6.33 23.20
E. Murphy, E.A. King / Applied Acoustics 106 (2016) 16–22 19
at all reference conditions. Moreover, at the 90 dB(A) condition
there exists a significant number of data outliers
which suggests
a more erratic pattern of measurement at higher noise levels when
compared with the 50 and 70 dB(A) conditions.
In order to examine the relationship between app and measure-
ment accuracy more concretely, a sequential regression was
undertaken to examine the effect of noise measurement applica-
tion on the ability of a smartphone to measure noise accurately.
The results of the regression were statistically significant
(p= 0.00) when other factors were controlled for such as phone
brand, platform and the age of the smartphone indicating a statis-
tically significant relationship between the app being used and
measurement accuracy. This highlights the importance of choosing
the correct app for environmental noise measurement.
Table 7 shows the results of smartphone testing by the age of
phone broken down into five categories. They show that the new-
est phones are also the phones that measure noise closer to true
noise levels; phones that are less than six months old have an aver-
age differential from reference noise levels of only 0.15 dB(A) when
compared with smartphones that are more than two years old
(2.76 dB(A)). Moreover, the results show that all phones perform
poorly at measuring background noise irrespective of age. At both
50 dB(A) and 70 dB(A) phones that are less than six months old
perform best while at 90 dB(A) phones that are more than two
years old perform best. This suggests either that newer phones
are equipped with better microphone technology when compared
with older phones or that the performance of the microphone in
smartphone devices deteriorates with age. While this trend is
hardly surprising, what is counter-intuitive is the fact that the
standard deviation values tends to decline with smartphone age.
This implies that while younger phones are typically better on
average at measuring true noise levels they are also less consistent
in doing so when compared with older smartphones which are
associated with measurements that have a tighter distribution
around the mean. Indeed, the result of one-way ANOVA confirmed
a significant difference between the mean values of smartphones
by age category (p= 0.00). In order to examine the issue further,
a sequential regression was undertaken to examine the effect of
phone age on the ability of a smartphone to measure noise accu-
rately. The results were statistically significant (p= 0.01) when
other factors were controlled for, such as phone brand, platform
and the app being utilized confirming a statistically significant
relationship between phone age and ability to measure noise
4. Discussion and conclusion
The use of smartphones for measuring environmental noise,
while currently in its infancy, has significant potential in the future
to act as a form of crowd sourced noise monitoring. The use of
everyday technology such as a smartphone to measure environ-
mental noise has the potential to improve the monitoring of the
sound environment in cities and the countryside alike but it poten-
tially has the added advantage of engaging and indeed empower-
ing citizens to contribute to monitoring the environment in
which they live and work. Moreover, if smartphone-based noise
measurement apps prove to be useful in the future, they could play
an important role for mapping environmental noise in cities and
assisting with the implementation of the EU Environmental Noise
Directive and associated action planning [see 9–11]. The examina-
tion of the errors associated with smartphone-based noise apps is a
useful first step in this regard.
Compared with previous studies that have tested the accuracy
of smartphones for measuring noise [6,7], this study includes a
Table 6
Performance of individual apps compared to reference conditions.
NMean Standard
SLA Lite (i) Background (27) 65 0.57 1.11 6.40
50 65 0.76 1.21 6.20
70 65 0.55 1.68 12.00
90 65 0.75 1.14 4.90
Total 260 0.37 1.41 12.90
SPLnFFT (i) Background (27) 66 3.97 1.20 6.90
50 66 2.90 1.68 12.70
70 66 2.31 2.55 10.90
90 66 1.79 3.02 10.80
Total 264 2.74 2.36 14.30
dB Meter Pro (i) Background (27) 66 19.92 2.95 20.00
50 66 4.23 2.97 20.00
70 66 3.38 2.80 19.00
90 66 10.94 3.10 17.00
Total 264 2.45 11.81 51.00
UE SPL (i) Background (27) 66 9.70 1.47 9.00
50 66 8.02 1.56 10.00
70 66 7.68 1.77 10.00
90 66 1.89 2.19 11.00
Total 264 6.82 3.43 19.00
Sound Meter (A) Background (27) 35 3.60 6.00 31.00
50 35 3.11 8.77 36.00
70 35 4.80 9.34 44.00
90 35 3.77 9.40 37.00
Total 140 1.93 9.04 44.00
Noise Meter (A) Background (27) 35 6.73 9.77 45.20
50 35 7.49 8.87 41.20
70 35 5.09 7.82 40.40
90 35 13.65 5.64 30.30
Total 140 8.24 8.71 56.40
Decibel Pro (A) Background (27) 35 5.21 8.99 36.00
50 35 0.75 8.58 34.40
70 35 2.86 7.10 30.50
90 35 5.11 4.22 19.30
Total 140 2.05 8.11 39.40
Fig. 2. Boxplot showing data distribution of difference between reference and
measured values by smartphone application.
