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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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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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[5] D’Hondt E, Stevens M, Jacobs A. Participatory noise mapping works! An
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[6] Kardous CA, Shaw PB. Evaluation of smartphone sound measurement
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[8] International Organization for Standardisation. ISO 3741-1999, Acoustics:
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[10] Murphy E, King EA. Environmental noise pollution: noise mapping, public
health and policy. Amsterdam: Elsevier; 2014.
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private cars in Dublin city centre, Ireland. Transport Res Part D: Transport
Environ 2011;16:532–9.
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
... However, in the context of environmental and occupational noise monitoring, for most of these apps there is no information available on the algorithms used as well as no systematic and standardized evaluation of their quality and accuracy, which is a common issue in the field of mHealth apps [21]. There are various studies evaluating the accuracy of existing apps [22][23][24][25][26][27][28][29]. These studies were thereby either conducted in controlled laboratory environments [22][23][24][25][27][28][29] and used pink noise [23,24,28,29], white noise [25,[27][28][29], 1/3 octave band noise [22], or representative audio samples [29] to simulate sound sources with different sound levels, or were performed in real-world field environments [26,28]. ...
... There are various studies evaluating the accuracy of existing apps [22][23][24][25][26][27][28][29]. These studies were thereby either conducted in controlled laboratory environments [22][23][24][25][27][28][29] and used pink noise [23,24,28,29], white noise [25,[27][28][29], 1/3 octave band noise [22], or representative audio samples [29] to simulate sound sources with different sound levels, or were performed in real-world field environments [26,28]. Results indicate that some sound measurement smartphone apps may be considered accurate and reliable to a certain degree (±1 dB(A) or ±2 dB(A) respectively), but most of the apps cannot be used as reliable tool to assess the environmental sound [23,25]. ...
... There are various studies evaluating the accuracy of existing apps [22][23][24][25][26][27][28][29]. These studies were thereby either conducted in controlled laboratory environments [22][23][24][25][27][28][29] and used pink noise [23,24,28,29], white noise [25,[27][28][29], 1/3 octave band noise [22], or representative audio samples [29] to simulate sound sources with different sound levels, or were performed in real-world field environments [26,28]. Results indicate that some sound measurement smartphone apps may be considered accurate and reliable to a certain degree (±1 dB(A) or ±2 dB(A) respectively), but most of the apps cannot be used as reliable tool to assess the environmental sound [23,25]. ...
Full-text available
The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.
... Generally speaking, smartphones' sensors are useful to collect information about noise-related human activities. Recently, measurement of noise with smartphones has gathered attention in the environment monitoring area (Kardous and Shaw 2014;Nast et al. 2014;Robinson and Tingay 2014;Murphy and King 2016;Aumond et al. 2017). In addition, there are many applications available that allow recording of noise levels with a mobile phone. ...
... They attribute this conclusion to the fact that Android devices are built by several different manufacturers' components and features. These results are in agreement with those reported by Murphy and King (2016) who conducted tests with 100 smartphones (1472 tests in total) in these two different platforms in a laboratory environment. Their work Weighting curves (2017) carried out a total of 3409 environmental noise measurements by 60 volunteers using a modified version of the NoiseTube app at 28 locations. ...
... The evolution of the use of smartphones as an instrument to sense noise levels is relatively recent. For example, for sound environmental monitoring, several authors have reported results that seem promising Shaw 2014 andMurphy and King 2016;Aumond et al. 2017). Also, some studies have shown the good performance of sound level meter apps specifically under controlled environments (Murphy and King 2016;Ventura 2017;Kardous 2014). ...
Nowadays, environmental noise pollution is recognized as a major public health concern in big cities around the world. Therefore, it has been of great interest to measure the amount of noise that populations are exposed to as well as its evolution through time. In this work, citizen participation in noise sensing by using their smartphones as sound level meters is discussed. Specifically, relevant studies about this technique are described in the context of urban noise monitoring and noise mapping. There seems to be more studies supporting the use of smartphones for this matter. However, the authors have identified a series of limitations regarding proper calibration of the apparatuses, poor microphone response, and the not-utter control of the behavior of participants, among others. Despite this, the progress observed in this field suggests that there are high possibilities to continue working successfully toward the generation of quality noise maps by means of smartphones as sound sensors.
... Accuracy is increased to ±1 dB(A) when external microphones are employed. Murphy and King [14] found that iOS platforms are superior to Android ones in a reverberation room test. Ventura et al. [15] reported a measurement error standard deviation below 1.2 dB(A) within a wide range of noise levels. ...
... After calibration fewer extreme noise levels were left, being worth a closer look. Nine LAeq values were below 25 dB(A), which are incompatible with the results of Phase III, as we have seen that the lowest possible threshold in this dataset is 26 dB(A), considering sufficiently reliable the results obtained from iOS-based smartphones (see also [14]). All of them refer to the same smartphone model, a circumstance that suggests that this is probably a case of wrong model classification, for which the applied calibration is just not appropriate. ...
Full-text available
We designed and performed a participatory sensing initiative to explore the reliability and effectiveness of a distributed network of citizen-operated smartphones in evaluating the impact of environmental noise in residential areas. We asked participants to evaluate the comfort of their home environment in different situations and at different times, to select the most and least comfortable states and to measure noise levels with their smartphones. We then correlated comfort ratings with noise measurements and additional contextual information provided by participants. We discuss how to strengthen methods and procedures, particularly regarding the calibration of the devices, in order to make similar citizen-science efforts effective at monitoring environmental noise and planning long-term solutions to human well-being.
