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

Felipe Ramos de Mello 1

Acoustical Engineering, Federal University of Santa Maria Av. Roraima nº 1000, Cidade Universitária, Bairro Camobi, 97105-900, Santa Maria, RS, Brazil

William D’Andrea Fonseca 2

Acoustical Engineering, Federal University of Santa Maria Av. Roraima nº 1000, Cidade Universitária, Bairro Camobi, 97105-900, Santa Maria, RS, Brazil

Paulo Henrique Mareze 3

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-e ff ective embedded systems. In this sense, digital micro-electrical-mechanical (MEMS) microphones are a 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 o ffi ce and measured for one hour, with all data being successfully retrieved. Ultimately, the system could be applied to indoor noise monitoring and used to assess the e ff ectiveness of noise control projects, for example.

Keywords: Smart cities, Internet of Things, Teensy 4.0.

1 felipe.mello@eac.ufsm.br .

2 will.fonseca@eac.ufsm.br .

3 paulo.mareze@eac.ufsm.br .

21-24 auGust SCOTTISH EVENT CAMPUS, ‘GLASGOW

1. INTRODUCTION

In modern days, a great percentage of the population lives in cities and urban centers [ 1 ]. Therefore, a higher concentration of people are living and working in buildings, circulating in public spaces, and using motor vehicles for transportation. This, in turn, leads to an increase in noise levels, which interferes with life quality and can cause serious health problems in humans and animals [ 2 ]. On top of that, noise complaints are becoming 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, Conama). In Brazil, for example, the standard ABNT NBR 10151:2019 establishes procedures for measurement and evaluation of sound pressure levels (SPL) on 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 L Aeq , L den , and L night measurements as their main evaluation parameters. For instance, WHO’s Environmental Noise Guidelines for the European Region recommends a yearly average of 70 dB L Aeq,24h for all leisure noise sources combined and correlates health outcomes such as cardiovascular diseases, e ff ects on sleep, and cognitive impairments to L den , and L night 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 1 , time-consuming, and not feasible for high space discretization. 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 is common to use those kinds of measurements to calibrate sound maps and environmental noise simulations. To overcome the aforementioned disadvantages, much interest has arisen in developing Wireless Acoustic Sensors Networks (or WASN) for noise monitoring, especially using low-cost devices (see these works [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-e ff ective 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 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 and 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 published work or projects in this regard (at least to the authors’ knowledge). Ultimately, this 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 used, software, and algorithm validations. Section 3 detail the used components and their interconnections. Section 4 outlines the Android application development and its features. Section 5 shows the result of a test

1 Standardized SLMs can cost from $1500 to $20000.

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 works.

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. Ultimately, Section 2.4 outlines previous tests used to validate the implemented algorithms (see [12]).

2.1. Digital MEMS microphones MEMS microphones are acoustic transducers built into silicon chips using semiconductor technology. Those techniques allow for the construction of really small and a ff ordable 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 80 Hz up to 10 kHz 2 , an adequate Signal-to-Noise Ratio for environmental noise evaluation [ 11 ], and are very robust against climate changes (having 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 (I 2 S); 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 [14–16].

I2S MICROPHONE

ANALOG MICROPHONE

MEMS TRANSDUCER

ADC FILTER

SCK

OUTPUT AMPLIFIER

I2S SERIAL PORT HARDWARE CONTROL POWER MANAGEMENT

SD

WS

MEMS TRANSDUCER POWER

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CHIPEN

(b) Digital I 2 S model.

(a) Analog model.

Figure 1: Block diagram of MEMS microphones. On the left, an analog model. On the right, an I 2 S digital model.

2 May vary depending on the model and mounting position.

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-e ff ective MCU. 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 input / output and flexible signal routing. The Teensy Audio Library is object-oriented. That way, all audio tasks are handled by specific objects responsible for input reading, streaming, processing, output streaming, etc. Furthermore, being open-source, it is possible to create new objects that integrate seamlessly into the pipeline. In addition, the library o ff ers a Graphic User Interface (GUI) 3 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 objects, 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 [17, 18].

input output mixer play record synth effect filter analyze control info Type AudioOutputl2s ID i2s2 Properties name i2s2 Summary Transmit 16 bit stereo audio to the audio shield or another 12S device, using I2S master mode.