Outliers are indicated by asterisks and circles.
20 E. Murphy, E.A. King / Applied Acoustics 106 (2016) 16–22
much more extensive range of testing. First, it tests 100 phones of
various makes and models comprising 1472 tests. Smartphones
from seven manufacturers were tested comprising 18 different
Android phone models and 7 different iOS models. By virtue of
the testing range, smartphones across a variety of age cohorts are
included in the analysis thereby reflecting to a greater extent the
population of smartphones in use among the general public. Sec-
ond, we tested a range of leading smartphone apps across the
iOS and Android platforms. While other studies have also com-
pleted similar testing, the testing of apps has not been completed
across such a volume and variation of phone makes and models
as is included in this study.
The accuracy of noise measurement apps varied widely relative
to pre-specified reference levels. Overall, there is little doubt that
iOS apps performed better than Android-based apps. While some
Android apps performed better than those for the iOS in terms of
mean differential from reference values (e.g. Sound Meter), the
high degree of measurement variability associated with such apps
renders their reliability questionable. What we can say is that if a
large number of sample measurements are being taken then
Android apps such as Sound Meter and Decibel Pro will tend to
converge on a noise measurement level that is roughly within
±2 dB(A) of the true noise levels. However, in the absence of a large
number of sample measurements, iOS apps such as SLA Lite and
SPLnFFT should be utilized due to their ability to measure with less
variability around the mean noise level. In fact, SLA Lite was the
only app accurate to within ±2 dB(A) across all of the reference
conditions background, 50, 70 and 90 dB(A) even though other
apps such as SPLnFFT (iOS) and Sound Meter (Android) performed
relatively well. Thus, as things stand currently we can conclude
that noise apps are not quite ready to replace traditional SLMs
but our results suggest the likelihood that as software and hard-
ware technology improves there is ample scope for noise apps to
perform an important role in crowd sourced environmental noise
monitoring in the near future. The accuracy of the SLA Lite app
clearly demonstrates that a combination of good hardware and
software achieves noise monitoring results that are very accurate
provided an adequate number of sample measurements are taken.
Another issue is the fact that three out of the seven apps tested
reported average sound levels below reference levels. This is some-
what worrying because apps that incorrectly report noise levels
below the true level are more problematic from a public health
perspective. Adaptation of the precautionary principle informs us
that it is better for an app to report noise levels slightly above
the true level because at least then regulators have information
which allows the public to be protected adequately. In this regard
there was a greater tendency for Android-based apps to under
report noise levels than iOS apps.
Fig. 3. Boxplots showing measurement results by smartphone application at various reference conditions.
E. Murphy, E.A. King / Applied Acoustics 106 (2016) 16–22 21
The technical specification of a Type 1 SLM includes a floor
below which the device cannot measure sound (16.6 dB for A-
weighting) [7]. Similar to results emerging from Nast et al’s [7]
study, our experiments demonstrate that the average differential
from true noise level was greater than 5 dB(A) for all apps tested.
An exception to this trend was the SLA Lite app which was within
0.15 dB(A) of the true noise level for the background reference con-
dition. Indeed, the differential between measured and true noise
levels was greatest for background noise suggesting that apps per-
form poorest for ambient noise measurement. However, this is not
a significant problem given that noise at ambient levels does not
typically pose a public health threat.
Finally, our results also suggest that the age of the phone has a
bearing on its ability to measure noise accurately; on average,
younger phones measure noise more accurately than older phones
but with greater volatility. This result elucidates an aspect of
smartphone testing which has not been investigated previously
in the academic literature. Whether the result is due to the deteri-
oration of microphone hardware over time due to everyday wear
and tear or due to contemporary versions of noise apps which
are coded more accurately for microphones in newer smartphones
is unclear and requires more extensive testing. Also requiring fur-
ther work is the relationship between phone manufacturer and
noise measurement accuracy. In this regard, further research is
needed to investigate whether microphones used by specific
smartphone manufacturers is producing better measurement out-
comes as is tentatively implied by the results for our study.
