Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices [original]

sensors

Article

Towards the Interpretation of Sound Measurements from

Smartphones Collected with Mobile Crowdsensing in the

Healthcare Domain: An Experiment with Android Devices

Robin Kraft 1,2,* , Manfred Reichert 1and Rüdiger Pryss 3





Citation: Kraft, R.; Reichert, M.;

Pryss, R. Towards the Interpretation

of Sound Measurements from

Smartphones Collected with Mobile

Crowdsensing in the Healthcare

Domain: An Experiment with

Android Devices. Sensors 2022,22,

170. https://doi.org/10.3390/

s22010170

Academic Editor: Annie Lanzolla

Received: 7 October 2021

Accepted: 23 December 2021

Published: 28 December 2021

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in

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iations.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

1Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany;

manfred.r[email protected]

2Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany

3Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97078 Würzburg, Germany;

ruediger[email protected]

*Correspondence: r[email protected]

Abstract:

The ubiquity of mobile devices fosters the combined use of ecological momentary assess-

ments (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.

Keywords: mHealth; crowdsensing; tinnitus; noise measurement; environmental sound

1. Introduction

Smart mobile devices (e.g., smartphones) are becoming increasingly ubiquitous. Their

capabilities allow the combined use of ecological momentary assessments (EMA) and

mobile crowdsensing (MCS) in the healthcare domain to not only collect qualitative lon-

gitudinal and ecologically valid data, but also to use sensors of smartphones as well as

connected external sensors (e.g., wearables) to capture the context in which these data

are collected [

]. For example, environmental data (e.g., noise [

]) can be measured

when a questionnaire is answered to correlate the questionnaire data with the environ-

mental data to gain new insights about patients. However, sensor measurements must be

accurate, comparable, and interpretable to provide meaningful information. Especially

for non-standardized smartphone sensors like the microphone (i.e., different manufactur-

ers, different mobile operating systems, different scales), it can be challenging to achieve

these properties.

The TrackYourTinnitus (TYT) mobile platform uses EMA and MCS to track a user’s

individual tinnitus. Tinnitus is the perception of an internal sound in the ears in the

absence of a corresponding external sound. As symptoms are subjective and vary over

time, TYT was created to monitor and evaluate the variability of these symptoms in the

daily life of tinnitus affected patients or interested users [

]. The platform has been in

Sensors 2022,22, 170. https://doi.org/10.3390/s22010170 https://www.mdpi.com/journal/sensors

Sensors 2022,22, 170 2 of 17

operation since 2014 and is composed of a registration and information website ( https:

//www.trackyourtinnitus.org/, accessed on 1 October 2021), a central backend for data

storage, and a mobile application available for both Android and iOS. The mobile apps

assess users’ individual tinnitus perceptions (e.g., tinnitus loudness and distress) by asking

them to complete tinnitus EMA questionnaires at different times of the day [

]. In addition,

the environmental sound level is captured in parallel with the completion of the daily

questionnaire [

]. The detailed process of the TYT app is described in [

], whereas the

underlying data set (i.e., structure and insights to the collected data) is described in [

The overall objective of this work is to investigate the correlations between environmental

sound level and reported tinnitus symptoms. More specifically, it should be examined

whether the environmental sound level has an effect on tinnitus. If the sound levels can be

correlated to questionnaire-collected data, new insights might be unveiled as the sound

level data can be considered more objective than data from completed questionnaires alone

(e.g., to allow predictions on tinnitus loudness based on the sound data). In this context,

further note that, for tinnitus and many other diseases and disorders, longitudinal studies

that are able to collect ecologically valid data for such a long time are still very rare. In

addition, the collection of objective data succh as the sound level is even more scarce.

Since TYT has been running for more than half a decade, and not all circumstances of the

collection procedure were clear to the developers beforehand, it is now of great interest

to make the collected amount of sound levels interpretable from a medical perspective.

Therefore, the experiment at hand is important for TYT, but the results and lessons learned

may be of much greater value for the healthcare domain in general.

However, the data available in the TYT database [

] do not contain calibrated sound

pressure level (SPL) or weighted decibel (e.g., dB(A)) values, but rather relative amplitude

(Android) or uncalibrated decibel (iOS) values as retrieved from the mobile system APIs.

