INTEGRATING A SIMPLE SENSOR-BASED TESTBED IN SIGNAL
PROCESSING COURSES
A Recipe to Design an Electronic Instrument
Bel
´
en Torrente, Adriana Dapena, Santiago J. Barro, Julio Breg
´
ains and Carlos J. Escudero
Department of Electronics and Systems, University of A Coru
˜
na, A Coru
˜
na, Spain
Keywords:
Learning applications, Supervised projects, Hardware and software integration, Music synthesis, Fourier se-
ries.
Abstract:
The widespread idea that traditional classroom lectures is not the best teaching approach has motivated edu-
cators to continuously seek new ways to encourage students towards active involmenent. Studies show that
constructivist learners tend to explore the concepts covered in laboratory activities, resulting in a richer un-
derstanding. In this sense, we will propose to integrate simple sensor-based electronic musical instruments
in signal processing courses to help students understand basic concepts, while making them to interact with a
real systems that integrates, at once, software and hardware components.
1 INTRODUCTION
Our experience of more than 10 years teaching sig-
nal processing related courses as part of the Computer
Engineering degree shows that students have difficul-
ties for understanding the mathematical foundations
of this engineering area. This limitation can be miti-
gated by proposing laboratory activities consisting of
computer simulations performed using software tools
such as Matlab/Simulink, or similar (Blanchet and
Charbit, 2006). The experiments are designed not
only to write code for a specific method, but also
to test the students’ comprehension of the materials
covered in previous theoretical lectures. However,
we have observed that this kind of laboratory activity
does not allow us to exploit the “rich world” of signal
processing because students tend to regard laboratory
classes as an activity where the most important task is
to write code.
Over the past few years, we have invested great
effort in setting the possibility of taking advantage of
integrating hardware and software in our laboratories
(Garc
´
ıa-Naya et al., 2010). This allows students to
face a series of problems that they do not encounter
in computer simulations like, for instance, hardware
selection, faults and integration of hardware and soft-
ware.
In this paper we present a “recipe” to create cus-
tom sensor-based electronic musical instruments con-
formed by three elements: sensors, an acquisition de-
vice and a synthesis environment. Sensors are de-
vices capable of turning the measurement of a phys-
ical quantity into an electrical signal. Their outputs
are processed by the acquisition device to provide a
specific list of sound parameters needed by the syn-
thesis algorithm. Instead of using comercial products
(like LabView (National Instruments, 2010)), we have
preferred to use open-source software adapted to our
courses needed. In particular, the selected acquisi-
tion device has been the Arduino board for several
reasons: it is an open-source project, it uses a C++
like programming language (called Wiring), includes
a built-in ADC, and has USB connectivity. Finally,
a synthesis algorithm is implemented in a Personal
Computer (PC), generating music in accordance with
the sensors’ parameters.
The modular structure of our testbed allows access
to it at different levels of abstraction. This allows the
system to be used with different purposes:
• Explaining basic concepts of signal processing.
• Developing supervised projects where students
must integrate software and hardware.
• In addition, although this paper focuses on edu-
cation, the ideas presented here have many other
applications like electronic music instrument de-
sign, generation of sound banks and didactic mu-
seum activities,
This paper explains the two phases needed to develop
such electronic instruments:
87
Torrente B., Dapena A., J. Barro S., Bregáins J. and J. Escudero C..
INTEGRATING A SIMPLE SENSOR-BASED TESTBED IN SIGNAL PROCESSING COURSES - A Recipe to Design an Electronic Instrument.
DOI: 10.5220/0003311700870092
In Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU-2011), pages 87-92
ISBN: 978-989-8425-50-8
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Overall System.
Figure 2: Our testbed.
• Component selection. Section 2 offers a general
description of the system structure by presenting
the parts involved.
• Component integration by developing a graphical
application (Section 3).
In the 2010-2011 academic year, we have used elec-
tronic instruments to explain the ideas behind the
Fourier series. Section 4 shows the results obtained
from this experience.
2 GENERAL SYSTEM
OVERVIEW
The idea behind our laboratory activity is that stu-
dents must interact with real music instruments ob-
tained by integrating hardware equipment and soft-
ware programming. Figure 1 shows the organisation
of our testbed and Figure 2 shows a picture of a spe-
cific configuration. Three main parts can be distin-
guished from left to right in Figure 1. The first el-
ements we encounter are the sensors, which are ca-
pable of providing context information of a varying
nature. Next, data must be collected by an acquisition
device, which will typically do some preprocessing
on the sensor information. Finally, the output of the
acquisition device is the input to the computational
system (for instance, a PC) in charge of a synthesis
algorithm that should be able to provide two kinds of
outputs: a text-based descriptive model, and the real-
time sound output of the synthesis algorithms, both
using the parameters provided by the sensor integra-
tion.