This research was supported by grants awarded to E. Murphy
from the Irish Research Council under their Research Project
Grants Scheme (Starter Award) and by a Fulbright Scholarship to
conduct research as a visiting professor at the University of Hart-
ford, USA. E. Murphy also thanks the Department of Mechanical
Engineering, University of Hartford for hosting his research stay
and for providing the research infrastructure necessary to com-
plete the experiments. E.A. King acknowledges the financial assis-
tance of the College of Engineering, Technology and Architecture
Faculty Student Engagement Grant at the University of Hartford,
USA. We both thank Jake Schepro, Sean Rahusen and Lane Miller,
students of the Acoustics Program at the University of Hartford,
who provided research assistance for the lab experiments. We also
thank Prof. Bob Celmer, University of Hartford for constructive
comments on an early draft of the manuscript.
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Table 7
Descriptive statistics showing phone testing results by age of smartphone.
NMean Standard deviation Range
<6 Mths Background (27) 145 4.18 10.16 43.80
50 145 0.81 7.67 54.20
70 145 0.32 6.71 53.80
90 145 4.69 7.13 46.20
Total 580 0.15 8.61 61.20
6–12 Mths Background (27) 76 5.19 9.12 43.00
50 76 1.81 7.11 44.70
70 76 1.62 7.71 53.50
90 76 3.32 8.09 51.00
Total 304 1.32 8.55 54.00
1–1.5 Yrs Background (27) 39 6.15 9.74 39.60
50 39 3.05 4.31 15.10
70 39 2.38 4.91 19.90
90 39 2.58 5.87 19.70
Total 156 2.24 7.21 39.60
1.5–2 Yrs Background (27) 76 5.76 9.60 42.00
50 76 3.90 4.65 20.00
70 76 2.58 4.84 25.40
90 76 2.93 6.64 32.00
Total 304 2.32 7.44 53.50
2 + years Background (27) 32 8.85 7.77 27.50
50 32 3.12 3.40 10.20
70 32 1.01 4.04 13.00
90 32 1.90 4.90 16.70
Total 128 2.76 6.55 37.00
22 E. Murphy, E.A. King / Applied Acoustics 106 (2016) 16–22
... Some scientists and technicians have started to investigate on questions like those, see for example Kardous and Shaw [1], and Nast et al. [2], whose articles start indicating, perhaps for the first time, how to evaluate these applications. Murphy and King [3] point out that: "The use of smartphones for measuring environmental noise, while currently in its infancy, has significant potential in the future to act as a form of crowd sourced noise monitoring". ...
... On the contrary, the one that has responded worse is SPL Meter. Results seem to coincide with those shown by the authors previously mentioned in the literature [1][2][3]. With regard to reverberation time, the best results correspond to the application called Reverberation Time. ...
Nowadays, the number of applications developed for smartphones is quite huge and, among them, we can find applications dedicated to measure acoustics parameters. In this work, we have done a comparison between eight of these applications and a reference sound level meter, obtaining sound pressure level, directivity and reverberation time at different frequencies and levels. The results can help to choose the most precise application according to the required magnitudes for acoustics studies.
... Nowadays, the developments of applications for mobile devices have provided nonexperts with an accessible and low-cost alternative to measure noise levels. In laboratory testing, noise measurement apps for Apple smartphones and tablets with built-in microphones were found to be better than Android devices [21]. While three iOS apps were found to be unreliable compared to the sound level meter [22], certain apps without calibration proved to be reliable in the laboratory conditions [21,23,24]. ...
... In laboratory testing, noise measurement apps for Apple smartphones and tablets with built-in microphones were found to be better than Android devices [21]. While three iOS apps were found to be unreliable compared to the sound level meter [22], certain apps without calibration proved to be reliable in the laboratory conditions [21,23,24]. Consequently, several attempts have been made to measure noise levels using apps outdoors [25], in slaughterhouses [26], and in hospitals [22,[27][28][29][30]. ...
