An Arduino-based Device to Detect Dangerous Audio Noises

Lorenzo de Lauretis

, Tiziano Lombardi

, Stefania Costantini

and Ludovica Clementini

Universit

a degli studi dell’Aquila, DISIM Department, L’Aquila, Italy

Universit

a degli studi dell’Aquila, DIIIE Department, L’Aquila, Italy

Keywords:

Arduino, Health Issue Prevention, Dangerous Noises Detection, IoT, Portable Device.

Abstract:

The issue of noise pollution is becoming more and more relevant in our today’s way of life. Studies have shown

that some noise waves are especially damaging, triggering continuous harm to the nervous scheme with the

resulting failure of listening capacity in some instances. Thanks to the latest technological ﬁndings, noises can

be sampled and analyzed even on very tiny appliances that can possibly be carried anywhere. By testing the

noise via a condenser microphone and evaluating the outcome of applying the Fast Fourier Transform to the

sampled samples, we can identify the existence of frequencies that are considered detrimental to the auditory

system, warning a person in real-time about the prospective risk to which (s)he is facing. Moreover, using a

buzzer to alert the user when malicious sounds are detected, our device is also suitable for blind people.

1 INTRODUCTION

Living in our culture today implies being continu-

ally surrounded by the voices and vibrations gener-

ated by the most disparate causes like smartphones,

vehicles, automobiles, aerial transport, etc. Due to

population growth and urbanization, this “noise pollu-

tion” will increase (Goines and Hagler, 2007). Noises

should not be hazardous to the human ear in small

doses and low volume, but they can be hazardous if

they increase in volume and density. According to re-

cent research, with long-lasting exposition to certain

sound thresholds, there are speciﬁc sounds that can

trigger serious hearing loss in rats for several days;

these sounds have a frequency of 16kHz and stress of

115dB. There are also worst noises, with a frequency

of 4kHz with a pressure of 125 dB, that cause perma-

nent damage to the hearing of rats in both the low-

and high-frequency range (Reed et al., 2014). Sounds

at so high pressure can be dangerous for the human

ear too (Maassen et al., 2001).

To advice the user of the occurrence of these dan-

gerous noises, we built a very small device. It samples

sounds through a condenser microphone and analyzes

the result of the application of the Fast Fourier Trans-

form (FFT) to the sampled signals, thus detecting dan-

gerous noises. When dangerous noises are detected,

a buzzer installed on our device starts buzzing, so as

to warn the user of the potentially harmful noise. We

used a buzzer instead of a led, because, in this way, the

product is also suitable for blind people, helping them

in the prevention of hearing loss due to malicious

noises. We tested the device with different noises at

different frequencies/pressures, obtaining satisfactory

results.

The paper is structured as follows. In Section

2 there is an excursus about similar works related

to this topic. Section 3 presents background knowl-

edge needed to better understand the work we have

done. Section 4 describes our prototype, explaining

the architecture, the ﬁrmware and their functionali-

ties. Sections 5 and 6 discuss the testing phase and

the achieved results. Then, Section 7 reports our con-

clusions based on data obtained using our prototype.

2 RELATED WORK

In (Rajagukguk and Sari, 2018), Rajagukguk et al.

sampled the sound using a condenser microphone, an-

alyzing the data using an Arduino Uno. They used an

LCD display, a buzzer and LEDs to display the infor-

mation about the danger level of the analyzed sound.

Their device warns the user when the sound reaches

the pressure of 75dB, considered by them harmful for

the human ear. Our device is more precise, because it

does not keep track only of the sound level, but also

of the sound frequencies.

In (Bianchi, 2013), Bianchi et. al. explored the

use of the Arduino platform, that can be a versatile

de Lauretis, L., Lombardi, T., Costantini, S. and Clementini, L.

An Arduino-based Device to Detect Dangerous Audio Noises.

DOI: 10.5220/0010476403030308

In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security (IoTBDS 2021), pages 303-308

ISBN: 978-989-758-504-3

 2021 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

303

audio processor. They treated the real-time signal

processing, with particular emphasis on the FFT, re-

alizing a sort of benchmark to discover the maximum

length of an FFT that can be computed in real-time in-

side an Arduino. Their work is more about the opera-

tions that Arduino can make on audio, with the related

benchmarks, while we focus more on the practical ap-

plication of the results of FFT computation to prevent

hearing loss.