2.1 Sensors
A sensor is a device which responds to a certain stim-
ulus (physical quantity), converting it into an electri-
cal signal, that electronic devices can use (Wilson,
2005). They can be classified according to different
criteria: the technology used (inductive, capacitive,
resistive, piezoelectric, optical, etc.), the kind of fea-
ture measured (acceleration, distance, position, rota-
tion, etc.).
The first step to design the student’s instrument
is to make an appropriate selection of the sensors in-
volved. This must be done taking into consideration
several parameters (Wilson, 2005): sensibility (small-
est variation that can be detected), dynamic range
(signals outside this range are expected to cause unac-
ceptably large inaccuracy), etc. In particular, for our
purpose, it is very attractive to consider touch sensors
(potentiometers) because, by using them, the correct
functioning of the overall system is easier to check.
That is, the numeric result can almost be calculated at
a guess. As an example, the system shown in Figure
2 uses three potentiometers, each of them controlling
an individual parameter of the synthesis algorithm de-
scribed in Section 2.3.
2.2 Acquisition Device
The sensing devices are connected to a circuit that
captures the input values with a certain periodicity
and transforms signals into a specific list of sound
parameters required by the synthesis algorithm. For
this task, we have selected Arduino (Arduino project,
2010).
The Arduino platform is composed by a single-
board microcontroller and a software developing en-
vironment. The Arduino board is based on an 8-
bit Atmel AVR microcontroller with other additional
components, such as input pins. Its layout makes it
easy for the CPU board to be connected to a wide
range of modules.
The Arduino IDE (Integrated Drive Electronics),
written in Java, which makes it cross-platform, is
an application derived from the IDE made for the
Wiring project and the Processing programming lan-
guage. It was initially designed for teaching purposes,
so it is perfect for newcomers to software develop-
ment. Using the so-called Arduino2Max patch it is
possible to program our instruments visually by using
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88

Table 1: Popular Synthesisers.
Model Year Synthesis method
Moog Minimoog 1971 Subtractive
Yamaha DX7 1983 FM
Casio CZ-101 1984 Phase distortion
Kawai K5 1987 Additive
Roland D50 1987 Sample-based
Korg Wavestation 1990 Wavetable
Korg Prophecy 1996 Physical model
MAX/MSP objects (as will be explained later in this
paper). Arduino2Max is also responsible for sending
the data in real time so that users can see the values at
each instant.
Communications in Arduino are performed via
the serial port. From the existing approaches, the
one adopted by Arduino2Max is based on the serial
communications library called SimpleMessageSys-
tem (Arduino SimpleMessageSystem, 2010). It pro-
vides three different operations: one for clearing a pin
and the other two for reading data as an ASCII string
(one for digital pins and the other one for analog pins).
SimpleMessageSystem allows us to send, receive and
parse characters, integers and lists between Arduino
and other applications, such as MAX/MSP or Pure
Data.
2.3 Synthesis Techniques
Sound synthesis is the process of generating sounds
artificially (Pinch, 2002). Nowadays, the most pop-
ular kind of synthesis uses sounds sampled from an
acoustic instrument (sample synthesis, granular syn-
thesis). Other types use cyclic patterns (wavetable
synthesis), physical mathematical models, or basic
operations using simple waves (additive, subtractive
and FM synthesis) (Russ, 2008). Table 1 provides a
list of some popular synthesisers, and which method
they have used.
Additive synthesis is directly related to the Fourier
Series which describes a signal as a combination of
elementary trigonometric functions. It is the tool
most extensively applied in both signal analysis and
synthesis and indeed finds application in many areas
(Vaseghi, 2007) such as telecommunications, signal
processing (filtering, etc.), music processing , signal
coding and feature extraction. However, this concept
proves difficult to understand by many undergradu-
ate students (Friedberg, 2001) because the mathemat-
ical derivations are tedious and the students are not
able to see the practical applications. In our opinion,
programming and testing an additive synthesiser is a
good opportunity to catch students’ attention towards
Fourier Series.