Full-text available
Purpose: This study aims to explore the suitability of using smartphone applications with low-cost external microphones in measuring noise levels in intensive care units. Methods: Four apps and two external microphones were tested in a laboratory by generating test signals at five noise levels. The average noise levels were measured using the apps and a professional device (i.e. a sound level meter). A field test was performed in an intensive care unit with two apps and one microphone. Noise levels were measured in terms of average and maximum noise levels according to the World Health Organisation's guidance. All the measurements in both tests were conducted after acoustic calibration using a sound calibrator. Results: Overall, apps with low-cost external microphones produced reliable results of averaged noise levels in both the laboratory and field settings. The differences between the apps and the sound level meter were within ±2 dB. In the field test, the best combination of app and microphone showed negligible difference (< 2 dB) compared to the sound level meter in terms of the average noise level. However, the maximum noise level measured by the apps exhibited significant differences from those measured by the sound level meter, ranging from − 0.9 dB to − 4.7 dB. Conclusion: Smartphone apps and low-cost external microphones can be used reliably to measure the average noise level in the intensive care unit after acoustic calibration. However, professional equipment is still necessary for accurate measurement of the maximum noise level.
... The voice module then identifies whether the sound received by the library has been stored or not. The microcontroller [8] [9]. then boosted the servo according to the command that was available in the command in gear position one it would move in position one and so on [10] [11]. ...
... In this storage test, an experiment was carried out up to 7 times to do the test whether the sound could be saved by the voice recognition module [9]. The sound given must be clear in terms of intonation and pronunciation of the sentence, known in the first experiment of the voice recognition module repeating the same sentence up to 4 times. ...
Full-text available
Voice recognition is also connected to the microcontroller connected to the servo motor to move the derailleur rear as a gear drive. The results of research on the gear motion control system on bicycles using voice comments can move in the correct gear position. Storage of voice commands is done 7 times and repetition of the same command no later than 4 times while the fastest 2 times repetition. Test results in a crowded condition obtained 72% success and an average delay of 1.38 seconds and the test results in a quiet state obtained the success of 88% and an average delay of 1.20 seconds. The results of this study indicate that this study is able to move the gear in a moving state with voice commands in a quiet state, the maximum level of voice commands is 96.1 dB and the minimum level of voice commands is 80.2 dB when crowded, the maximum level of voice commands is 101.9 dB and obtained the minimum level of voice commands is 88.1 dB, but this research still has shortcomings, namely if the voice command given is above the maximum voice command level and below the minimum level, the voice command cannot be processed and the gear cannot move. seen from the difference between the maximum crowded state and the maximum quiet state of 5.8 dB and the difference between the minimum crowded state and the minimum quiet state of 7.9 dB.
... The results revealed that the apps made measurements within 1.0 dB(A) of a Type 1 SLM, which indicates that, in some cases, smartphones can measure noise levels as accurately as traditional SLMs. Additionally, King and Murphy (2016), who have tested over 100 models of smartphones, asserted that iOS apps were superior to Android apps, and certain iOS apps measured noise levels within 1.0 dB(A) of an ANSI Type 1 SLM. In 2016, another extended study by Kardous and Shaw highlighted that the gap between smartphone-based apps and professional instruments was swiftly narrowing, which concurred with the findings of Roberts et al. (2016). ...
... The difference between iPhone 7 and 12 was insignificant (i.e., 1dB(A)). This indicates that iPhone devices (i.e., iOS) can give more reliable noise measurements, which is consistent with the findings of King and Murphy (2016). On the other hand, the difference for the Galaxy device (Android apps) from the SLM was quite significant (i.e., 6dB(A)), as illustrated in Figures 1 and 2 below. ...
Technical Report
Full-text available
Noise can be described as “unwanted sounds,” while sound is a term used to describe the sensation the brain experiences when the ear senses pressure changes in the air. An example is environmental noise (also known as community noise and noise pollution), which can be defined as noise emitted from all sources except industrial workplaces (WHO, 2011; IOSH, 2018). It is a global occupational health hazard with notable social and physiological impacts. Excessive noise can seriously affect people’s health and daily activities at home, work, school as well as during leisure time. It is a pervasive environmental pollutant that can lead to various adverse effects including disturbance of rest and sleep, interference with speech communication and intended activities, effects on performance, behavior, mental-health as well as psycho-physiological effects. Along with being the key causative environmental agent for sensorineural hearing loss, noise has also been linked with an increased prevalence of cardiovascular disease (e.g., myocardial infarction) and hypertension (Girard et al., 2009; Basner et al., 2014; Kerns et al., 2018). This Technical Report provides information pertaining to noise level evaluations using traditional industry means and smart applications with key guiding principles.
