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

Full text

Testing the accuracy of smartphones and sound level meter applications
for measuring environmental noise
Enda Murphy
a,
⇑
, Eoin A. King
b,1
a
School of Architecture, Planning & Environmental Policy, Planning Building, Richview, University College Dublin, Dublin 4, Ireland
b
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
Keywords:
Noise measurement apps
Environmental noise
Crowd sourced noise monitoring
Smartphones
abstract
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
http://dx.doi.org/10.1016/j.apacoust.2015.12.012
0003-682X/Ó2015 Elsevier Ltd. All rights reserved.
⇑
Corresponding author. Tel.: +353 17162810; fax: +353 17162788.
1
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
purposes.
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
3
ISO 3741 [8] compliant reverberation room. This source
was generated through Brüel & Kj
æ
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
æ
r
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
2
; 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
(Price)
Web Link
Sound Level
Analyzer Lite
(iOS) version 1.3
Toon, LLC
(€4.99)
https://itunes.apple.com/us/
app/sound-level-analyzer/
id886109671?mt=8
SPLnFFT (iOS)
version 1.1
Fabien Lefebvre
(HK€28)
https://itunes.apple.com/hk/app/
splnfft-noise-meter/id355396114?
mt=8
Decibel Meter Pro
(iOS) version
2.05
Performance
Audio (€0.99)
https://itunes.apple.com/ie/app/
decibel-meter-pro/id382776256?
mt=8
UE SPL (iOS) version
2.1.1
Logitech Inc.
(Free)
https://itunes.apple.com/us/app/
ue-spl/id332300068?mt=8
Sound Meter
(Android)
version 1.6
Smart Tools co.
(Free)
https://play.google.com/store/
apps/details?id=kr.sira.sound&hl=
en
Noise Meter
(Android)
version 2.1
JINASYS (Free) https://play.google.com/store/
apps/details?id=com.pjw.
noisemeter&hl=en
Decibel Pro
(Android)
version 1.4.22
BSB Mobile
Solutions Tools
(€4.99)
https://play.google.com/store/
apps/details?id=bz.bsb.decibel.pro
2
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,
3
the operating system
4
and the age of the phone in one of
five categories
5
. 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
outcome.
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
conditions.
6
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
(A)).
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.
3
If students did not know the exact model, it was identified prior to testing.
4
iOS or Android.
5
These were: <6 months, 6–12 months, 1–1.5 years, 1.5–2 years, 2+ years.
6
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
platform.
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.
a
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
a
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
7
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
accurately.
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
deviation
Range
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.
7
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.
Acknowledgements
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.
References
[1] Pew Research Center. Cell phone and smartphone ownership demographics;
2014. <http://www.pewinternet.org/data-trend/mobile/cell-phone-and-
smartphone-ownership-demographics/> [accessed 30 April 2015].
[2] Smith A. U.S. Smartphone Use in 2015; 2015. <http://www.pewinternet.org/
2015/04/01/us-smartphone-use-in-2015/> [accessed 30 April 2015].
[3] GlobalWebIndex; 2015. <http://cdn2.hubspot.net/hub/304927/file-2301369304-
pdf/Reports/GWI_Device_Summary_Q3_2014.pdf?submissionGuid=41ba1017-
ee82-435b-8762-9d837a0cc5ac> [accessed 30 April 2015].
[4] Kanjo E. NoiseSPY: a real-time mobile phone platform for urban noise
monitoring and mapping. Mobile Netw Appl 2010;15:562–74.
[5] D’Hondt E, Stevens M, Jacobs A. Participatory noise mapping works! An
evaluation of participatory sensing as an alternative to standard techniques for
environmental monitoring. Pervasive Mobile Comput 2013;9:681–94.
[6] Kardous CA, Shaw PB. Evaluation of smartphone sound measurement
applications. J Acoust Soc Am 2014;135:EL186–92.
[7] Nast DR, Speer W, Prell CG. Sound level measurements using smartphone
‘‘apps”: useful or inaccurate? Noise Health 2014;16:251–6.
[8] International Organization for Standardisation. ISO 3741-1999, Acoustics:
determination of sound power levels of noise sources using sound pressure –
precision methods for reverberation rooms; 1999.
[9] Murphy E, King EA. Strategic environmental noise mapping: methodological
issues concerning the implementation of the EU Environmental Noise
Directive and their policy implications. Environ Int 2010;36:290–8.
[10] Murphy E, King EA. Environmental noise pollution: noise mapping, public
health and policy. Amsterdam: Elsevier; 2014.
[11] King EA, Murphy E, Rice HJ. Evaluating the impact on noise levels of a ban on
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