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 iMXRT1062 1 , ⋄ 600 MHz 2 MB (Flash Memory) 1 MB 32 channels DOIT ESP32 Xtensa LX6 2 , ⋄ 240 MHZ 4 MB (Flash Memory) 520 KB (SRAM) - Teensy 3.6 NXP MK66FX1M0 3 , ⋄ 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) -

1 ARM Cortex-M7. 2 Dual-core 3 ARM Cortex-M4. ⋄ FPU = Floating Point Unit. Additional info can be found at PJRC store , Arduino.cc , Board db , and .

PDF

1,8 cm

3,6 cm

2,95 cm

8 OS8ees e.@ ND 013,012 014 027026 SeeRE 25033 i : FS] a S. Pd CSSSCORSCLLOES

5,15 cm

(a) Teensy 4.0 and ESP32.

(b) Teensy’s Audio System Design Tool.

Figure 2: On the left, Teensy (on top) and ESP32 (on bottom) boards dimensions. On the right, a print of 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:

3 Audio System Design Tool, available at https: // www.pjrc.com / teensy / gui / .

– AudioFilterFrequencyWeighting: this object is responsible for applying the frequency weighting via Infinite Impulse Response (IIR) filters. It has options for A and C weighting curves.

– 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 / 3 octave bands. Filters were implemented according to IEC 61260:1-2014 [19].

– AudioAnalyzeSPL: this object is responsible for SPL calculations. It returns L eq , time-weighted SPL (Fast or 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 L eq integration) and which fractional octave band uses for calculations. The firmware is programmed to return SPL values in all frequency weightings (A, C, and Z). Moreover, fractional octave band analysis doesn’t 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, 20]; and

2. ANSI / ASA S1.11-2014 (part 1), IEC 61260:1-2014, and IEC 61260:2-2016 for the fractional octave band filters [19, 21, 22].

In short, all filters comply with Class 1 standards. Some of the results are shown in Figures 3 (a) and (b). For more details regarding the methodology, performance tests, and results, please check [ 12 ].

A-weighting filter validation according to IEC 61672-1 Class 1 acceptance limits

1/3 Octave-band tests (30 runs): mean and confidence interval for 99.73%

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850 875 900 925 950 975 1000 1025 1050 1075 1100 1125 1150 Frequency [Hz]

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(a) A frequency-weighting validation.

(b) 1 ⁄ 3 -octave bands: 1 kHz.

Figure 3: On the right, A frequency-weighting filter and Class 1 acceptance limits. On the left, 1 kHz 1 ⁄ 3 -octave bands test with 30 runs: mean and confidence intervals (shaded area) for 99.73%. 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. 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 microcontrollers handle the calculations and data transmission (Teensy 4.0 and ESP32, respectively). Both are powered with 5V using a breadboard power supply, which can be supplied by a simple 6.5V ˜ 12V 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 [23].

Parameter Limits / Data Min. Nom. 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 —

(a) Components inside the prototype box.

(b) Fully assembled prototype.

Figure 4: Pictures of the prototype.

3.2. Frequency response correction To guarantee a fair reading across the spectrum, the prototype frequency response was measured through a comparison method on an anechoic chamber (above 100 Hz, using an exponential sine sweep) and a pressure cavity (below 100 Hz, using a stepped sine sequence) 4 — see Figure 5. Both measurements used a Bruel & Kjær Type 4189 measurement microphone as reference (cite). More details regarding the methodology, procedure, and results are going to be published in future work. From the measured frequency response, a correction curve was created (Figure 6 (a)). From this curve, a 513 taps Finite Impulse Response (FIR) filter was generated in Matlab, and its coe ffi cients 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 [25]. The correction filter’s frequency response is shown in Figure 6 (b).

4 For more details regarding the pressure cavity, see [24].

(a) Anechoic chamber measurement setup.

(b) Pressure cavity measurement setup.

Figure 5: Measurement setups for the prototype frequency response.

Sipeed MSM261S4030H0 mounted in the prototype

Prototype frequency correction filter

measured frequency response

(513 taps FIR filter)

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20 100 1 k 10 k 20 k Frequency [Hz]

(a) Prototype’s measured frequency response (smoothed in 1 ⁄ 8 octave bands).

(b) Prototype’s frequency correction filter.

Figure 6: Prototype’s frequency response and implemented correction filter.

3.3. Data flow Figure 7 illustrates how 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 frequency correction FIR 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, the signal goes through the fractional octave filter bank, and the L eq 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 (more details on Section 4.1).