... In terms of app selection, this study only screened apps for iOS systems and did not search for apps for Android systems. A prior study compared the accuracy of SLM apps for both systems and concluded that iOS apps were more accurate [8]. In addition, there are many brands of Android phones, unlike Apple iPhones, which use fewer and uniform hardware (eg, microphones and chips), and this may influence study outcomes and translatability [7]. ...
Full-text available
Background Overexposure to occupational noise can lead to hearing loss. Occupational noise mapping is conventionally performed with a calibrated sound level meter (SLM). With the rise of mobile apps, there is a growing number of SLM apps available on mobile phones. However, few studies have evaluated such apps for accuracy and usefulness to guide those with occupational noise detection needs in selecting a quality app. Objective The purpose of this study was to evaluate the accuracy and usefulness of SLM mobile apps to guide workplace health and safety professionals in determining these apps’ suitability for assessing occupational noise exposure. Methods The following three iOS apps were assessed: the NIOSH (National Institute for Occupational Safety and Health) Sound Level Meter, Decibel X, and SoundMeter X apps. The selected apps were evaluated for their accuracy in measuring sound levels in low-, moderate-, and high-noise settings within both simulated environments and real-world environments by comparing them to a conventional SLM. The usefulness of the apps was then assessed by occupational health specialists using the Mobile App Rating Scale (MARS). Results The NIOSH Sound Level Meter app accurately measured noise across a range of sound levels in both simulated settings and real-world settings. However, considerable variation was observed between readings. In comparison, the Decibel X and SoundMeter X apps showed more consistent readings but consistently underestimated noise levels, suggesting that they may pose a risk for workers. Nevertheless, none of the differences in sound measurements between the three apps and the conventional SLM were statistically significant (NIOSH Sound Level Meter: P =.78; Decibel X: P =.38; SoundMeter X: P =.40). The MARS scores for the three apps were all above 3.0, indicating the usefulness of these apps. Conclusions Under the conditions of this study, the NIOSH Sound Level Meter app had equivalent accuracy to the calibrated SLM and a degree of usefulness according to the MARS. This suggests that the NIOSH Sound Level Meter app may be suitable for mapping noise levels as part of a monitoring strategy in workplaces. However, it is important to understand its limitations. Mobile apps should complement but not replace conventional SLMs when trying to assess occupational noise exposure risk. Our outcomes also suggest that the MARS tool may have limited applicability to measurement-based apps and may be more suited to information-based apps that collect, record, and store information.
... Hal yang perlu dipastikan untuk menggunakan smartphone sebagai alat ukur kebisingan adalah tingkat akurasi. Ini membuat perlu dilakukan studi yang membandingkan nilai kebisingan dari Sound Level Meter dengan aplikasi dari smartphone seperti studi dari Murphy & King (2016) dan McLennon et al. (2019). Jika dapat dipastikan bahwa aplikasi di smartphone bisa digunakan dalam pengukuran kebisingan, maka selanjutnya pengumpulan data kebisingan berpotensi dilakukan berbasis crowdsourcing atau banyak orang seperti studi (Lee et al., 2020) dan ) (Gambar 2). ...
Full-text available
Kebisingan lalu lintas dapat memberikan dampak negatif pada manusia seperti penyakit kardiovaskular dan kesehatan mental. Ini membuat pengendalian kebisingan merupakan hal yang penting. Data yang representatif menjadi salah satu kunci utama dalam pengendalian kebisingan. Hal ini dikarenakan data yang representatif seperti peta sebaran kebisingan dapat membantu dalam pengambilan keputusan terkait rencana aksi pengurangan kebisingan di lingkungan. Oleh karena itu, artikel ini akan melakukan review literatur untuk membahas beberapa tantangan atau gap yang mungkin muncul dalam kajian pemetaan kebisingan menggunakan SIG (Sistem Informasi Geografis), sehingga dapat disimpulkan beberapa future work untuk pemetaan kebisingan lalu lintas. Hasil mengindikasikan bahwa kajian pemetaan kebisingan di Indonesia masih terbatas sehingga perlu ada kajian, salah satunya adalah perbandingan antar metode interpolasi hingga hiperparameter.
... The sound detector was an iPhone 7 with an iOS platform app (Sound Level Analyzer Lite (iOS version 6.0.2)) 16 . In addition, light transmission was evaluated atthe light source and detecter, which were placed at the same position of the light source and detector. ...