This fact prevents a direct comparison of these values and therefore a meaningful interpre-

tation regarding the correlation with tinnitus symptoms. A preceding calibration of the

mobile devices and storing respective dB

SPL

, dB(A) or dB(C) values would circumvent this

issue. To encounter that sound sensor values measured by a smartphone require further

considerations in healthcare scenarios is also recognized by other works than

TYT [7]

. From

a general viewpoint, sensor measurements collected by a modern smartphone for health-

care purposes require many considerations before collected sensor data can be actually

evaluated. In [

], for example, challenges are discussed in the context of fall detection. One

of the challenges discussed by the authors of [

] also has implications for the data collected

by TYT, namely the usability when collecting sensor data. If a user has his or her smart-

phone in the pocket, collected sensor values may not be usable. Consequently, works can

be found that try to mitigate such challenges on a more generic level [

]. However, the data

in the TYT database were collected for more than six years with more than 100,000 entries,

and the respective mobile apps used to collect these values cannot be changed retroactively

to counteract the described issues. Since no other works could be found that helped to

analyze these pre-existing collected sound pressure levels, the following requirements were

established for the experiment shown in the work at hand:

•

Identification of an experimental setting that can be used to learn more about the

interpretation possibilities of the collected TYT sound level values.

•

In addition to the latter point, in the best case, the experiment should be appropriate

to enable us to compare all sound level values across the different smartphone devices

from different manufacturers and different mobile operating systems.

•

Conduction of the experiment without the use of an expensive sound laboratory, with

the goal to foster and facilitate the overall reproducibility.

Based on these requirements, different scenarios have been discussed. In the end, the

following approach (i.e., list of decisions for the experiment) was conceived to make the

described values usable and comparable:

Sensors 2022,22, 170 3 of 17

The TYT database was analyzed to identify the mobile device models that contributed

the most environmental sound measurement data.

The analysis of the database showed that more detailed device information is available

for Android devices. For this reason, it was decided to use Android devices for

the experiment.

A sample of the identified device models was selected and acquired (i.e., we purchased

these devices for the experiment).

A new mobile application was developed that mimics the behavior of the TYT app

with respect to the sound measurement. More specifically, the app was implemented

with the specific focus on the sound measurement but using the same software

functions as TYT (i.e., by copying the relevant source code fragments from the origi-

nal app).

5. The selected device models were equipped with this mobile application.

For the evaluation of the smartphone devices equipped with the app, a sound signal

was generated, for which the volume was adjusted to different sound levels using

a professional calibrated sound level meter (SLM). Based on this setting, the values

captured by the mobile app on the different mobile devices were recorded.

Finally, the results were used to derive equations for the different device models that,

in turn, can be used to transform the measurement data in the database into (partially)

comparable dB(C) values.

How these steps were carried out in practice and what results were achieved are

discussed in the following sections. In Section 2, a detailed discussion of related works

will be presented. Section 3presents the experiment in detail, while Section 4presents its

results. A discussion of the results with respect to limitations and practical relevance will

be provided in Section 5. Section 6closes our work with a summary and an outlook for

future work.

2. Related Work

Measuring sound levels with smartphones has been a topic of research for some time.

There are both scientific and commercial implementations of apps that perform sound

measurements. In addition, studies evaluating the accuracy and precision of these apps

can be found in the literature. Moreover, the ability of smartphones to perform sound level

measurements in the environment as well as their calibration has been investigated and

discussed in a thorough manner. Finally, there are works that deal with large data sets of

sound levels measured with smartphones.

NoiseMap [

] is an Android app that performs geo-referenced sound measurements

and sends these data to an open urban sensing platform following a participatory sensing

approach to create real-time noise maps and data graphs. The incoming sound signal is

sampled and first translated to a relative dB full scale (dBFS) value and subsequently to a

SPL

value by adding a constant calibration value. A built-in calibration tool can be used

to determine this value using a constant pink noise [

]. The iOS app SoundLog [

] was

developed by the Australian National Acoustic Laboratories (NAL) with the aim to pro-

vide a personal noise dosimeter. The app is capable of measuring A-weighted equivalent

continuous sound levels (LA

), C-weighted peak sound pressure levels (LC

), as well as

other values for different sampling periods [

]. Ambiciti [

] is a mobile app developed

for both Android and iOS that utilizes mobile crowdsensing to enable urban noise moni-

toring. The app performs automatic background noise measurements in dB(A) using the

microphone and the user’s location. In addition, a calibration feature is provided [

]. The

accuracy of the app has been evaluated and found to be within

1.2 dB(A) [

]. The City

Soundscape [

] mobile app is used as part of a noise monitoring platform in the context of

acoustic urban planning in smart cities. The app mimics the user interface of a professional

SLM and is able to measure dB

SPL

and equivalent continuous sound level (L

) values [

Furthermore, there are numerous apps implementing sound measurements available in the

Google Play Store (e.g., refs. [

–

]) and the Apple App Store (e.g.,

refs. [18–20]).