In (Silva et al., 2012), Silva et. al. made a study

on digital sound processing using Arduino and Mat-

lab

, using two different approaches: in the ﬁrst one,

all the computation is done on the Arduino, in the

second one the sound samples are instead sent to a

laptop where Matlab software performs the compu-

tations, before sending back to playback on the Ar-

duino. Their work, differently from ours, is partly im-

plemented on the Arduino and partly on a PC with

complex sound analysis. Instead, we focus only on

the Arduino without the need of the PC, thus devising

a fully portable device.

In (Raj et al., 2019), the authors explored the use

of the Arduino platform in conjunction with Matlab,

in order to develop a voice-controlled door lock sys-

tem. Interfacing among Matlab and Arduino, they

developed a tool that, using a microphone and FFT

technique, it is able to recognize different voices and

open/close a smart lock. Differently from them, we

used Arduino as a standalone device.

3 BACKGROUND

This section discusses some heritage notions about

Arduino and Fast Fourier Transform approach.

3.1 Internet of Medical Things

The Internet of Medical Things (also called the in-

ternet of health things) is an extension of the Inter-

net of things (IoT)

. It is the speciﬁc ﬁeld of appli-

cation for medical and health purposes, such as data

collection and analysis for research and monitoring.

This ‘Smart Healthcare’ infrastructure facilitates the

creation of digitized healthcare systems, connecting

available medical resources and healthcare services.

IoT healthcare systems manage chronic diseases and

their prevention and control. The connectivity for re-

mote monitoring enables health practitioners to cap-

ture data of the patients and applying complex algo-

rithms in health data analysis.

https://it.mathworks.com/products/matlab.html

https://en.wikipedia.org/wiki/Internet of things

3.2 Arduino

Arduino

is an open-source hardware and software

company that designs and manufactures single-board

microcontrollers and microcontroller kits. They are

used for building or prototyping digital devices and

interactive objects, which make use of various sen-

sors and actuators. Boards are equipped with sets of

digital and analog input/output (I/O) pins that may be

interfaced to various hardware components.

Arduino boards can be easily programmed using

an Integrated Development Environment (IDE)

and

a USB cable. A typical Arduino program is composed

of two main functions:

• setup: it is the initialization phase of the board

• loop: it is a portion of code executed repeatedly.

3.3 Fast Fourier Transform

The Fast Fourier transform (FFT

) algorithm is a

method for computing the Finite Fourier transform of

a series of N (complex) data points in approximately

N log

N operations.

The Fast Fourier Transform can be useful in many

ﬁelds, among which the one of our interest: sound

analysis. In fact, it can be used to detect Speech Spec-

trogram (Oppenheim, 1970) from the original wave-

form signal. Another use case is the Automatic Index-

ing of Musical Sounds (Zhang and Zbigniew, 2007)

through their timbre recognition.

4 ARCHITECTURE AND

FIRMWARE

4.1 SYSTEM Structure

This section discusses the architecture of the sys-

tem in order to show relations among different stages

composing our system.

Our device is composed of ﬁve macro-

components:

• Controller: the “brain” of our device, it has the

Firmware core inside. It receives input data from

the Microphone Sensor and, when dangerous

noises are revealed, it triggers the Alert Buzzer.

• Health rule checker: it contains all the mathemat-

ical rules applied to collected microphone data in

order to reveal dangerous situations.

https://en.wikipedia.org/wiki/Arduino

https://www.arduino.cc/en/Main/Software

https://en.wikipedia.org/wiki/Fast Fourier transform

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• Buzzer actuator: it is the component designed

to drive an alert buzz sound; when triggered it

changes its state, from on to off and vice-versa.

• Mic sensor: is a sound sensor, which detects audio

from the ambient and transmits data samples to

the Controller component.

The presented Component architecture is designed to

be Hardware independent, in the sense that the same

designed system can be implemented also in differ-

ent platform apart from the chosen Arduino frame-

work; which means, it could be used also in other crit-

ical applications, such as the detection of ultrasonic-

frequencies in a medical environment or very low fre-

quencies in sonar applications (i.e. resonant testing of

buildings).

In our Arduino example, we have three hardware

boards wired together. In particular:

• Arduino Board: the core board with the main con-

troller installed into it. Here is where the main

ﬁrmware runs.

• Sensor Shield: input peripheral board where sen-

sors are installed (microphone in our case). This

board collects data from the environment and

sends them to the main controller.

