Autonomous noise monitoring system based on digital MEMS microphones: development of a smartphone application for remote communication

Abstract

Noise monitoring is a useful diagnostic tool for making better decisions in noise control projects and understanding the sonic behavior of a place. Traditionally, sound pressure levels are measured using a sound level meter (SLM), usually for a limited time frame. However, recently much interest has arisen in developing wireless sensor networks that work continuously, especially using cost-effective embedded systems. In this sense, digital micro-electrical-mechanical (MEMS) microphones offer great promise in creating such devices due to their low-cost, high-quality, and integrated analog to digital converter. This paper presents a noise monitoring system based upon digital MEMS microphones, Arduino compatible microcontrollers (Teensy and ESP32), and an Android app. The app connects with the system via Bluetooth for configuration and control. Once a measurement is set up and running, the system acts autonomously, saving data into an SD card. By the end of the assessment, it is possible to wirelessly retrieve the saved data and upload it to an online spreadsheet. The device was deployed in an office and measured for one hour, with all data then successfully retrieved. Ultimately, the system could be applied to indoor noise monitoring and used to assess the effectiveness of noise control projects, for example.

Full text

Autonomous noise monitoring system based on digital MEMS
microphones: development of a smartphone application
for remote communication
Felipe Ramos de Mello1
Acoustical Engineering, Federal University of Santa Maria
Av. Roraima nº 1000, Cidade Universitária, Bairro Camobi, 97105-900, Santa Maria, RS, Brazil
William D’Andrea Fonseca2
Acoustical Engineering, Federal University of Santa Maria
Av. Roraima nº 1000, Cidade Universitária, Bairro Camobi, 97105-900, Santa Maria, RS, Brazil
Paulo Henrique Mareze3
Acoustical Engineering, Federal University of Santa Maria
Av. Roraima nº 1000, Cidade Universitária, Bairro Camobi, 97105-900, Santa Maria, RS, Brazil
ABSTRACT
Noise monitoring is a useful diagnostic tool for making better decisions in noise control projects and
understanding the sonic behavior of a place. Traditionally, sound pressure levels are measured using
a sound level meter (SLM), usually for a limited time frame. However, recently much interest has
arisen in developing wireless sensor networks that work continuously, especially using cost-effective
embedded systems. In this sense, digital micro-electrical-mechanical (MEMS) microphones offer great
promise in creating such devices due to their low-cost, high-quality, and integrated analog to digital
converter. This paper presents a noise monitoring system based upon digital MEMS microphones,
Arduino compatible microcontrollers (Teensy and ESP32), and an Android app. The app connects with
the system via Bluetooth for configuration and control. Once a measurement is set up and running, the
system acts autonomously, saving data into an SD card. By the end of the assessment, it is possible to
wirelessly retrieve the saved data and upload it to an online spreadsheet. The device was deployed in
an office and measured for one hour, with all data then successfully retrieved. Ultimately, the system
could be applied to indoor noise monitoring and used to assess the effectiveness of noise control
projects, for example.
Keywords: Smart cities, Internet of Things, Teensy 4.0, Sound Measurement.
PACS: 43.58.Fm, 43.50.Yw, 43.60.Qv, 07.07.Df, 43.38.Kb.
1felipe.mello@eac.ufsm.br.
2will.fonseca@eac.ufsm.br.
3paulo.mareze@eac.ufsm.br.
1