However, in

Sensors 2022,22, 170 4 of 17

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 [

]. There are various studies evaluating the accuracy of existing

apps [

–

]. These studies were thereby either conducted in controlled laboratory en-

vironments [

–

] and used pink noise [

], white noise [

–

], 1/3

octave band noise [

], or representative audio samples [

] to simulate sound sources with

different sound levels, or were performed in real-world field environments [

]. 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 [

]. In general, iOS

apps performed better than Android apps, which can be attributed to the fact that Android

devices are built by several different manufacturers and there is a lack of conformity of

microphones and other audio components [

]. It has been shown that accuracy can

be improved if the smartphone apps are calibrated before the measurements [

]. Fur-

thermore, it has been shown that the use of an external calibrated microphone can further

increase the accuracy and precision of sound measurements compared to measurements

using internal smartphone microphones [30].

Moreover, the ability of smartphones to perform environmental sound level mea-

surements in general has been extensively discussed in the literature [

–

]. In [

], the

sound capture and processing procedure when using smartphones for environmental noise

measurements is investigated by analyzing the impact and accuracy of different algorithms,

time periods, and sampling strategies for noise calculation. The results indicate that, with

the correct settings, it is possible to measure noise levels in the range of 35–95 dB(A), with

an accuracy of

2 dB(A). Other studies have shown that an adequate sound level meter

smartphone app that is used together with an external microphone can achieve compliance

with most of the requirements of Class 2 of the IEC 61672/ANSI S1.4-2014 standard for

periodic testing [

], as well as full compliance for directional response in the horizontal

plane [

]. The authors of [

] discuss the use of smartphones in the context of urban noise

pollution and present a field-study evaluating the relevancy and accuracy in this context.

The results indicate that smartphones can be used as useful noise measurement devices

with an accuracy of ±3 dB(A) if careful review of the collected data is undertaken.

Furthermore, the calibration of smartphones for sound measurements and different

approaches in this regard have been discussed in this context [

–

]. In [

], a labora-

tory calibration method for noise measurement smartphone apps is presented based on

frequency response linearization and an A-weighted sound level correction. The authors

of [

] introduce a calibration method that does not require user interaction and is based

on a node-based calibration utilizing a linear model and a common indoor quiet noise

base. Slow-start issues of this approach are mitigated with the help of a crowdsourcing-

based calibration. A cross-calibration method for participatory sensor networks based

on outlier detection, crowd sensors-based correction, fixed sensors-based correction, and

day–evening–night noise level (L

den

) estimation is proposed by [

]. In [

], an averaging

method for the calibration of a smartphone microphone against a reference microphone in

terms of sound pressure level and frequency spectrum measurements is presented. It is

shown that the method can be used to calibrate a smartphone using another smartphone

calibrated using the same method. Finally, the authors of [

] propose a calibration method

for smartphones that does not require specific equipment or knowledge of the user by

utilizing the low variability of the average noise emission of vehicles.

Finally, works that deal with large data sets of sound levels measured with smart-

phones can be found in the literature. For example, interpolation [

] and simulation [

]

strategies for producing sound maps based on such smartphone measurements have been

investigated and discussed in this context.

However, to the best of our knowledge, the evaluation of an pre-existing large data set

of uncalibrated environmental sound level amplitude values measured with smartphone

Sensors 2022,22, 170 5 of 17

sensors has not yet been considered in the literature. In this context, the chosen approach

of making the data set of sound measurements comparable and interpretable by taking

a sample of devices from this data set, calibrating them, and deriving corresponding

equations is a novelty. Furthermore, none of the existing related works considers the

assessment of environmental sounds measured with smartphone sensors, or smartphone

sensor measurements in general, in the context of tinnitus.

3. Materials and Methods

First, the materials and methods used to perform the experiments in the scope of the

work at hand are described. In this context, the data set used for the initial analysis is

outlined. Furthermore, the selection of hardware and software components used for the

experiments is described. Finally, the experimental setup and procedure are delineated.

3.1. Data Set for the Analysis

The data set for the analysis has been extracted from the TYT database on 26 January

2020 and contains a total of 76,542 entries. The structure of the TYT data set has been

described in [

]. In this data set, 45,712 (59.72%) entries belong to an Android device, 30,607

belong to an iOS device (39.99%), and 223 of the entries contain no user agent information

(0.29%), as shown in Table 1. As described in [

], for every answer sheet that is collected

with the TYT mobile applications for Android and iOS, the user agent is extracted and

stored together with the answer data. For the Android version of the app, this user agent

contains, among other information, the constant

Build.MODEL

from the

android.os.Build

API ( https://developer.android.com/reference/android/os/Build#MODEL, accessed on

1 October 2021), which can be used to uniquely identify the respective device model (see

Table 2). Note that for the iOS version of TYT, only the device type (iPhone/iPad) and the

OS version is stored in this variable. For this reason, it was decided to use Android devices

for the experiments in the scope of this work.

Table 1. Data set from the TYT database used for the analysis.