• Actuator Shield output peripheral board, where

actuators are installed (the Buzzer in our case).

This board is interfaced to the user.

4.2 SYSTEM Behavior

This system is characterized by three running states

during its execution, related to the danger index.

• Normal: is the idle state in which the environment

is in a safe state and nothing is prompted to the

user.

• Danger: the environment is exiting the safe state

for the presence of some dangerous frequency.

The user is softly prompted of that.

• Critical: the environment is totally unsafe for

the presence of dangerous frequencies at a high-

pressure level. The user is strongly alerted.

When entering a state, the system properly sets

its actuators to inform the user about environmen-

tal changes. Speciﬁc conditions allow the system to

change state in a scale of danger; moreover, the sys-

tem can return in a normal state if a critical situations

end.

In this ﬁrst prototype, the state machine in Fig-

ure 1 realizes the Arduino Firmware, which allows

our device to be reliable and functional. It relies on

the usage of the FFT algorithm, which is a polyno-

mial approximation of the Fourier Transformation

Algorithm 1: FFT Implemented on Arduino.

s e t s am p l i n g m a x f r e q ue n c y

s e t n u m b er o f s a m pl e s

s e t d a n g e r o u s f r e q u e n c i e s a r r a y

s e t dan g ero us c o mmo n SP L

s e t dan g e r ou s SP L

Loop {

r e a d n u mb e r o f s a mp l e s i n r a ng e ( 0 ,

s a m p l i ng m a x f r e q u en c y ) −> s a m p l e a r r a y

do FFT on s a m p l e a r r a y −> F FT a r ra y

f o r e a c h FFT elem i n F F T a rr a y {

i f FF T e lem [ SPL]> = d ang erou s co mmo n SP L {

a l e r t

}

f o r e a c h d a n g e r o u s f r e q u e n c y i n

d a n g e r o u s f r e q u e n c i e s a r r a y {

i f FF T e lem [ f r e q u e n c y ] == d a n g e r o u s f r e q u e n c y {

i f FF T e lem [ SPL ] >= d a ng er ou s SP L {

a l e r t

}

method. This method is parametrized on two parame-

ters: the sampling maximum frequency, which deter-

mines the maximum detectable frequency; the num-

ber of samples to be stored and then analyzed, which

describes our implementation is shown in Algorithm

At the beginning of the code, it is possible to see

the declaration of the two parameters cited above.

• Sampling maximum frequency: it is chosen high

enough to catch all wanted frequencies with re-

spect to hardware capabilities.

• The number of samples: it is chosen large enough

to have a certain number of samples which can

emphasize the characteristic of the audio signal,

accordingly with hardware capabilities.

After the declaration of the two parameters described

above, there is the declaration of global variables to

describe the samples’ list of data and the initializa-

tion of our algorithm variables. In the Loop method,

there is the cyclic core of the algorithm. First of all, it

collects the chosen number of samples, storing them

in dedicated memory locations. Then Fast Fourier

Transform is applied on the set of data, identifying

the spectrum characteristic of the detected audio sig-

nal, which can be compared with the target to possibly

trigger an alert, which turns on/off the output buzzer

of the circuit. This algorithm is iterated every second

in order to have real-time continuous monitoring of

the environment.

An Arduino-based Device to Detect Dangerous Audio Noises

305

Figure 1: Our Device Behavior evolution.

5 DETECTING MALICIOUS

FREQUENCIES

The main objective of our device is to warn the user

of potentially harmful noises that are in her/his sur-

rounding, helping them to prevent hearing loss. To do

that, we ﬁrst have to understand which are the harm-

ful noises, and what they can cause to the auditory

system during short and long period expositions.

As discussed in (Reed et al., 2014), there are two

main kinds of sound that are very dangerous for the

auditory system of the rat: one is 16kHz at 115dB

and one is 4kHz at 125dB. The exposition to the

noise with 16kHz and 115dB can cause severe hear-

ing loss for several days, while the exposition to the

noise with 4kHz at 125dB can cause permanent dam-

age to the auditory system of the rat. Also, as argued

in (Maassen et al., 2001), sounds at so high pressure

are dangerous for the human auditory system too, and

they should be avoided.