Smartphone app

SPL calculations inside Teensy

freq.-weighting

Sound

Z, A or C

MEMS microphone

freq. resp. correction

SD card

time-weighting

ADC

Level

PCM/I2S audio

x [ n ]

Z -1 Z -1 Z -1

h 0 h 1 h 2 h n

Fast or Slow

y [ n ]

Σ Σ Σ

RMS, Max or Peak

via BLE

Leq

Teensy 4.0

ESP32

1/1 or 1/3 oct

octave filters

Audio streaming

SPL data Control messages

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Figure 7: 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. 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 5 (see Figure 8). The app comprises three main screens, responsible for the following:

1. Screen 1: real-time data visualization and a button to start a new measurement (when connected to the noise monitoring device);

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 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).

LAeq: 52.64 LAp: 53.85 LCpeak: 69.41 Fractional Octave Bands: 1/3 MS Duration [min]: 1 _Log interval [s]: 1.000 Time elapsed: 00:00:08 ‘START MEASUREMENT SLM Configuration Retrieve Measurements

Figure 8: MIT App Inventor interface and program example.

5 For more information, check http: // ai2.appinventor.mit.edu /

4.1. App features

1. Connection and connection status buttons: here the user can connect to the system via Bluetooth Low-Energy connection (BLE).

1

2

2. SPL display: allows the user to monitor a few SPL parameters (chosen in Screen 2). Values showed 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.

3

4. Control buttons: allows the user to start a measurement, access the configuration screen (Screen 2), or access the file retriever screen (Screen 3).

4

Figure 9: Screen 1, control and real- time visualization .

1. Set log interval: adjusts the rate at which the SLM algorithm returns data. This parameter is also used as the integration period for L eq calculations.

LAeq: 52.64 LAp: 53.85 LCpeak: 69.41 Fractional Octave Bands: 1/3 MS Duration [min]: 1 _Log interval [s]: 1.000 Time elapsed: 00:00:08 ‘START MEASUREMENT SLM Configuration Retrieve Measurements

1

2. Set display: sets which SPL parameters are going to be shown on Screen 1. Here, the user can choose between one of the three frequency-weightings (A, C, or Z) for the L eq , SPL ( L p ), and peak SPL ( L peak ) individually.

2

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 plus one).

3

4

5

4. Set measurement duration: how much time the system should run.

5. Set fractional octave bands: sets which fractional octave band is going to used for calculations ( 1 ⁄ 1 or 1 ⁄ 3 ).

Figure 10: Screen 2: system configuration .

08:24 + v.40 SET LOG INTERVAL 1.000 ¥ SET DISPLAY Leq A ¥ Lp A ¥ Lpeak c v SET MEASUREMENT NAME measurement ‘SET MEASUREMENT DURATION IN MINUTES. A Set Fractional Octave Bands 130

1. Connection and connection status buttons: here the user can connect to the system via Bluetooth Low-Energy connection (BLE).

1

2

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

4

3. Retrieve button: starts the file retrieving process.

4. Upload: upload the retrieved file to a Google Sheets spreadsheet.

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 [ 26 ] — see Code 1. When deploying the script, an URL ID is created, which is used by the Android app to access the spreadsheet.

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

sh.appendRow(data[i]);

}

return ContentService.createTextOutput( "Success" ) ; }

5. DEPLOYMENT

A test deployment was held in an o ffi ce to verify the system’s behavior. The prototype was set to measure throughout an hour, returning values every one second, saving SPL and L eq values with A, C, and Z frequency-weightings and Fast time-weighting (SPL only), as well as L eq 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.

08:26 4 File retriever]and uploader Files on the SD card MS1.csv v Upload File to Google Sheets Back to SLM. About: EAC MEMS Sound Level Meter (EAC MSLM) is a project developed at the Acoustical Engineering Program of the Federal University of Santa Maria, in Brazil, as part of Felipe Ramos de Mell's Bachelor's Thesis (advised by professor William D’Andrea Fonseca) @ s—w-EAC E ACOUSTICAL ENGINEERING |UFSM. whem bt

Deployment test 1/3-octave bands L Zeq

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(a) Broadband A-weighted noise levels.

(b) 1 ⁄ 3 -octave bands noise levels.

Figure 12: Deployment test measured levels.

6. FINAL REMARKS AND FUTURE WORK

This project was the first step into an autonomous and wireless setup. The system is working as expected, and the hardware o ff ers a very promising performance. However, for the future, improvements must be developed, specifically:

1. Design of a more rugged and reliable data transmission setup;

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 e ffi cient and redundant power supply setup; and

5. Real-life deployment.

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.

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