1. INTRODUCTION
As of 2018, about 55% of the world’s population lives in cities and urban centers [
1
]. Therefore, a
higher concentration of people live and work in buildings, circulate in public spaces, and use motor
vehicles for transportation. This, in turn, has led to increased noise levels, which interfere with quality of
life and can cause serious health problems in humans and animals [
2
]. On top of that, noise complaints
have become more frequent, yielding city managers to come up with strategies to regulate and inspect
noise emission [3–5].
In this regard, agencies and public departments have created laws, rules, and guidelines to establish
acceptable community noise levels (e.g., WHO, European Commission, and Conama
1
). In Brazil, for
example, the standard ABNT NBR 10151:2019 establishes procedures for measurement and evaluation
of sound pressure levels (SPL) in inhabited areas (both external and internal), as well as limits for
SPL as a function of land use and occupation [
6
]. The majority of those guidelines use
LAeq
,
Lden
, and
Lnight
measurements as their main evaluation parameters. For instance, WHO’s Environmental Noise
Guidelines for the European Region recommend a yearly average of 70 dB
LAeq,24h
for all leisure noise
sources combined and correlate health outcomes such as cardiovascular diseases, effects on sleep, and
cognitive impairments to Lden and Lnight values [2].
Usually, to evaluate environmental noise, measurements are carried out on demand by an acoustician
or technician using a standardized Sound Level Meter (SLM, according to IEC 61672 [
7
]). This
approach is expensive
2
, time-consuming, and not feasible for high space discretization (that is,
measurements in diverse points). In addition, someone must carry out the evaluations in-person,
which constraints both the assessment period and the number of evaluation points. Furthermore, in
Brazil it is common to use such measurements to calibrate sound maps and environmental noise
simulations.
To overcome the aforementioned situations,much interest has arisen in developing Wireless Acoustic
Sensors Networks (WASN) for noise monitoring, especially using cost-effective devices (see the
researches [8–10]). This field of research considers two types of devices [11]:
1. Those that measure only sound pressure levels; and
2. Those that are capable of source identification and/or separation.
This paper presents a cost-effective prototype for autonomous noise monitoring based on digital
MEMS microphones and readily available electronic components. The device is capable of measuring
Equivalent Continuous Sound Pressure Levels with A, C, and Z frequency-weightings, instantaneous
time-weighted SPL (Fast and Slow), as well as SPL in Fractional Octave Bands (
1/1
and
1/3
), storing
everything into an SD card. Additionally, it has a wireless connection, allowing for communication
with smartphones, for example. Finally, to configure, control, and retrieve data from the device, an
Android application was developed. The app can read files from the device’s SD card (via wireless
communication) and send them to an online spreadsheet for data visualization and further analysis.
The system is intended for continuous indoor noise monitoring (such as in condominiums, buildings,
schools, etc.), enabling communities to closely inspect their noise levels and act accordingly — it can
also be used as a portable SLM. Furthermore, this research aims to contribute to the implementation
of WASNs in Brazil, as the country has no publications or projects in this regard (to the authors’
knowledge). Ultimately, this article is a follow-up to the paper entitled “MEMS digital microphone
and Arduino compatible microcontroller: an embedded system for noise monitoring” presented at
Internoise 2021 [12].
The next sections are organized as follows. Section 2 describes the hardware, software, and algorithm
validations. Section 3 details the components used and their interconnections. Section 4 outlines the
1(Brazilian) National Environment Council.
2Standardized SLMs may cost from $1500 to $20000 dollars, for instance.
2

Android application development and its features. Section 5 shows the results of a test deployment in
which the system measured for one hour and data was successfully retrieved and uploaded. Finally,
Section 6 sums everything up and delineates future work.
2. HARDWARE AND SOFTWARE
This section briefly describes the system’s main hardware and software. Section 2.1 outlines the
MEMS microphone technology and its characteristics. Section 2.2 addresses the microcontrollers
used for processing and data transmission (Teensy 4.0 and ESP32, respectively). Section 2.3 describes
the prototype features. Finally, Section 2.4 outlines previous tests used to validate the implemented
algorithms (see Mello, Fonseca & Mareze [12]).
2.1. Digital MEMS microphones
MEMS microphones are acoustic transducers built into silicon chips using semiconductor technology.
The techniques used allow for the construction of very small and affordable devices that comprise an
acoustic transducer, a signal conditioning circuit, and in digital models, an Analog-to-Digital Converter
(ADC) (see Figure 1). Concerning the microphones’ acoustic characteristics, they usually have a flat
frequency response ranging from 100 Hz up to 10000 Hz
3
, an adequate Signal-to-Noise Ratio (SNR)
for environmental noise evaluation [
11
], and are very robust against climate changes (demonstrating
almost no sensitivity drift over time).
Among the digital models, there are three communication protocols available:
1. Pulse Density Modulation (PDM);
2. Inter-IC Sound (I2S); and
3. Time-Division Multiplex (TDM).
In short, PDM streams 1 bit signals at a very high sampling rate that must be converted to a Pulse
Code Modulation (PCM) format for further processing — which can be very CPU-intensive for most
microcontrollers [
13
]. The I
2
S protocol, in turn, already transmits data in the PCM format, usually
at a sampling rate up to 48 kHz. Both PDM and I
2
S are capable of sending stereo signals through a
single data line. Finally, the TDM protocol is very similar to the I
2
S but is intended for multichannel
streaming of data (up to 16 microphones can share the same data line). For more information regarding
MEMS microphones, please check the references [14–16].
MEMS
TRANSDUCER POWER
OUTPUT
AMPLIFIER
ANALOG MICROPHONE
(a) Analog model.
ADC FILTER
I2S
SERIAL
PORT
HARDWARE
CONTROL
POWER
MANAGEMENT
MEMS
TRANSDUCER
SCK
SD
WS
CHIPEN
L/R
I2S MICROPHONE
(b) Digital I2S model.
Figure 1: Block diagram of microphones. On the left (a), an analog model; and on the right (b), a MEMS I
2
S
digital model.
3The frequency response may vary depending on the model and mounting position.
3