The noises at 115dB and 125dB are at very high

pressure, so they can be identiﬁed by the human ear;

the frequency recognition, instead, can be more difﬁ-

cult for the human ear, so we need a device that helps

the user in recognizing also these potentially harmful

frequencies. As the ﬁrst step, we built our prototype

with Arduino as the microcontroller, as shown in the

previous section. The resulting device can be used for

audio sampling and analysis. The device is very small

and is battery powered, so it is perfect to be carried

around potentially everywhere.

As next step, we wrote the code that permits to the

Arduino to work, written entirely in C++

: through

this code, we were able to sample and analyze the

audio surrounding our device and to make a buzzer

buzz.

Our device helps the user in the recognition of fre-

quency and pressure, buzzing a buzzer to alert the user

when one of the following conditions are met:

• Analyzed sound is at a frequency of 4kHz or

16kHz and the pressure is greater equal 75dB.

• Analyzed sound has a pressure greater equal

100dB.

The buzzer volume is proportional to the pressure

of the analyzed noises. When the sound pressure is

lower than 75dB, the buzzer is switched off, when the

sound pressure is greater equal 75dB it buzzes at a

low volume, when the sound pressure increases even

more, the buzzer buzzes with an increasing volume,

until it reaches a very high volume, at the 125dB.

Our buzzer does not interfere with the FFT sound

analysis: it produces sounds with a frequency of

10kHz and a volume between 30dB and 50dB (re-

programmable), frequency and volume not consid-

ered harmful from our device, thus being discarded

from the system, preventing a ”backtracking effect”,

in which the sound of the buzzer is given as an input

for our system and so on.

The device was tested using a wave generator, an

object that is able to produce sounds at particular fre-

quencies and pressures. We performed forty noises

tests, with four different pressure levels: 60dB, 80dB,

https://it.wikipedia.org/wiki/C%2B%2B

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Table 1: buzzer buzzing testing results.

60dB 80dB 90dB 105dB

2KHz No No No Yes

3KHz No No No Yes

4KHz No Yes Yes Yes

5KHz No No No Yes

6KHz No No No Yes

10KHz No No No Yes

14KHz No No No Yes

16KHz No Yes Yes Yes

18KHz No No No Yes

20KHz No No No Yes

90dB and 105dB, varying the frequency from 2kHz to

20kHz. The results of our tests are shown in Table 1:

if the buzzer buzzes at the corresponding frequency

and pressure, in the table is reported “Yes”, “No” oth-

erwise. We tested our device in a silent room, without

external sounds, to prevent interferences given from

them.

6 DISCUSSION

This paper started with an introduction to Arduino

and why it is so useful to cope with problems that

are always more relevant nowadays. We choose Ar-

duino for our project for a number of reasons: it is

open-source, it is cheap, it is all-in-one and can be

programmed using C++. Via the combination of all

these features, we got a dedicated microcontroller that

is easily expandable. The sensors and actuators are

relatively cheap, permitting, with a very low budget,

to build one’s own devices. There are a lot of Arduino

libraries available online, that permits rapid develop-

ment of code for any device. Without Arduino and

all its features, a work such as the one described in

this paper would have been more complicated and, in

some cases, it would have not been possible.

For example, a similar work could have been done

using the smartphone, with an App that samples and

analyzes the sounds. We discarded this option, be-

cause, the microphone of the smartphone is not sensi-

ble enough to sample and record the sounds that sur-

round the user, while, with Arduino, we can choose

the most useful microphone, with speciﬁc sensibility

and characteristics.

Within the data obtained from the testing, it is

possible to verify that the devices work well, cor-

rectly identifying the potentially dangerous frequen-

cies/pressures. When the noises produced from our

wave generator had a pressure of 60dB, the buzzer on

our device had not been active, because the analyzed

sound was not potentially harmful. When we tested

the device with noises with the pressure set at 80dB,

the buzzer buzzed when the noises reached 4kHz and

16kHz frequencies, correctly advising the user of the

analyzed harmful frequencies. When the noises pro-

duced from our wave generator had a pressure of

90dB, the buzzer buzzed when the noises reached

4kHz and 16kHz frequencies, correctly warning the

user of the potential danger for his ears. When the

noises had a pressure of 105dB, the buzzer was ac-

tive within all the frequencies reached from our wave

generator, because we consider dangerous noises at so

high pressure. The buzzer buzzing volume is propor-

tional to the pressure of the analyzed noises.

A problem that is possible to identify in our sys-

tem is the microphone sensibility, that, in some cases,

cannot be enough to sample very high sound pres-

sures: the microphone we used for testing is a con-

sumer one, that can be bought from everybody to

build own devices, and not a professional one; so, the

precision is not so high for professional use; it may

lead to a loss of precision during measurements.