Fourier’s work can be summarised as follows. De-
note by f (x) a function of the real variable x. This
function is usually taken to be periodic, of period 2π,
i.e., f (x+2π) = f (x), for all real numbers x. Fourier’s
work states that this function can be expressed as an
infinite sum, or series, of simpler periodic functions
with period 2π. More formally, an integrable function
on [−π, π] denoted by f (x) can be expressed as an in-
finite sum of sine and cosine functions on the same
interval, given by
f (x) =
a
0
2
+
k
max
∑
k=1
[a
k
cos(kx) + b
k
sin(nx)] (1)
where k
max
denotes the number of harmonics, and a
n
and b
n
are the amplitudes of the components of the
Fourier series,
a
n
=
1
π
π
Z
−π
f (x)cos(nx)dx (2)
b
n
=
1
π
π
Z
−π
f (x)sin(nx)dx (3)
From these equations, it is a good exercise for stu-
dents to determine the expression of the Fourier series
in terms of the complex exponentials e
jnx
= cos(nx)+
j sin(nx) (Oppenheim et al., 1998). This development
is beyond the scope of our paper.
It may be noted that the sound created by any
musical instrument can be expressed in terms of the
Fourier series (Vaseghi, 2007). This is the basis of ad-
ditive synthesis (also called Fourier synthesis), which
consists of adding together simple sinusoidal waves
to create a more complex timbre (Russ, 2008). More
rigorously, any periodic sound in the discrete time do-
main can be synthesised as follows
x(n) =
a
0
[n]
2
+
k
max
∑
k=1

a
k
[n]cos(
2πk f
0
F
s
n)
+ b
k
[n]sin(
2πk f
0
F
s
n)

(4)
where a
k
[n] and b
k
[n] are the series coefficients, f
0
is the fundamental frequency and F
s
is the sampling
frequency.
3 COMPONENT INTEGRATION
The production of sounds can be done by giving con-
trol over the frequency and amplitude of each individ-
ual harmonic. For instance, in our testbed (Figure 2)
we have three potentiometers for controlling timbre
INTEGRATING A SIMPLE SENSOR-BASED TESTBED IN SIGNAL PROCESSING COURSES - A Recipe to Design
an Electronic Instrument
89

Figure 3: Example of additive synthesis (fundamental frequency is 440 Hz).
import com.cycling74.max.*;
import java.math.BigInteger;
public class stdfilt extends MaxObject {
public stdfilt(Atom[] args) {
declareInlets(
new int[]{DataTypes.ALL});
declareOutlets(
new int[]{DataTypes.ANYTHING});
}
private float num1,num2,num3,num4 = 0;
public void inlet(float f){
num4 = num3;
num3 = num2;
num2 = num1;
num1 = f;
double mean = (num1+num2+num3+num4)/4;
double std2 = (num1-mean)*(num1-mean)+
(num2-mean)*(num2-mean)+
(num3-mean)*(num3-mean)+
(num4-mean)*(num4-mean);
if (Math.sqrt(std2)<10)
outlet(0,f);
else
outlet(0,0);
}
}
}
Figure 4: JAVA external component mxj stdfilt.
colour, pitch and amplitude. Timbre is related to the
weight of the components of the Fourier series (a
k
[n]
and b
k
[n]), pitch corresponds to the fundamental fre-
quency f
0
, and by amplitude we mean the weight of
the fundamental frequency component, a
o
[n].
Using the Arduino2Max patch, the additive syn-
thesiser is programed by adding and combining boxes
(objects) as seen in Figure 3. From top to bottom, we
can see the following elements:
• (mxj stdfilt) is a Java external component that we
have coded to avoid the undesirable behaviour
of contact sensors that produce erroneous out-
puts when the sensor is not pressed (see Figure
4). Basically, this function computes the variance
with respect to the mean of the sensor’s values in
four consecutive samples, and compares it with a
threshold. If the value is lower than the threshold,
the sensor is pressed. On the contrary, a high vari-
ation implies that the sensor is giving an erroneous
output.
• Multiplication boxes ((∗1), (∗2), (∗3) and (∗4))
are used to multiply the value of the fundamental
frequency indicated in Sensor 2 by a scalar quan-
tity (1, 2, 3 and 4), i.e., the outputs are the har-
monic frequencies.
• (cycle ˜ ) generates a wave with the input fre-
quency.
• ( * ˜ ) multiplies the amplitude of a wave times the
value indicate by Sensor 2.
• (expr $ f1/1023 ) is used to convert the values into
the range (0, 1)
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<instrument id=’ins1’ synth=’add’ f0=’440’>
<overtones>
<overtone freq=’1’ phase=’0’ amp=’1’/>
<overtone freq=’2’ phase=’0’ amp=’0.2’/>
<overtone freq=’3’ phase=’0’ amp=’0.2’/>
<overtone freq=’4’ phase=’0’ amp=’0.2’/>
</overtones>
<envelope length=’200’ finite=’true’>
...