2.2. Teensy 4.0 and ESP32 microcontrollers
The core processing unit of the developed system is the Teensy 4.0 microcontroller (MCU), an
Arduino-compatible, small-sized, and cost-effective MCU (microcontroller unit) [
17
]. Its main features
include an ARM Cortex M7 (running at 600 MHz); a floating-point math unit (64 and 32 bits, FPU); and
compatibility with the I
2
S protocol. Moreover, this MCU comes with an extensive open-source audio
library containing a well-structured audio pipeline suited for multiple simultaneous inputs/outputs and
flexible signal routing.
The Teensy Audio Library is object-oriented [
18
]. Thus, all audio tasks are handled by specific
classes responsible for input reading, streaming, processing, output streaming, etc. Furthermore, being
open-source, it is possible to create new classes that integrate seamlessly into the pipeline. In addition,
the library offers a Graphic User Interface (GUI)
4
that helps users to easily create new audio projects
by interconnecting functional blocks (representing audio objects, see Figure 2 (b)). The interface has a
list of all available classes, a brief documentation of each, and allows the user to export the project as
an Arduino sketch.
ESP32 is a family of low-cost Arduino-compatible microcontrollers with integrated Wi-Fi and
Bluetooth (both classic and low-energy). They have become quite popular among the Internet of Things
(IoT) community for their processing power, features, and ease of use. This project uses a DOIT ESP32
DEVKIT V1 board to add wireless communication capabilities to the system. Figure 2 (a) depicts both
the Teensy 4.0 and ESP32 boards and Table 1 compares popular Arduino compatible boards — for
more information see the references [19–21].
Table 1: Hardware comparison between the prototype used and other Teensy and Arduino boards.
Name Processor CPU Speed Storage Memory Direct memory access (DMA)
Teensy 4.0 NXP iMXRT10621,⋄600 MHz 2 MB (Flash Memory) 1 MB 32 channels
DOIT ESP32 Xtensa LX62,⋄240 MHZ 4 MB (Flash Memory) 520 KB (SRAM) -
Teensy 3.6 NXP MK66FX1M03,⋄180 MHz 1 MB (Flash Memory) 256 KB 32 channels
Arduino Due AT91SAM3X8E 84 MHz 512 KB (Flash Memory) 96 KB (SRAM) 23 channels
Arduino Mega 2560 ATmega2560 16 MHz 256 KB (Flash Memory) 8 KB (SRAM) -
1ARM Cortex-M7. 2Dual-core 3ARM Cortex-M4. ⋄FPU =Floating Point Unit. Additional info can be found at PJRC store,Arduino.cc &Board db.
3
,60 cm
1,80 cm
5,15 cm
2,95 cm
(a) Teensy 4.0 and ESP32. (b) Teensy’s Audio System Design Tool.
Figure 2: On the left (a), Teensy (on top) and ESP32 (on bottom) boards and their dimensions. On the right (b),
Teensy’s audio library user interface.
2.3. Measurement capabilities
For this project, three proprietary Teensy Audio Objects were implemented, each responsible
for tasks such as frequency-weighting, fractional octave band analysis, and sound pressure level
calculations. Specifically:
–
AudioFilterFrequencyWeighting: this object is responsible for applying the frequency
weighting via Infinite Impulse Response (IIR) filters. It has options for A and C frequency
weighting curves.
4Audio System Design Tool, available at https://www.pjrc.com/teensy/gui/.
4

