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.
Living in our culture today implies being continually surrounded by
the voices and vibrations generated by the most disparate causes like
smartphones, vehicles, automobiles, aerial transport, etc. Due to
population growth and urbanization, this “noise pollution” will increase
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To advice the user of the occurrence of these dangerous noises, we
built a very small device, called EarSaver. It samples sounds through
a condenser microphone and analyzes the result of the application
of the Fast Fourier Transform (FFT) to the sampled signals, thus
detecting dangerous noises. When dangerous noises are detected, a led
installed on EarSaver starts blinking, so as to warn the user of the
potentially harmful noise. We tested the device with different noises
at different frequencies/pressures, obtaining satisfactory results. This
work is part of the eHealth project [
The paper is structured as follows. In Section 2 there is an excursus about similar works related to this topic. Section 3 presents background knowledge needed to better understand the work we have done. Section 4 describes our prototype, explaining the architecture,
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This section discusses some heritage notions about Arduino and Fast Fourier Transform approach. 3.1
The Internet of Medical Things (also called the internet of health things) is an extension of the Internet of things (IoT)3. It is the specific field of application 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 remote monitoring enables health practitioners to capture data of the patients and applying complex algorithms in health data analysis.
3.2 Arduino4 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 sensors 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)5 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
The Fast Fourier transform (FFT6) algorithm is a method for computing the Finite Fourier transform of a series of N (complex) data points in approximately N log 2N 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 Spectrogram [
This section discusses the architecture of the system using an UML Diagram Component in order to show relations among different stages composing our system. As shown in Figure 1, EarSaver is composed of five macro-components:
Controller: the “brain” of EarSaver, it has the Firmware core inside. It receives input data from the Microphone Sensor and, when dangerous noises are revealed, it triggers the Alert LED. Health rule checker: it contains all the mathematical rules applied to collected microphone data in order to reveal dangerous situations.
LED actuator: it is the component designed to drive an alert LED; 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 different platform apart from the chosen Arduino framework; which means, it could be used also in other critical applications, such as the detection of ultrasonic-frequencies in a medical environment or very low frequencies in sonar applications (i.e. resonant testing of buildings).
When entering a state, the system properly sets its actuators to inform the user about environmental changes. Specific conditions allow the system to change state in a scale of danger; moreover, the system can return in a normal state if a critical situations end.
In this first prototype, the state machine in Figure 3 realizes the Arduino Firmware, which allows EarSaver to be reliable and functional. It relies on the usage of the FFT algorithm, which is a polynomial approximation of the Fourier Transformation method. This method is parametrized on two parameters: the sampling maximum frequency, which determines the maximum detectable frequency; the number of samples to be stored and then analyzed, which describes our implementation is shown in Algorithm 1.
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 respect 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 initialization 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 signal, which can be compared with the target to possibly trigger an alert, which turns on/off the output LED of the circuit. This algorithm is iterated every second in order to have real-time continuous monitoring of the environment. 5
The main objective of our device is to warn the user of potentially harmful noises that are in her/his surrounding, helping them to prevent hearing loss. To do that, we first have to understand which are the harmful noises, and what they can cause to the auditory system during short and long period expositions.
As discussed in [
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 difficult 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 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++7: through this code, we were able to sample and analyze the audio surrounding our device and to make a led blink.
Our device helps the user in the recognition of frequency and pressure, blinking a led 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 led blinking frequency is proportional to the pressure of the analyzed noises. When the sound pressure is lower than 75dB, the led is switched off, when the sound pressure is greater equal 75dB it blinks at a 2Hz frequency, when the sound pressure increases even more, the led blinks with an increasing frequency, until it reaches the 100Hz blinking frequency, at the 125dB.
The device was tested using a wave generator, an object that is able to produce sounds at particular frequencies and pressures. We performed forty noises tests, with four different pressure levels: 60dB, 80dB, 90dB and 105dB, varying the frequency from 2kHz to 20kHz. The results of our tests are shown in Table 1: if the led blinks at the corresponding frequency and pressure, in the table is reported “Yes”, “No” otherwise. We tested our device in a silent room, without external sounds, to prevent interferences given from them. 2KHz 3KHz 4KHz 5KHz 6KHz 10KHz 14KHz 16KHz 18KHz 20KHz 6
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 Arduino 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 permit rapid development 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.
Within the data obtained from the testing, it is possible to verify that the devices works well, correctly identifying the potentially dangerous frequencies/pressures. When the noises produced from our wave generator had a pressure of 60dB, the led on our device had not blinked, because the analyzed sound was not potentially harmful. When we tested the device with noises with the pressure set at 80dB, the led blinked when the noises reached 4kHz and 16kHz frequencies, correctly advising the user of the analyzed harmful frequencies. When the noises produced from our wave generator had a pressure of 90dB, the led blinked 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 led blinked within all the frequencies reached from our wave generator, because we consider dangerous noises at so high pressure. The led blinking frequency is proportional to the pressure of the analyzed noises.
A problem that is possible to identify in our system is the microphone sensibility, that, in some cases, cannot be enough to sample very high sound pressures: the microphone we used for testing is a consumer 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. From the testing results, we can ensure that our device works correctly with respect to the workflow, properly identifying the potentially dangerous frequencies/pressures. 7
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 blink of a led if the analyzed noises are potentially harmful to them. In the near future, we will build another version of our system, with a better professional microphone, that will be able to sample the sounds with better precision, even at higher sound pressures. Using the design guidelines illustrated here, everybody can build its own system with a very low budget, safeguarding their own auditory system and protecting their ears from auditory diseases.