Description Entries %

Total 76,542 100

Android 45,712 59.72

iOS 30,607 39.99

No user agent information 223 0.29

Furthermore, a sound level measurement capturing the environmental noise level for

the first 15 s of the user completing the EMA questionnaire is performed and stored together

with the EMA answer data. For the Android version of the app, this value represents an

amplitude value retrieved by the Android

MediaRecorder

API [

] and averaged over the

measurement period. The Android source code that was used in the application to retrieve

this value is later analyzed and discussed in Section 4.2. In contrast, the iOS version stores

a relative dB value, which is not further analyzed in the scope of this work.

3.2. Hardware and Software Selection

The selection of the hardware as well as software used for the experiments is described

in the following. This includes the selection process used to decide on the mobile devices

to be investigated. In addition, other relevant hardware and software used to perform the

experiments themselves, namely the sound level meter, calibrator, speaker, tone generator,

and the mobile application for the sound measurement, are described.

Sensors 2022,22, 170 6 of 17

3.2.1. Mobile Devices

In order to perform the experiments for an optimal subset of devices that allows

assumptions to be made about as many entries in the data set as possible, the data set

described in the previous section was analyzed from two different perspectives.

For the first analysis, the data set was analyzed on a per-device basis. To this end, the

following procedure was used:

1. For each entry, the device IDs of the device models (see Section 3.1) are extracted.

For each extracted device ID, the number of unique users and entries containing a

sound measurement are counted.

For each device ID, the device names are looked up and device IDs with the same

device name are summarized in a new row.

The 30 most used device models resulting from this process are shown in Table 2.

For the second analysis, the data set was analyzed on a per-user basis with regard to

the intended interpretation of the data. Thereby, users (and their respective device models

used) were selected based on the following conditions:

• There are more than 500 entries containing sound measurements for the user.

•

The reported tinnitus loudness (see [

]) is fluctuating and appears plausible (e.g., not

only zero values and not always the same value).

•

The sound measurement is fluctuating and appears plausible (e.g., not only zero

values and not always the same value).

Finally, the identified devices from both analyses were combined, resulting in eight

devices, as highlighted in Table 2. Since the selected device models had to be purchased

and not all devices were available at the time of starting the experiments, only four of the

eight identified devices could be used (highlighted in dark gray in Table 2). On top of these

four devices, a Google Pixel 2 was used simply because it was available to the experimenters.

This resulted in the five devices shown in Table 3. The Android version installed on each

device can be found in the table. These are the maximum versions that were officially

supported by the acquired devices at the time of the experiments.

3.2.2. Reference Sound Level Meter and Calibrator

As a reference sound level meter (SLM) for the performed sound measurements the

testo 815 by Testo SE & Co. KGaA is used. It allows measurements in the range of 32 to 130 dB

and a frequency range of 31.5 to 8000 Hz. The SLM supports frequency weightings A and

C. Its accuracy is

0.5 dB under reference conditions at 94 dB and 1000 Hz in accordance

with Class 2 of IEC 60,942 [

], with a resolution of 0.1 dB. In order to avoid distortions due

to differences in temperature and air pressure, the sound level calibrator PeakTech 8010

by PeakTech Prüf- und Messtechnik GmbH was used to calibrate the SLM. The accuracy of

the calibrator is

0.5 dB under reference conditions at 23

◦

C, 1013 mbar air pressure and

65% humidity.

3.2.3. Speaker and Tone Generator

As a sound source for the experiments, the speaker of the GigaWorks T20 Series II

by Creative connected to a notebook was used. The Online Tone Generator by Tomasz

P. Szynalski [

] was used on the notebook to generate a sine wave (pure tone) on

different frequencies.

3.2.4. Mobile Application for Sound Measurement

In order to mimic the behavior of the TYT app for the experiments, the corresponding

code for the sound measurement was extracted and integrated into a new sound mea-

surement mobile application. In addition, this allows to implement a more convenient

way of extracting the results, as well as more insights into various parameters of the

sound measurement. Equivalent to the TYT app, the sound measurement application

utilizes the previously described

MediaRecorder.getMaxAmplitude()

method to capture

Sensors 2022,22, 170 7 of 17

the “maximum absolute amplitude that was sampled since the last call to this method” [

]

every 500 ms for a total of 30 values (15 s). These values, in turn, are then averaged into a

single value. This averaging step was found to be erroneous in the original application,

as will be discussed in Section 4.2, and has been corrected for the application used in the

experiments. Furthermore, the first two values of the sound measurement have shown to

be erroneous for several smartphone models (see Section 4.2) and are therefore discarded

for the measurements. A screenshot of the sound measurement application is shown in

Figure 1. The user interface of the application allows to start the measurement and displays

the measured single amplitude values as well as the resulting average value after the

measurement is done. As shown in the screenshot, the first two values that are discarded

and excluded from the average are highlighted by displaying them as crossed out in red. In

addition to the features used for the experiments in the scope of this work, the application

allows further configurations for experimental purposes (e.g., the option to change the

audio encoding as well as to remove any audio compression) and offers the possibility to

perform a continuous measurement of the sound level.