–
AudioAnalyzeOctaveBands: this object is responsible for the fractional octave band analysis. It
uses a band-pass IIR filter bank to evaluate equivalent continuous sound pressure levels in
1/1
and
1/3octave bands. Filters were implemented according to the standard IEC 61260:1-2014 [22].
–
AudioAnalyzeSPL: this object is responsible for SPL calculations. It returns
Leq
, time-weighted
SPL (Fast and Slow), maximum time-weighted SPL, and peak SPL.
All objects receive a stream of 128 audio samples (Teensy’s default) for computations. The
frequency-weighting object streams an output of 128 filtered audio samples, while both
AudioAnalyzeOctaveBands and AudioAnalyzeSPL returns calculated values when available (see
Figure 7 for a detailed data flow). The user can define the log interval (both the rate at which SPL data
is returned, as well as the time period for
Leq
integration) and which fractional octave band is used for
calculations. The firmware is programmed to return SPL values in all frequency weightings (A, C, and
Z). Moreover, fractional octave band analysis does not support frequency-weighting (however, it can
be added in post-processing).
2.4. Algorithm validations
Finally, as described in [
12
], a series of tests were held to evaluate the implemented filters’ responses
using the appropriate standards, particularly:
1. IEC 61672:2013 (Parts 1 and 2) for the time- and frequency-weighting filters [7, 23]; and
2.
ANSI/ASA S1.11-2014 (Part 1), IEC 61260:1-2014, and IEC 61260:2-2016 for the fractional
octave band filters [22, 24, 25].
In short, all filters comply with Class 1 standards. Some of the results are shown in Figures 3 (a) and
3 (b). For more details regarding the methodology, performance tests, and results, please check Mello,
Fonseca & Mareze [12].
101102103104
Frequency [Hz]
-80
-60
-40
-20
0
Normalized level
[dB ref. 1 @ 1 kHz]
A-weighting filter validation according to
IEC 61672-1 Class 1 acceptance limits
Teensy
Class 1 Mask
(a) A frequency-weighting validation.
850 875 900 925 950 975 1000 1025 1050 1075 1100 1125 1150
Frequency [Hz]
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
Normalized level [dB ref. 1]
1/3 Octave-band tests (30 runs): mean and confidence interval for 99.73%
1 kHz
Class 1 mask
Class 2 mask
(b) 1⁄3-octave band: 1 kHz.
Figure 3: On the left (a), frequency-weighting filter for A curve and Class 1 acceptance limits. On the right (b),
1 kHz
1⁄3
-octave band tests with 30 runs: mean and confidence intervals (shaded area) for 99.73%, along with
Class 1 & 2 acceptance limits — complete results can be verified in [12].
3. THE PROTOTYPE
This section describes the prototype components and data flow. Section 3.1 lists and describes the
hardware and its interconnections; Section 3.2 addresses the device’s frequency response correction;
and finally, Section 3.3 summarizes the data flow inside the system.
3.1. Components
To capture sound, the developed prototype uses the Sipeed MSM261S4030H0 breakout board,
which contains an I
2
S digital microphone. This board was chosen mainly due to its availability in
the Brazilian market. Some of its specifications are shown in Table 2. As stated in Section 2.2, two
5

microcontrollers handle the calculations and data transmission (Teensy 4.0 and ESP32, respectively).
Both are powered with 5 V using a breadboard power supply, which can be supplied by a simple
6.5 V
∼
12 V power supply (connected to a wall socket) or a portable power bank. An Arduino SD
Card module and a 2 GB card are used for data storage. Finally, Figure 4 contains pictures of (a) the
components inside the box and (b) the assembled prototype.
Table 2: MEMS microphone Sipeed MSM261S4030H0 technical specifications [26].
Parameter Limits /Data
Min. Nominal Max. Unit Condition
Directivity Omnidirectional — —
Sensitivity -27 -26 -25 dB dBFS @ 1 kHz 1 Pa
Operation voltage 1.6 – 3.6 V —
Frequency range 100 – 10k Hz —
Signal-to-noise ratio — 57 — dB 20 kHz bandwidth, A-weighted
Total Harmonic Distortion — — 1 % 100 dB SPL @1 kHz, S =nom., Rload >2k
Acoustic Overload Point — 124 — dB SPL 10% THD @1 kHz, S =nom., Rload >2k
Maximum SPL 140 dB SPL —
Figure 4: Fully assembled prototype (with its housing).
3.2. Frequency response correction
To guarantee a fair reading across the spectrum, the prototype frequency response was measured
through a comparison method in an anechoic chamber (above 100 Hz, using an exponential sine sweep)
and using a pressure cavity (below 100 Hz, using a stepped sine sequence)
5
— see Figure 5. Both
measurements used a Brüel & Kjær Type 4189 measurement microphone as reference [
28
]. Further
details regarding the methodology, procedure, and results will be published in the near future.
From the measured frequency response, a correction curve was created (observe Figure 6 (a)). That
is, a 513 taps Finite Impulse Response (FIR) filter was generated in Matlab, and its coefficients were
imported into Teensy 4.0. For the filter implementation, Brian Millier’s convolution audio object was
used. The object uses the fast convolution (via FFT) overlap-add algorithm — more information can
be read in Millier [29]. The correction filter’s frequency response is shown in Figure 6 (b).
3.3. Data flow
Figure 7 illustrates how signal-data circulates in the system. First, the MEMS microphone captures
the sound and sends it to the Teensy 4.0 board via I
2
S. Inside Teensy, the signal frequency content is
adjusted via the FIR frequency correction filter. Next, the signal is routed to two paths. On one side,
it passes through the frequency-weighting filters (A, C, and Z), and then the SPL is calculated. On
the other side, the signal goes through the fractional octave filter bank, and the
Leq
of each band is
measured. Finally, all SPL data is saved into the SD card, and the broadband values are also sent to
ESP32, that in turn, sends the user-specified values to the smartphone app via Bluetooth Low-Energy
(BLE) — see details in Section 4.1.
5For more details regarding the pressure cavity used, see Wunderlich et al. [27].
6