Full-text available
In the COVID-19 pandemic, lockdown and acryl partitions were adopted as countermeasures against droplets/aerosol infections; however, these countermeasures restrict communication. In this study, a blocking device was developed using negative ions and an electric field. The device blocks mists simulating droplets/aerosol by a maximum of 89% but transmits light and sound, which is important for communication. The device demonstrated effective blocking performance for aerosol, including the COVID-19 virus spread from patients in a clinic. Our device can help prevent infections without disrupting communication.
... The arrangement of the microphones was randomly changed in each session to reduce the effect of microphone positioning on the recordings. Therefore, before each recording session, the ambient noise was measured in units of dB(A) using the Sound Level Analyzer Lite 4 + (Toon, LLC) iOS application with an Apple iPhone 6 (ModelMQ3D2TU/A, iOS8) with an iOS operating system [22]. Researchers have recommended background noise levels of < 25 dB(A) for measurements at a distance of 30 cm and < 35 dB(A) for shortrange measurements with head-mounted microphones [3,23]. ...
Full-text available
Purpose: This study examined and compared the diagnostic accuracy and correlation levels of the acoustic parameters of the audio recordings obtained from smartphones on two operating systems and from dynamic and condenser types of external microphones. Method: The study included 87 adults: 57 with voice disorder and 30 with a healthy voice. Each participant was asked to perform a sustained vowel phonation (/a/). The recordings were taken simultaneously using five microphones AKG-P220, Shure-SM58, Samson Go Mic, Apple iPhone 6, and Samsung Galaxy J7 Pro microphones in an acoustically insulated cabinet. Acoustic examinations were performed using Praat version 6.2.09. The data were examined using Pearson correlation and receiver-operating characteristic (ROC) analyses. Results: The parameters with the highest area under curve (AUC) values among all microphone recordings in the time-domain analyses were the frequency perturbation parameters. Additionally, considering the correlation coefficients obtained by synchronizing the microphones with each other and the AUC values together, the parameter with the highest correlation coefficient and diagnostic accuracy values was the jitter-local parameter. Conclusion: Period-to-period perturbation parameters obtained from audio recordings made with smartphones show similar levels of diagnostic accuracy to external microphones used in clinical conditions.
Audiometric calibration, which includes the calibration of different audiometer transducers and the measurements of ambient noise levels, is historically carried out using Class 1 sound level meters. As technologies advance, many mobile applications (apps) have been developed to measure sound levels. These apps can provide alternative methods for audiometric calibration in places where sound level meters are not available, such as field testing environments, low-to-mid-income countries, and humanitarian settings. These apps, however, cannot be used for audiometric calibration without first evaluating their performance, which depends on multiple factors including the external components (if any), the operating system and the hardware of the electronic devices. The evaluation of the apps is actually the evaluation of the app and associated factors (i.e., the app systems). This paper discusses methods to assess several key functions of apps implemented in either Android or iOS operation system for audiometric calibration: 1) checking the measurement accuracy at all testing frequencies, 2) deriving and using correction factors, 3) determining the self-noise levels, and 4) evaluating the linear/measurement range. As audiometric calibration usually uses octave or 1/3 octave bands to measure sound pressure levels of tones and narrowband noises with relatively steady temporal characteristics, the accuracy of an app can be evaluated by comparing the levels measured by the app and a Class 1 sound level meter at each frequency. The level difference between the app and the Class 1 sound level meter at each frequency can then be used to calculate correction factors that can be added to subsequent levels measured by the app to improve its accuracy. In addition, methods to determine the self-noise level and the linearity range of apps are discussed. Sample measurement scenarios and alternative methods are provided to illustrate the evaluation process to determine whether an app is suitable for measuring ambient noise levels and for calibrating different audiometric transducers.
Full-text available
In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest.
Full-text available
This paper explores methodological issues and policy implications concerning the implementation of the EU Environmental Noise Directive (END) across Member States. Methodologically, the paper focuses on two key thematic issues relevant to the Directive: (1) calculation methods and (2) mapping methods. For (1), the paper focuses, in particular, on how differing calculation methods influence noise prediction results as well as the value of the EU noise indicator L(den) and its associated implications for comparability of noise data across EU states. With regard to (2), emphasis is placed on identifying the issues affecting strategic noise mapping, estimating population exposure, noise action planning and dissemination of noise mapping results to the general public. The implication of these issues for future environmental noise policy is also examined.