... This review did not extract the extent to which remote hearing assessment tools monitored environmental noise due to the variable sensitivity of microphones in Android and Windows devices, which leads to inaccurate measurement of background noise levels (Murphy and King 2016). ...
Full-text available
Objective: Remote hearing screening and assessment may improve access to, and uptake of, hearing care. This review, the most comprehensive to date, aimed to (i) identify and assess functionality of remote hearing assessment tools on smartphones and online platforms, (ii) determine if assessed tools were also evaluated in peer-reviewed publications and (iii) report accuracy of existing validation data. Design: Protocol was registered in INPLASY and reported according to PRISMA-Extension for Scoping Reviews. Study sample: In total, 187 remote hearing assessment tools (using tones, speech, self-report or a combination) and 101 validation studies met the inclusion criteria. Quality, functionality, bias and applicability of each app were assessed by at least two authors. Results: Assessed tools showed considerable variability in functionality. Twenty-two (12%) tools were peer-reviewed and 14 had acceptable functionality. The validation results and their quality varied greatly, largely depending on the category of the tool. Conclusion: The accuracy and reliability of most tools are unknown. Tone-producing tools provide approximate hearing thresholds but have calibration and background noise issues. Speech and self-report tools are less affected by these issues but mostly do not provide an estimated pure tone audiogram. Predicting audiograms using filtered language-independent materials could be a universal solution.
... Each smartphone was subjected to separate testing for the top noise monitoring applications, for a total of 1472 tests. The data also suggests that there is a considerable link between phone life and its capacity to reliably assess noise, as well as the cost of ambient noise monitoring and evaluation systems (Murphy & King, 2016). The findings of the evaluation led to the creation of a calibration technique, which was included in Ambiciti and used on over 50 devices at public events. ...
Full-text available
Noise pollution is one of the most serious environmental threats to human health. Noise is becoming more prevalent in urban areas, and it is having a negative impact on human health. The increase in noise is due to the increase in the number of vehicles that creates chaos over the road due to honking. Smart monitoring using is smartphones is required to reduce human dependency and monitor data efficiently to reduce logistical obstacles. A smartphone-based noise monitoring solution can handle the problem of monitoring noise at various traffic crossings in a metropolis. The topographical data, noise data, and noise prediction models are required for forecasting noise levels and showing them as maps. In the Indian city of Lucknow, the entire procedure is being performed by providing a map of 2D and 3D forms. The smartphone-based software tracks noise levels at three road crossings at three different times each day. The collected noise levels were calibrated against a standard noise metre to achieve correct noise levels for these sites. Following that, three noise environment types are chosen and mapped using open-source satellite images and conventional noise models through the web on the GIS platform. The anticipated noise levels on the maps were compared to recorded noise data from identical locations using a conventional noise metre for these three crossings and were found to be within 5.5 dB of accuracy. For 3D mapping, shadow height provides the Z value for point cloud DEM generation for 3D model for noise data of city of Lucknow.
... However, they are often bulky and expensive devices that are difficult for a layperson to operate. Several studies demonstrated the applicability of smartphones for noise dosimetry using the built-in microphones and dedicated sound level meter apps (12,(14)(15)(16)(17). However, the quality of assessment varies widely and depends on the monitoring application, with increasing accuracy seen in newer generations of smartphones and apps (18). ...
Full-text available
Introduction and Objectives Noise-induced hearing loss (NIHL) and tinnitus are common problems that can be prevented with hearing protection measures. Sound level meters and noise dosimeters enable to monitor and identify health-threatening occupational or recreational noise, but are limited in their daily application because they are usually difficult to operate, bulky, and expensive. Smartwatches, which are becoming increasingly available and popular, could be a valuable alternative to professional systems. Therefore, the aim of this study was to evaluate the applicability of smartwatches for accurate environmental noise monitoring.Methods The A-weighted equivalent continuous sound pressure level (LAeq) was recorded and compared between a professional sound level meter and a popular smartwatch. Noise exposure was assessed in 13 occupational and recreational settings, covering a large range of sound pressure levels between 35 and 110 dBA. To assess measurement agreement, a Bland-Altman plot, linear regression, the intra-class correlation coefficient, and descriptive statistics were used.ResultsOverall, the smartwatch underestimated the sound level meter measurements by 0.5 dBA (95% confidence interval [0.2, 0.8]). The intra-class correlation coefficient showed excellent agreement between the two devices (ICC = 0.99), ranging from 0.65 (music club) to 0.99 (concert) across settings. The smartwatch's sampling rate decreased significantly with lower sound pressure levels, which could have introduced measurement inaccuracies in dynamic acoustic environments.Conclusions The assessment of ambient noise with the tested smartwatch is sufficiently accurate and reliable to improve awareness of hazardous noise levels in the personal environment and to conduct exploratory clinical research. For professional and legally binding measurements, we recommend specialized sound level meters or noise dosimeters. In the future, smartwatches will play an important role in monitoring personal noise exposure and will provide a widely available and cost-effective measure for otoprotection.
Historically, noise and technology have been closely related – from assessing, measuring, monitoring and mapping noise, to its control and mitigation as well as engaging the general public. For environmental noise, technology is becoming increasingly important for all these tasks but particularly for passive and dynamic monitoring of noise pollution in a range of urban and rural settings. In this area, improvements in microphone technology in mobile devices such as smartphones and wireless sensors has facilitated more large-scale and dynamic monitoring of the surrounding noise environment. This chapter focuses on essential technological changes in the broad area of environmental noise including low-cost acoustic devices, wireless monitoring, electric vehicles and tyre technology as well as considering some of the main drawbacks of technological advances for the sound environment.
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].