(a) Anechoic chamber measurement setup. (b) Pressure cavity measurement setup.
Figure 5: Measurement setups for the prototype frequency response.
20 100 1 k 10 k 20 k
Frequency [Hz]
-20
-10
0
10
20
30
Level [dB ref. 1 @ 1 kHz]
Sipeed MSM261S4030H0 mounted in the prototype
measured frequency response
(a) Measured frequency response (smoothed using
1⁄8
octave bands).
20 100 1 k 10 k 20 k
Frequency [Hz]
-25
-20
-15
-10
-5
0
5
10
Level [dB ref. 1 @ 1 kHz]
Prototype frequency correction filter
(513 taps FIR filter)
(b) Frequency correction filter.
Figure 6: Prototype’s frequency response and implemented digital correction filter.
SD card
Sound
MEMS
microphone
Teensy 4.0
PCM/I2S
audio
ADC
Z, A or C
freq.-weighting
octave filters
1/1 or 1/3 oct
SPL calculations inside Teensy
time-weighting
Fast or Slow
Leq
Level
RMS, Max or Peak
Z-1 Z-1 Z-1
ΣΣΣ
h0h1h2hn
y[n]
x[n]
freq. resp.
correction
SPL data
Control messages
Audio streaming
ESP32
Smartphone app
via BLE
Figure 7: Prototype signal-data flow diagram.
4. SMARTPHONE APPLICATION
In order to control, configure, and retrieve data from the noise monitoring device, an Android
application was developed using the MIT App Inventor platform [
30
]. This is a web-based tool that
allows the user to easily design and deploy Android apps through a block-based approach, in which
each block relates to a specific function or set of instructions
6
(see Figure 8). The app comprises three
main screens, responsible for the following:
1.
Screen 1: real-time data visualization and a button to initiate a new measurement (when
connected to the noise monitoring device);
6For more information, check http://ai2.appinventor.mit.edu.
7

2. Screen 2: measurement and system configuration; and
3. Screen 3: data retrieval from the SD card and upload to an online spreadsheet.
A Bluetooth connection is used for communication between the noise monitoring device and the
cellphone app. As described in Section 2.2, the ESP32 board is responsible for establishing the
Bluetooth server and acts as a mediator linking the smartphone with Teensy 4.0, where the Sound
Pressure Level program is running. The following sections show in detail each screen feature
(Section 4.1) and the spreadsheet used to save and visualize the retrieved data (Section 4.2).
Figure 8: MIT App Inventor interface and program example.
4.1. App features
Features of Screen 1, see Figure 9.
1.
Connection and connection status buttons: here the
user can connect to the system via Bluetooth Low-Energy
connection (BLE).
2.
SPL display: allows the user to monitor a few SPL
parameters (chosen on Screen 2). Values shown here are
not saved in the smartphone and are just for visualization.
3.
Measurement setup: shows the configurations set up by the
user, as well as a “time-elapsed” display.
4.
Control buttons: allows the user to initiate a measurement,
access the configuration screen (Screen 2), or access the file
retriever screen (Screen 3).
1
2
3
4
Figure 9: Screen 1, control and real-
time visualization.
8