Environmental Noise Pollution: Noise Mapping, Public Health and Policy addresses the key debates surrounding environmental noise pollution with a particular focus on the European Union. Environmental noise pollution is an emerging public policy and environmental concern and is considered to be one of the most important environmental stressors affecting public health throughout the world. This book examines environmental noise pollution, its health implications, the role of strategic noise mapping for problem assessment, major sources of environmental noise pollution, noise mitigation approaches, and related procedural and policy implications. Drawing on the authors considerable research expertise in the area, the book is the first coherent work on this major environmental stressor, a new benchmark reference across disciplinary, policy and national boundaries. • Highlights recent developments in the policy arena with particular focus on developments in the EU within the context of the European Noise Directive • Explores the lessons emerging from nations within the EU and other jurisdictions attempting to legislate and mitigate against the harmful effects of noise pollution • Covers the core theoretical concepts and principles surrounding the mechanics of noise pollution as well as the evidence-base linking noise with public health concerns.
Many recreational activities are accompanied by loud concurrent sounds and decisions regarding the hearing hazards associated with these activities depend on accurate sound measurements. Sound level meters (SLMs) are designed for this purpose, but these are technical instruments that are not typically available in recreational settings and require training to use properly. Mobile technology has made such sound level measurements more feasible for even inexperienced users. Here, we assessed the accuracy of sound level measurements made using five mobile phone applications or "apps" on an Apple iPhone 4S, one of the most widely used mobile phones. Accuracy was assessed by comparing application-based measurements to measurements made using a calibrated SLM. Whereas most apps erred by reporting higher sound levels, one application measured levels within 5 dB of a calibrated SLM across all frequencies tested.
This study reports on the accuracy of smartphone sound measurement applications (apps) and whether they can be appropriately employed for occupational noise measurements. A representative sample of smartphones and tablets on various platforms were acquired, more than 130 iOS apps were evaluated but only 10 apps met our selection criteria. Only 4 out of 62 Android apps were tested. The results showed two apps with mean differences of 0.07 dB (unweighted) and -0.52 dB (A-weighted) from the reference values. Two other apps had mean differences within ±2 dB. The study suggests that certain apps may be appropriate for use in occupational noise measurements.
In an effort to alleviate traffic congestion and increase the efficiency of public transport in Dublin’s city centre, a ‘bus gate’ was introduced to one particularly sensitive area in the city centre. The scheme restricts private vehicles from accessing the area during peak traffic hours with the aim of generating significant journey time-savings for public transport users and reduced noise pollution in the city centre. This paper quantifies the effect the ‘bus gate’ has had on noise levels in the area. Noise levels were monitored prior to and after the introduction of the scheme and the extent to which the scheme impacted on the noise levels was thus evaluated. The study also estimates the impact extending the ban would have on noise exposure levels in Dublin city centre.
Participatory sensing enables a person-centric collection of environmental measurement data with, in principle, high granularity in space and time. In this paper we provide concrete proof that participatory techniques, when implemented properly, can achieve the same accuracy as standard noise mapping techniques. We do this through a citizen science experiment for noise mapping a 1 km2 area in the city of Antwerp using NoiseTube, a participatory sensing framework for monitoring ambient noise. At the technical side, we set up measuring equipment in accordance with official norms insofar as they apply, also carrying out extensive calibration experiments. At the citizen side, we collaborated with up to 13 volunteers from a citizen-led Antwerp-based action group. From the data gathered we construct purely measurement-based noise maps of the target area with error margins comparable to those of official simulation-based noise maps. We also report on a survey evaluating NoiseTube, as a system for participative grassroots noise mapping campaigns, from the user perspective.
Cell phone and smartphone ownership demographics; 2014. <>
  • Research Center
References [1] Pew Research Center. Cell phone and smartphone ownership demographics; 2014. <> [accessed 30 April 2015].
Background (27) 66 19
  • Meter Db
  • Pro
dB Meter Pro (i) Background (27) 66 19.92 2.95 20.00 50 66 4.23 2.97 20.00 70 66 À3.38 2.80 19.00
Smartphone Use in 2015
  • A U S Smith
Smith A. U.S. Smartphone Use in 2015; 2015. < 2015/04/01/us-smartphone-use-in-2015/> [accessed 30 April 2015].