Another problem that is possible to identify is the

”backtracking effect”: using a buzzer to alert the user

when dangerous noises are revealed, the sound of the

buzzer may act as an input for our microphone, loop-

ing through the system. To prevent this, our buzzer

produces sounds with a frequency of 10kHz and with

a volume between 30dB and 50dB (reprogrammable);

being this kind of sound unharmful, our algorithm

simply discards it and does not act as a recursive in-

put for our device. If, for example, our buzzer had a

frequency of 4kHz and a volume of 70dB, it will have

acted as a dangerous sound source, producing sounds

considered harmful by our device, and causing our

buzzer to increase the volume, causing detection of

more dangerous sounds, and so on.

Another problem that is possible to identify is that,

if our device is used in a place with other people in the

near surroundings, the device can eventually be dis-

turbing for them, if the buzzer buzzes continuously.

A way to avoid this, without losing the feature of be-

ing suitable for blind people, is simply to decrease the

volume of the buzzer: being the device volume repro-

grammable, it is possible to set to a very low buzzing

volume range, for example, 5dB to 10dB, resulting no

more disturbing for people in the surroundings.

From the testing results, we can ensure that our

device works correctly with respect to the workﬂow,

properly identifying the potentially dangerous fre-

quencies/pressures.

An Arduino-based Device to Detect Dangerous Audio Noises

307

7 CONCLUSIONS AND FUTURE

WORK

In this work, we created a tool that warns the user

when potentially harmful noises are detected in its

near surroundings. It is very small, meaning that it

can be carried potentially everywhere. It is cheap and

is easy to build, meaning that it can be constructed

potentially by everybody. It is functional because

it is able to sample and analyze the noises coming

from the near surroundings, warning the user with the

buzz of a buzzer if the analyzed noises are potentially

harmful to them. Using the buzzer instead of a led

or other kinds of light-based actuators, our system is

suitable also for blind people, helping them in the pre-

vention of hearing loss. In the near future, we will try

to develop a system that uses a vibrating actuator in-

stead of a buzzer one, to avoid disturbing people that

can be in the near surrounding of the user. We will

also build another version of our system, with a bet-

ter professional microphone, that will be able to sam-

ple the sounds with better precision, even at higher

sound pressures. Using the design guidelines illus-

trated here, everybody can build its own system with a

very low budget, safeguarding their own auditory sys-

tem and protecting their ears from auditory diseases.

REFERENCES

Bianchi, A. J. (2013). Real time digital audio processing

using arduino.

Goines, L. and Hagler, L. (2007). Noise pollution: A mod-

ern plague. Southern Medical Journal, 100(3).

Maassen, M., Babisch, W., Bachmann, K., Ising, H.,

Lehnert, G., Plath, P., Plinkert, P., Rebentisch, E.,

Schuschke, G., Spreng, M., Stange, G., Struwe, V.,

and Zenner, H. (2001). Ear damage caused by leisure

noise. Noise and Health, 4(13):1–16.

Oppenheim, A. V. (1970). Speech spectrograms using the

fast fourier transform. IEEE Spectrum, 7(8):57–62.

Raj, L. D. W., Santhosh, K., Subash, S., Sujin, C., and

Tharun, P. (2019). Voice controlled door lock system

using matlab and arduino. In 2019 IEEE International

Conference on System, Computation, Automation and

Networking (ICSCAN), pages 1–6. IEEE.

Rajagukguk, J. and Sari, N. E. (2018). Detection system

of sound noise level (snl) based on condenser micro-

phone sensor. Journal of Physics: Conference Series,

970(1):012025.

Reed, A. C., Centanni, T. M., Borland, M. S., Matney, C. J.,

Engineer, C. T., and Kilgard, M. P. (2014). Behav-

ioral and neural discrimination of speech sounds after

moderate or intense noise exposure in rats. Ear Hear,

35(6):e248–e261. 25072238[pmid].

Silva, S., Soares, S., and Valente, A. (2012). Digital sound

processing using arduino and matlab.

Zhang, X. and Zbigniew, W. R. (2007). Analysis of sound

features for music timbre recognition. In 2007 Inter-

national Conference on Multimedia and Ubiquitous

Engineering (MUE’07), pages 3–8.

IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security

308