1
2
3
4
5
Figure 10: Screen 2, system
configuration.
Features of Screen 2, see Figure 10.
1.
Set log interval: adjusts the rate at which the SLM algorithm
returns data. This parameter is also used as the integration
period for Leq calculations.
2.
Set display: sets which SPL parameters will be shown on
Screen 1. Here, the user can choose among one of the three
frequency-weightings (A, C, or Z) for the
Leq
, SPL (
Lp
), and
peak SPL (Lpeak) individually.
3.
Measurement name: allows the user to define a
measurement name. If no name is inserted, the default is
MSnum
(“num” being the number of measurements on the
SD card plus one).
4.
Set measurement duration: how much time the system
should run.
5.
Set fractional octave bands: sets which fractional octave
band is going to be used for calculations (1⁄1or 1⁄3).
Features of Screen 3, see Figure 11.
1.
Connection and connection status buttons: here the
user can connect to the system via Bluetooth Low-Energy
connection (BLE).
2.
Files to retrieve: lists the files saved into the prototype’s SD
card and allows the user to select which one to retrieve.
3. Retrieve button: starts the file retrieving process.
4.
Upload: upload the retrieved file to a Google Sheets
spreadsheet. This is carried out by the app (using the
smartphone Wi-Fi or cellphone mobile connection).
1
2
3
4
Figure 11: Screen 3: file retriever
and uploader.
4.2. Online spreadsheet
A special spreadsheet was created in Google Sheets to receive data from the Android Application.
In order to do this, a simple script was added via the Apps Scripts extension, following the instructions
present in Metricrat AI2 [
31
] — see Code 1. When deploying the script, an URL ID is created, which
is used by the Android app to access the spreadsheet (observe Figure 12 (d)).
9

Code 1: Script used in the Google Sheets spreadsheet to enable remote data upload.
 
function doPost(e) {
var data = JSON.parse(e.postData.contents); // or >> eval(e.postData.contents) ;
var ss = SpreadsheetApp.getActive();
var sh = ss.getSheetByName(’retrievedData’);
for (var i=0;i<data.length;i++) {
sh.appendRow(data[i]);
}
return ContentService.createTextOutput("Success") ;
}
 
5. DEPLOYMENT
A test deployment was carried out in an office to verify the system’s behavior. The prototype was
set to measure for the duration of one hour, returning values every one second, saving SPL and
Leq
values with A, C, and Z frequency-weightings and Fast time-weighting (SPL only), as well as
Leq
in
1⁄3
-octave bands. By the end of the measurement, data was successfully retrieved from the SD card via
the Android app and properly sent to the online spreadsheet. Measured results are shown in Figure 12.
(a) App feedback.
0 5 10 15 20 25 30 35 40 45 50 55 60
Time [min]
30
40
50
60
70
80
SPL [dB ref. 20 µPa]
Deployment test A-weighted noise levels
53.5 dB 53.5 dB 52.2 dB 52.4 dB 52.8 dB 52.8 dB
LAF LAeq,10min LAFmax = 79.4 dB LAeq = 53.3 dB
(b) Broadband A-weighted noise levels.
Deployment test 1/3-octave bands LZeq
16 31.5 63 125 250 500 1k 2k 4k 8k 16k
Nominal frequency bands [Hz]
0
10
20
30
40
50
60
SPL [dB ref. 20 µPa]
(c) 1⁄3-octave bands noise levels.
(d) Online spreadsheet results (fractional octave band values are further on the right).
Figure 12: Deployment test of the prototype and measured levels.
6. FINAL REMARKS AND FUTURE WORK
This project was the first step towards an autonomous noise monitoring/sound pressure level
measurement system with wireless setup and control via cellphone app — a Prototype 2, as a follow
up to the previous work presented at Internoise 2021 [
12
], in which all algorithms, filters, and acoustic
performance were evaluated and validated. The ease of operation and access to the online spreadsheet
made it much easier for the user to have improved access — it is under testing at the Acoustical
Engineering Program at the Federal University of Santa Maria, Brazil. The system is working as
expected, and the hardware offers a very promising (and cost-effective) performance. However, for the
future, improvements are already under development, specifically:
10