Figure 1.

Screenshot of the sound measurement mobile application used for the experiments. The

values displayed represent the individual amplitude values for each of the 500 ms periods as well as

the average amplitude over the entire 15 s measurement period (the large number in the center of

the screen).

Sensors 2022,22, 170 8 of 17

Table 2.

The 30 most commonly used mobile device models, ordered descending by the number of

measurements for each device model in the TYT database. The devices that were selected to perform

the experiments are highlighted in gray. The devices in light gray were initially selected but could

not be used because we were not able to acquire them.

Device ID Device Name Number of Users Number of ∅Measurements

Measurements per User

Moto G * 9 2113 235

Galaxy S5 * 38 1779 47

LGL34C LG Optimus Fuel 1 1548 1548

Galaxy S3 mini * 15 1491 99

SM-G800F Galaxy S5 mini 14 1173 84

XT1032 Motorola Moto G 7 1113 159

Galaxy S3 * 28 1059 38

GT-I8190 Galaxy S3 mini 8 1030 129

SM-G900F Galaxy S5 24 998 42

GT-I9300 Galaxy S3 23 858 37

Galaxy S9 * 24 838 35

SM-A300FU Galaxy A3 2 779 390

HTC One HTC One 9 776 86

GT-I9195 Galaxy S4 mini 10 756 76

Galaxy S4 mini * 11 756 69

SM-A520F Galaxy A5 6 637 106

PRA-LX1 Huawei P8 Lite 1 620 620

WAS-LX1A Huawei P10 Lite 4 614 154

SM-C115 Galaxy K Zoom 1 606 606

Moto G Moto G 1 510 510

SM-T810 Galaxy Tab S2 2 506 253

XT1028 Moto G 1 490 490

Galaxy S4 * 30 464 15

SM-G960F Galaxy S9 16 462 29

LG-P970 LG P970 1 461 461

LT26i Sony Xperia S 3 458 153

Moto G (5) Plus Moto G5 Plus 2 446 223

SGH-M919 SGH-M919 3 444 148

Galaxy S7 * 21 442 21

Galaxy S6 * 30 433 14

* marks device models that summarize multiple device IDs under a common device name (e.g., “Moto G *”

summarizes the device IDs “Moto G”, “XT1028” and “XT1032”). These models can appear both as a single device

model and as part of their group.

Table 3.

Final selection of device models and respective installed Android versions used for

the experiments.

Device Name Device ID Android Version

Moto G XT1032 5.1

Galaxy A3 SM-A310F 7.0

Galaxy S7 SM-G930F 8.1

Moto G 5S Plus Moto G (5S) Plus 8.1

Pixel 2 Pixel 2 10.0

Sensors 2022,22, 170 9 of 17

3.3. Experimental Setup and Procedure

Before conducting the actual experiments, various measurements were taken with

different frequencies (125–2000 Hz), frequency weightings (A & C), distances to the sound

source, and different smartphones to find the optimal settings for the experiments. The

measurements indicate that—using the correct settings—the smartphones measure sound

frequency-independently in the study’s frequency range of 125–2000 Hz, allowing a single

frequency to be used for the experiments. The final settings are shown in Table 4. A pure

tone with a frequency of 1000 Hz was chosen for the sound source to obtain an unweighted

result with the given SLM, since it supports only A- and C-weightings and these frequency

weightings do not apply offsets at 1000 Hz [

]. Note that, for this reason, dB

SPL

, dB(A) and

dB(C) at 1000 Hz are all equal and may therefore be used interchangeably for measurements

at this frequency. For purposes of clarity, dB(C) is used for the remainder of this paper. To

promote and facilitate the overall reproducibility, it was decided against a professional

sound laboratory in favor of a simpler test environment for the experiments. Thus, for the

measurement range, a lower limit of 50 dB(C) was chosen because the background noise in

the test environment was measured at approximately 46 dB(C). 80 dB(C) was chosen as

upper limit to avoid hearing damage for the experimenter (without additional protective

measures). A distance of 30 cm between sound source and SLM/smartphone was chosen

due to spatial restrictions to avoid reflections in the test room.

Table 4. Settings for the experiments.

Parameter Value

Sound source

Frequency 1000 Hz

Sound pressure level 50–80 dB(C)

Increments 5 dB

Distance 30 cm

SLM

Frequency weighting C

Time weighting Fast

Measuring range 32–80

The experimental setup is shown in Figure 2. The experiment is performed in a room

of 15 square meters. The speaker is positioned at the edge of a 76 cm high table to avoid

reflections by the table surface. Furthermore, it is fixated in a way that accounts for its

slightly upward design and results in a vertical positioning of the speaker cone. The SLM

and each of the smartphones are screwed onto tripods and positioned as close as possible

to each other and 30 cm from the speaker, with their microphones pointed at the speaker.