</envelope>
</instrument>
Figure 5: An instrument described by means of XML.
The example in Figure 3 corresponds to a fundamen-
tal frequency f
0
= 440 Hz introduced by pressing sen-
sor 2, and an amplitude of 0.2 for the Fourier coeffi-
cients b
k
= a
k
= 0.2, k = 1, 2, 3 (sensor 1). The am-
plitude of the composite signal is fixed by sensor 3 to
0.5.
The application provides two kinds of output: the
sound output of the synthesis algorithms and a text-
based descriptive model. The sound allows the stu-
dents test in real-time the effect that the Fourier Series
parameters have in the synthesised signal.
The text-based descriptive model consists in en-
riching alternative way of storing music information.
This has been done using XML. It seems a good so-
lution because XML documents can be parsed with-
out difficulty, thus simplifying a possible port to other
platforms. For our purpose, we have studied the
most widely supported format, called MusicXML. It
is mainly aimed at constructing staff notation repre-
sentations, that could be played back by any MIDI
enabled device. Due to the nature of MIDI, that rests
upon a finite number of sound banks, referred to as in-
struments, and there is no room for synthesis param-
eters. We have therefore opted for a different XML
format tailored to suit this project’s specifications, in-
cluding dynamic timbre changes, as well as pitch and
velocity.
Figure 5 shows part of the XML representation
of the additive synthesis parameters given in Figure
3. For the sake of simplicity, envelope configuration
has been omitted. When the performance starts, the
<instrument> tag will be generated. The instrument
description will be followed by a series of tags de-
scribing any change that takes place to the original
parameters, containing a timestamp.
4 AN EXPERIENCE
In the current academic year (2010-2011), we have
used electronic instruments to explain the applicabil-
ity of the Fourier Series. The experience has consisted
in explaining how to generate music using the additive
synthesis method described in subsection 2.3. Change
the sensors parameters, the student have tested the ef-
fect of varying the Foruier serie parameters: funda-
mental frequency, amplitude of harmonic and ampli-
tude of the final wave.
We have evaluated the interest of our students in
this experience by means of a survey containing three
questions:
• Question 1. Did you known that the Fourier Series
can be used to generate music?
• Question 2. Would like you create your own elec-
tronic instrument?
• Question 3. What do you think about the presen-
tation?
There were 12 students in the course. Figure 6
shows the results of the survey. It seems that the
students have positively evaluated the fact of using
an electronic instrument to illustrate the concept of
the Fourier Series. For this reason, in the following
academic years, we will propose a laboratory activ-
ity where students must design their own electronic
music instruments.
5 CONCLUSIONS AND FURTHER
WORK
In this paper, we have explained the steps followed
to build a sensor-based electronic instrument by inte-
grating the open-source Arduino tools and low cost
sensors. The system that has been implemented is
characterized by its modularity and its innovative fo-
cus towards dynamic timbre features. We hope that
its utilization in courses of signal processing can help
the students to understand some important concepts,
like the Fourier Series, because the students can ex-
periment directly with hardware and software which
is always more enriching that mere computer simula-
tions. Besides, no proprietary software is needed, and
the hardware used has a very low cost.
Some improvements on the work presented here
are possible. First, implementing synthesis tech-
niques other than additive synthesis is one of the goals
envisioned by the authors in the short-term. Second,
testing other kinds of sensors, such as gyroscopes or
artificial vision could be a source of much innovation
with regards to the user-system interaction. Besides,
using wireless technologies (such as Bluetooth) can
make the interaction more natural. Finally, we have
INTEGRATING A SIMPLE SENSOR-BASED TESTBED IN SIGNAL PROCESSING COURSES - A Recipe to Design
an Electronic Instrument
91

Figure 6: Survey results.
found that Arduino’s 10-bit ADC may be not accurate
enough. Even if the 1024 value resolution is more
than adequate for certain parameters (e.g., velocity),
in some cases it could fall short in representing a
rich pitch range. For instance, such measurement-
frequency mapping would have steps that are too big
for a glissando. Further improvements could be made
in this direction, using a different ADC.
ACKNOWLEDGEMENTS
This work was supported by Xunta de Galicia (grants
numbers 10TIC105003PR and 10TIC003CT ), Minis-
terio de Ciencia e Innovaci
´
on of Spain (IPT-020000-
2010-35).
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