1.
Design of a more rugged and reliable data transmission setup, Wi-Fi connection is expected for
the next prototype;
2.
Design of a more robust smartphone application, using professional tools such as Android Studio;
3. Implementation of online and real-time data transmission into a web server;
4. Design of an efficient and redundant power supply setup (with battery monitoring);
5.
Prototypes with screens, either simple two-color small ones or wider with touch screen
capabilities; and
6. Real-life deployment (for example, in the city, university and/or farm).
ACKNOWLEDGMENTS
The authors want to thank all the support from the Acoustical Engineering Program at the Federal
University of Santa Maria (UFSM, Brazil), as well as its scholarship programs FIPE and FIEX, which
assisted this project. A special thanks to Sergio Aguirre for the support, Joe Lacey for the insightful
comments on this text, and all developers at the PJRC forum for helping build such a high-quality
open-source audio library.
REFERENCES
[1]
United Nations. 68% of the world population projected to live in urban areas by 2050, says UN.
Online, New York, May 2018. URL https://www.un.org/development/desa/en/news/population/201
8-revision-of-world-urbanization-prospects.html. Access on April of 2022.
[2]
World Health Organization. Environmental Noise Guidelines for the European Region. WHO
Regional Office for Europe, Denmark, 2018. ISBN 978-9289053563. URL https://www.euro.who
.int/__data/assets/pdf_file/0008/383921/noise-guidelines-eng.pdf.
[3]
L. Cardoso. The Politics of Noise Control in São Paulo. Journal of Latin American Studies, 49(4):
917–945, dec 2016. ISSN 0022-216X, 1469-767X. doi: 10.1017/s0022216x16001978.
[4]
D. MacCutcheon. Negative responses to urban residential noise as a social rebound effect of
increasing population density: Legislative challenges and auditory territoriality. Noise &health,
23:35–41, 2021. ISSN 1998-4030. doi: 10.4103/nah.NAH_45_18.
[5]
H. Tong and J. Kang. Relationship between urban development patterns and noise complaints in
England. Environment and Planning B: Urban Analytics and City Science, 48(6):1632–1649, jun
2020. ISSN 2399-8091. doi: 10.1177/2399808320930247.
[6]
Brazilian Association of Technical Standards (Associação Brasileira de Normas Técnicas).
Acoustics – measurement and evaluation of sound pressure levels in inhabited areas – general
purpose application (original: Acústica – Medição e avaliação de níveis de pressão snora em áreas
habitadas – Aplicação de uso geral. Brazilian Standard ABNT NBR 10151:2019 (link), pages
1–25, 2019–2020.
[7]
International Electrotechnical Commission (IEC). Electroacoustics – Sound level meters – Part 1:
Specifications. Standard IEC 61672-1:2013, pages 1–103, 2013. International standard.
[8]
J. C. Farrés. Barcelona noise monitoring network. In EuroNoise 2015, pages 2315–2320, Maastricht,
NL, May 2015.
[9]
E. Vidaña-Vila, J. Navarro, C. Borda-Fortuny, D. Stowell, and R. M. Alsina-Pagès. Low-cost
distributed acoustic sensor network for real-time urban sound monitoring. Electronics, 9(12):2119,
1–25, 2020. ISSN 2079-9292. doi: 10.3390/electronics9122119.
[10]
S. Santini and A. Vitaletti. Wireless Sensor Networks for Environmental Noise Monitoring. In 6th
GI/ITG KuVS Fachgespraech Drahtlose Sensornetze, pages 98–101, Aachen, Germany, Jul. 2007.
[11]
R. M. Alsina-Pagès, M. Hervás, L. Duboc, and J. Carbassa. Design of a Low-Cost Configurable
Acoustic Sensor for the Rapid Development of Sound Recognition Applications. Electronics, 9(7):
1155, 1–20, jul 2020. ISSN 2079-9292. doi: 10.3390/electronics9071155.
11