The SLM is thereby rotated 90 degrees so that its display can be read from a distance by the

experimenter. The speaker and the smartphone are controlled remotely with a notebook

that is positioned 2 m away from the table to avoid reflections by the equipment and

the experimenter.

Before conducting the experiments, the SLM is calibrated with the calibrator to ac-

count for the room conditions such as temperature and air pressure. Thereby, the calibrator

is attached to the SLM and turned on, producing a sound at 94 dB and 1000 Hz. The SLM is

then configured to measuring range 50–100 dB, time weighting “Fast” (the measured sam-

ples are averaged every 125 ms) and frequency weighting A. The SLM is then potentially

fine-tuned until the display also shows 94 dB.

Sensors 2022,22, 170 10 of 17

0.3m

Speaker

Table

SLM

Smartphone

Tripods

Remote Control

Experimenter

Figure 2. Setup for the experiments.

The experimental procedure is structured as follows and was repeated for each of the

five smartphones.

The tone generator software is used to create a 1000 Hz sinus signal (pure tone) with

the speaker.

2. The volume is then adjusted until the SLM shows the desired sound pressure level.

Subsequently, the measurement is started on the smartphone. As described in

Section 3.2.4, the mobile application captures 30 measurement values (while dis-

carding the first two values) for about 15 seconds, averages these values and stores

them in a table.

The steps 1.–3. are repeated for 5 dB increments between 50 and 80 dB(C) (an expla-

nation for the measuring range can be found in the first paragraph of this subsection),

resulting in seven values per smartphone.

4. Results

The final experiments resulted in a total of 35 values. The results are shown in

Figure 3. The y-axis shows the reference dB(C) value produced with the tone generator and

the speaker. On the x-axis, the output of the different smartphone models is displayed on a

logarithmic scale. It can be seen that the measured amplitudes of all smartphone models

show an almost linear slope on the logarithmic scaled axis, indicating a nearly logarithmic

slope of the values. Furthermore, it can be observed that the curves of the smartphones

are almost parallel, indicating that the slopes are nearly identical. The only noticeable

deviation is shown by the Pixel 2, where the curve seems to bend at 70 dB(C). Overall, the

curves appear to differ only by an offset on the x-axis.

Sensors 2022,22, 170 11 of 17

Figure 3.

Measured amplitude values for C-weighted sound pressure levels between 50–80 dB(C) for

the different mobile devices used in the experiments. The x-axis is logarithmically scaled.

The results of the experiments are then analyzed in terms of their interpretation. In

this context, first, the experimental results are used for a logarithmic regression to derive

respective equations for the different device models. Second, the legacy application code

of the TYT app is analyzed for relevant implementation errors and poor design decisions

that should be improved. Finally, the derived equations are used to transform the existing

data in the TYT database into (partially) comparable dB(C) values.

4.1. Deriving Equations from the Experimental Results

As can be seen in Figure 3, the curves for each device model have approximately the

same slope. A logarithmic regression analysis was performed to fit a logarithmic function

to the relationship between amplitude values of each device model and the respective

sound level in dB(C) measured with the SLM. The resulting equations are listed in Table 5

and plotted on top of the measured data in Figure 4. As can be seen in the table and the

figure, the regression curves have similar slopes (

1.33), but differ in their intercept.

Only the Samsung Galaxy S7 and A3 models seem to have an almost identical curve,

which suggests that the manufacturer used the same or similar hardware and software

components for the devices. For the other device models, the results indicate that the

Android devices process sound levels equally except for an offset of 0–15 dB. Furthermore,

the slopes of the equations appear to be similar to that of the definition of sound pressure

level (SPL), shown in Equation (1), where

is the root mean square sound pressure and

p0=

Pa = 2

−5

Pa is the reference sound pressure [

]. For sound measurements

by the device models used in the experiments, the equations from Table 5can be used to

transform an amplitude value of the respective device model into a corresponding dB(C)

value. As the slopes of the equations are similar, a simple calibration of any additional

device model in order to determine the respective offset might already be sufficient in order

to obtain approximately comparable measurements.

Lp=10 ·log10p2

p02dB =20 ·log10p

p0dB (1)

Sensors 2022,22, 170 12 of 17

Table 5.

Equations for the different device models as results from the logarithmic regression. R

the coefficient of determination.