[12]
F. R. Mello, W. D’A. Fonseca, and P. H. Mareze. MEMS digital microphone and arduino compatible
microcontroller: an embedded system for noise monitoring. In Internoise 2021 – 50th International
Congress and Exposition on Noise Control Engineering, pages 3921–3932, Washington, DC, USA,
2021. International Institute of Noise Control Engineering (I-INCE). doi: 10.3397/IN-2021-2557.
URL https://bit.ly/MEMS-int2021.
[13]
P. von Pflug and D. Krischker. Aspects of the use of MEMS microphones in phased array systems.
In 46th International Congress and Exposition on Noise Control Engineering – Internoise 2017,
pages 5983–5993, Hong Kong, China, Aug. 2017. URL https://bit.ly/int2017-mems.
[14]
M. Suvanto. The MEMS microphone book. MOSOMIC, Kangasala, Finland, 1 edition, 2022. ISBN
978-9529456604. URL http://www.mosomic.com/thememsmicrophonebook/.
[15]
P. Malcovati and A. Baschirotto. The evolution of integrated interfaces for MEMS microphones.
Micromachines, 9(7):1–20, n. 323, 2018. ISSN 2072-666X. doi: 10.3390/mi9070323.
[16]
J. Lewis. Analog and Digital MEMS Microphone Design Considerations, 2013. Analog Devices,
Technical Article (MS-2472).
[17]
PJRC – P. J. Stoffregen. Teensy 4.0 Development Board, 2021. URL https://www.pjrc.com/store/t
eensy40.html. Official Website.
[18]
W3Schools. C++ OOP (Object-Oriented Programming). Online, 2022. URL https://www.w3scho
ols.com/cpp/cpp_oop.asp. Access on April 2022.
[19]
PJRC – P. J. Stoffregen. Teensy 4.0 Development Board,Teensy Audio Library,Audio System
Design Tool,&Creating New Audio Objects, 2021. URL https://www.pjrc.com.
[20]
Yichone. ESP8266 ESP32. Online, Shenzhen Doctors of Intelligence & Technology Co. Ltd, 2017.
URL https://github.com/SmartArduino/SZDOITWiKi/wiki/ESP8266---ESP32.
[21]
F. R. Mello, W. D’A. Fonseca, and P. H. Mareze. Acquisition of acoustic signals using digital
MEMS microphones via Teensy embedded platform (Arduino compatible) (origial: Aquisição de
sinais acústicos utilizando microfones MEMS digitais e plataforma embarcada Teensy (compatível
com Arduino)). In 12th Iberoamerican Congress of Acoustics (FIA 2020/22) &XXIX Meeting of
the Brazilian Society of Acoustics – Sobrac, pages 1–12, Florianópolis, SC, Brazil, August 2022.
URL https://bit.ly/fia2022-mems.
[22]
International Electrotechnical Commission (IEC). Electroacoustics – Octave-band and fractional-
octave-band filters – Part 1: Specifications. Standard IEC 61260-1:2014, pages 1–88, 2014.
International standard.
[23]
International Electrotechnical Commission (IEC). Electroacoustics – Sound level meters – Part 2:
Pattern evaluation tests. Standard IEC 61672-2:2013, pages 1–80, 2013. International standard.
[24]
American National Standards Institute/Acoustical Society of America (ANSI/ASA). Specification
for octave-band and fractional-octave-band analog and digital filters. Standard S1-11:2004 (R2009),
2009. International standard.
[25]
International Electrotechnical Commission (IEC). Electroacoustics – Octave-band and fractional-
octave-band filters – Part 2: Pattern-evaluation tests. Standard IEC 61260-2:2016, pages 1–49,
2016. International standard.
[26]
MEMSensing Microsystems. MSM261S4030H0 – I
2
S digital output mems mic. with multi modes
(datasheet), v 1.7, 2018.
[27]
P. S. Wunderlich, W. D’A. Fonseca, E. Brandão, and P. H. Mareze. Development of a relative
response calibrator for electroacoustic transducers (original: Desenvolvimento de um calibrador de
resposta relativa para transdutores eletroacústicos). In XXVIII Meeting of the Brazilian Society
of Acoustics – Sobrac, pages 1–10, Porto Alegre, RS, Brazil, October 2018. Brazilian Society of
Acoustics (Sobrac), Galoá. doi: 10.17648/sobrac-87174.
[28]
Bruel & Kjær (HBK). Type 4189 – ½" Prepolarized free-field microphone. Online, 2022. URL
https://www.bksv.com/en/transducers/acoustic/microphones/microphone-cartridges/4189. Access
on April 2022.
[29]
B. Millier. Fancy Filtering with the Teensy 3.6. Circuit Cellar Magazine, (346), 2019. URL
https://circuitcellar.com/research-design-hub/fancy-filtering-with-the-teensy-3-6/.
[30]
Massachusetts Institute of Technology. MIT App Inventor. Online, 2022. URL https://appinventor.
mit.edu/. Access on April 2022.
[31]
Metric RAT AI2. Export CSV data (multiple data rows) to Google Sheet. Online, 2022. URL
https://ai2.metricrat.co.uk/guides/export-csv-data-multiple-data-to-google-sheet.
12