Device Model Logarithmic Regression

Model Equation R2

Galaxy S7 y=

20.5379

·log10(x) +

1.3481

0.9995

Galaxy A3 y=

20.7228

·log10(x) +

1.2215

0.9997

Moto G y=21.216 ·log10(x) + 14.258 0.9994

Moto G 5S Plus y=21.341 ·log10(x) + 10.166 0.9998

Pixel 2 y=

23.8364

·log10(x)−

2.7633

0.9925

4.2. Analysis of the Legacy Application Code

As mentioned in Section 3.2.4, the code of the TYT mobile application that is used

to measure the sound level values that are later stored in the database was analyzed and

tested in an isolated environment before the beginning of the experiments. Thereby, several

errors were found in the process used to obtain these values, which are briefly described in

the following:

•

Erroneous calculation 1: The 30 amplitude values sampled by the app as retrieved by

the Android

MediaRecorder

API are averaged arithmetically and stored as a single

value, which is supposed to represent the average sound level. This is erroneous, as

sound levels are logarithmic values, which must be transformed to their energetic

source values before they can be used for calculations [45].

•

Erroneous calculation 2: The first two measured values of the app often contained

errors in the initial experiments. For multiple of the investigated devices, the first

measured value was consistently 0, while the second value was often too low. Further

experiments showed that these errors occur very frequently for measurements within

the first 1000 ms after the start of the recording. These findings indicate that these first

two values should be excluded from the calculations.

•

Unsuitable audio codec: The audio encoder

AMR_NB

[

] is used for the measure-

ments, which is a narrowband audio codec optimized for a frequency range between

200 and 3400 Hz [

]. Lower and higher values may therefore be recorded in a

distorted manner.

•

Lack of user transparency: The app does not indicate that the sound measurement

is ongoing. The user could therefore interact with the mobile device in an unfavor-

able way, which might interfere with the measurement (e.g., microphone is covered,

smartphone collides with object). For example, interacting with the touchscreen of

the mobile device during the measurement resulted in an increase of the measured

sound level by about 10 to 20 dB(C). Placing the device on a table led to values above

100 dB(C).

To estimate the magnitude of the error due to the erroneous calculation, a worst case

was simulated, for which 29 of the 30 measured amplitude values are used as input for the

calculation that are rather small and one value that is rather high. We chose the amplitude

values measured for 50 dB(C) and 80 dB(C) respectively, as these were the lowest and

highest sound level values used in the experiments. The resulting dB value that would be

calculated by the TYT app as well as the correct dB value are shown in Table 6. These values

can be interpreted to mean that the sound level values stored in the TYT database are up

to 9.4–9.8 dB lower than the actual measured loudness. Note that a difference of 10 dB is

perceived as approximately double loudness [

]. Therefore, the measured values in the

TYT database cannot be considered as representative environmental noise measurement

in dB

SPL

(or dB(C), respectively), and thus cannot be used for corresponding conclusions.

However, the values could still be used to compare them relatively (e.g., lower and higher

sound levels) and to investigate correlations with other data (e.g., the perceived tinnitus

loudness of a single user).

Sensors 2022,22, 170 13 of 17

Figure 4.

Fitted regression curves for the measured sound levels of the different device models. The

x-axis is logarithmically scaled, resulting in linear curves.

Table 6.

Worst case errors due to the erroneous calculation of the TYT app for the dB(C) values used

in the experiments.

Device ID Device Name dB(C) Value

TYT App

dB(C) Value

(Correct)

Difference

(Error)

SM-G930F Galaxy S7 55.9 65.3 9.4

SM-A310F Galaxy A3 55.9 65.5 9.6

XT1032 Moto G 55.9 65.6 9.7

Moto G (5) Plus Moto G (5) Plus 55.4 65.2 9.8

4.3. Interpretation of the Existing Data

The equations from Section 4.1 can be used to transform the

soundlevel

data from

the TrackYourTinnitus database (see Section 3.1) for the respective device models into

(partially) comparable sound level dB(C) values (although these values are erroneous, as

shown in Section 4.2). Table 7shows the minimum (min), maximum (max) and average

(avg) dB(C) values for the amplitude values stored for the device models. Note that noise

exposure of 85 dB(A) over a period of 8 hours is considered hazardous [49].

Table 7.

Minimum, maximum, and average recorded environmental noise levels in the TYT database

for the different device models used in the experiments.

Device ID Device Name dB(C) Min dB(C) Max dB(C) Avg

SM-G930F Galaxy S7 31.0 94.7 79.1

SM-A310F Galaxy A3 38.4 93.4 81.1

XT1032 Moto G 40.4 105.4 82.7

Moto G (5) Plus Moto G (5) Plus 52.6 93.9 78.0

5. Discussion

In the following, the results are discussed. On the one hand, considerations towards

comparability of sound measurements with smartphones are discussed. On the other hand,

limitations of the experiments in the scope of this work are considered.

Sensors 2022,22, 170 14 of 17

5.1. Towards Comparability of Sound Measurements with Smartphones

The results have shown that measuring sound levels with mobile devices (e.g., smart-

phones) is possible if the devices are calibrated correctly beforehand. However, there are

several aspects that should be considered. The mobile application used to measure the

sound level should be carefully revised regarding the following aspects:

•

If system-APIs are used, it should be verified whether these APIs provide the correct

values and whether these values are in the desired format. If the recording requires a

setup time, the measurement should only be started after this setup is completed.

• Audio codecs that distort the measurements should not be used.

• Consideration should be given to whether average or peak values are of interest.

•

If sound level averages are calculated, the logarithmic nature of the amplitude values

must be taken into account and the correct formula must be used.

•

The mobile application should transparently indicate via the user interface that the

sound measurement is in progress to avoid the user unintentionally interacting with

the mobile device in a way that interferes with the measurement. The user should be

instructed to act appropriately to minimize the interference.

5.2. Limitations

The experiments performed in the scope of this work are subject to several limitations.

First, the measurements were not performed in a laboratory to foster and facilitate the

overall reproducibility. Therefore, measurement errors, especially due to sound reflections

or background noises (e.g., traffic noise), might have distorted the results. Second, the

measuring distance of 30 cm from the sound source was chosen for spatial reasons. It was

not verified whether a greater distance would lead to more accurate measurement results.

Third, the measurements were limited to levels between 50 and 80 dB(C). Values below or

above these limits cannot be verified. Fourth, the measurements were performed with a

single sinus signal (pure tone) sound source at 1000 Hz. Generalizations for other sound

signals and different frequencies might not be accurate. In addition, pure tones might lead

to room modes and standing waves that could have distorted the results, which was not

considered in the experiments. Fifth, in this context, dB(C) values as measured by the

SLM are treated as dB

SPL

in the experiments, which cannot be generalized for frequencies

other than 1000 Hz. Furthermore, for environmental noise measurements usually the

A-weighting filter is used to better reflect the hearing of the human ear. Sixth, the output of

the mobile application is a peak value and not an effective value as measured by the SLM.

These values should not be compared directly, but were nevertheless used to simulate the

behavior of the TYT app. Seventh, it is assumed that the Android API used to retrieve the

amplitude values behaves the same on each Android version, since the experiments were

performed with the maximum version that was officially supported by the acquired devices

(see Table 3). This assumption is supported by the fact that the API has been present since

Android API level 1 (Android 1.0) [42], but could not be verified.

6. Summary and Outlook

In this work, an experiment was described with the objective to make a large data

set of environmental sound measurements captured with smartphones and stored in the

TrackYourTinnitus (TYT) database usable and comparable to enable meaningful interpre-

tations in the context of tinnitus research. To this end, the existing data were analyzed

to find the device models that contributed the most data entries. Four of these device

models were then acquired for the experiments and equipped with a mobile app that

mimics the environmental sound measurement of the TYT Android app. For the actual

experiments, a sound signal was generated, the volume was adjusted to different sound

levels using a professional calibrated sound level meter (SLM), and the values captured by

the source code of the app on the Android devices were recorded. The results indicate that

the amplitude values retrieved by the devices behave similarly except for a constant offset.

Furthermore, equations derived from the results with a logarithmic regression analysis can

Sensors 2022,22, 170 15 of 17

be used to transform the values in the TYT database to (partially) comparable dB values.

However, there are several limitations to the experiments due to the code of the TYT app

and the experimental setup.

Since the experiments within the scope of this work were only conducted for a num-

ber of selected Android device models, in future work, more device models should be

considered. This includes both Android as well as iOS device models. For the latter, there

are far fewer different models, which are all produced by a single manufacturer, which

simplifies the process. Once the values retrieved by the system APIs of the different device

models and operation system versions are known, respective equations can be derived

and used for any future measurements of the same models. Alternatively, along with

the recommendations in Section 5.1, a calibration feature could be integrated in a future

version of the TYT app that could lead to even more accurate results.

In conclusion, it has been shown that measuring sound levels with mobile devices is

possible and feasible for healthcare purposes, but there are many challenges to ensuring

that the measured values are accurate, comparable, and interpretable and thus more future

work towards the interpretation of mobile crowdsensing data should be conducted.

Author Contributions:

Conceptualization, R.K. and R.P.; methodology, R.K. and R.P.; software,

R.K.; validation, R.K. and R.P.; formal analysis, R.K.; investigation, R.K.; resources, M.R.; data

curation, R.K.; writing—original draft preparation, R.K.; writing—review and editing, R.K. and R.P.;

visualization, R.K.; supervision, R.P.; project administration, M.R. and R.P.; All authors have read

and agreed to the published version of the manuscript.

Funding: This research received no external funding.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement:

The data presented in this study are available on request from the

corresponding author. The data are not publicly available due to privacy reasons.

Conflicts of Interest: The authors declare no conflict of interest.

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