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Instrumental gestures and sonic textures

  • Institut supérieur de Musique et Pédagogie (IMEP), Namur, France

Abstract and Figures

The making of textures is a field largely associated to the visual domain; sonic textures are less known and explored. Instrumental gestures, especially in digital musical instruments, are often seen from a pitch-oriented point of view, and are rarely viewed as natural gestures linked to textures. Mapping between gestures and textures still remains a case per case experiment. This article is a contribution to a framework including instrumental gestures and sonic textures. First mapping between a gesture and a sonic process is defined. The specificity of textures is then examined; algorithms implementations and sensor technology help stay on the ground of possible things with a computer and are illustrated by examples taken from the literature. A large part of the article is then dedicated to personal experiments conducted in our labs. A discussion follows, which gives an occasion to set new perspectives dealing with the notion of ecological sounds and gestures.
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Jehan-Julien FILATRIAU
Daniel ARFIB
Communications Laboratory,
Université catholique de Louvain (UCL),
1348 Louvain-la-Neuve, Belgium
31 Ch Joseph Aiguier
13402 Marseille Cedex 20 France
The making of textures is a field largely associated to
the visual domain; sonic textures are less known and
explored. Instrumental gestures, especially in digital
musical instruments, are often seen from a pitch-
oriented point of view, and are rarely viewed as natural
gestures linked to textures. Mapping between gestures
and textures still remains a case per case experiment.
This article is a contribution to a framework including
instrumental gestures and sonic textures. First mapping
between a gesture and a sonic process is defined. The
specificity of textures is then examined; algorithms
implementations and sensor technology help stay on the
ground of possible things with a computer and are
illustrated by examples taken from the literature. A large
part of the article is then dedicated to personal
experiments conducted in our labs. A discussion
follows, which gives an occasion to set new
perspectives dealing with the notion of ecological
sounds and gestures.
A new field (not to say a discipline) has taken place
around « gesture-controlled audio systems » [12].
Within this field, applications such as digital musical
instruments have pointed out the need for a proper
relationship between gesture sensing and sonic process,
in so as to get the feeling of an instrument and not only
the control of a process. Some of these digital musical
instruments (DMI) have a strong connotation of
acoustical instruments, and then some laws can be
retrieved from these. Others deal with sonic textures,
and look free from any convention. The goal of this
article is to set a framework to study gestural audio-
systems using audio textures, and to show that the
specificity of these sonic textures leads to different links
with gestures. In section 2 we shall see what kind of
algorithms and sensors can be used, and in section 3 we
shall present some realisations that can help us
understand where to go in this large area. We shall
finally have a discussion on the implications of these
experiments and a prospective view of the possible
research on gesture and texture.
2.1. Gesture to sound: the digital musical instruments
The matter of mapping in digital musical instruments
has given rise to some formalizations of mapping
possibilities between gesture sensing and sound
processing (Fig. 1).
gesture ?sound
Figure 1. Gesture and sound connection
On our side, we have strongly pushed two main ideas:
2.1.1. Sound to gesture
Instead of using gestural devices and trying to connect
these data to sound, which is a sonification of gesture, it
may turn out musically interesting to build the inverse
link: from sound to gesture. In fact many sonic
processes are already known in the “non real time
processing” as the result of algorithm using languages
such as Csound [30] or Music V. They are generally
composer oriented, which can be a drawback (they are
not playable) but which also has an advantage: the
sound is constructed and only the imagination sets the
rules and the borders of the domain. Also in musicology
one can talk about the “musical gesture” in the sense of
the development of musical features apprehensible by
the human brain during the listening process. So the
“gesture to sound” philosophy and practise would
ideally link a “gestural gesture” to a musical gesture”.
Acoustical instruments do so of course because they
have been born with this following concept in mind:
lutherie is the art of giving performers and composers a
tool to obtain the sound they want, within limits which
come either from the physics of the instrument or the
ergonomics of the human gesture.
2.1.2. Intention to expression
A gesture by itself is the result of an intention. A sound
is a result of an expression. So we could say that music,
and especially music performance is the realisation of an
expression coming from an intention. When it comes to
digital musical instruments, we have to connect in a way
gesture sensing parameters to sound algorithmic
(synthesis or analysis synthesis) parameters. It is always
a good practice to think about a way to recover intention
from gesture, to map this intention to an expression and
see how this expression can be translated to synthesis
parameters (Fig. 2). These two intermediate levels are
sometimes called “psycho acoustic parameters” [3].
intention expression
gesture sound
Figure 2. Intention and expression connection
2.2 Sonic textures
2.2.1. The specificity of sonic textures
Textures are sounds which grossly respond to this
definition: on a short-term scale, they are composed of a
succession of micro-structural elements, subject to some
randomness; at a long term scale, a temporal and
spectral coherence is preserved (“approximately
stationary”). We will see in next subsection a more
detailed typology of textures. For now what is important
is to understand that the normal cues for the design of
musical instruments just do not work: there is no strong
temporal profile, but usually the feeling of a flux, and
there is not either an harmonic structure on which one
could rely for a pitch oriented direction. Thus musicians
that tried to deal with textures have taken very specific
views, just like a case per case situation. The feeling of
“ambient music” evoked by textures makes them
favourites for installations, or video music, but an
instrumentalisation of textures is not a well-explored
2.2.2. Musical typologies of sonic textures
Electronic music has dealt a lot with sonic textures and
many attempts have been done which are concerned
either by the description of the sonic side, or by our
perception. From this perceptual point of view, textures
are good candidates for the ecological approach, which
has been initiated by Gibson and applied to sound by
some authors [26].
When it come to practice, we also need a more “signal
processing oriented” approach, and we can find in P.
Hanna’s work [19] such a typology. He distinguishes
- coloured noise in which the main characteristic looks
like the filtering of a stationary noise by an evolving
- pseudoperiodic noise where in fact we have a
texturisation of repetitive sound, especially in machines
that “make noise”;
- impulsive noise where the main characteristic is the
successive clicks, possibly following a statistical law.
Each of these three classes corresponds to a different
“musical gesture”. As an example, the ocean seashore
sound is belonging to the first class and the sound of
rain, to the third one, even through both of them use
water. The first class deals with spectral content, while
the third one looks more like a transient succession,
hence related to rhythm. The intermediate one is pitch
oriented, and the timbre is analog to a texturalisation of
a shape in the visual domain.
This points out on the fact that under the word
textures, we have a wide variety of sounds, and that we
should probably distinguish between them in order to
use them in an audio-gestural system. If for a vocal
instrument we can distinguish between the source
(glottal pulse) and the resonance (articulation), here we
have the distinction between the nature of a source –
impulsions or noise - and its coloration. However these
two, source and resonance are intermeshed, we do not
usually have independence between the two.
2.3. Algorithms and implementations: analysis-
synthesis techniques
In this section, we present a panel of analysis/synthesis
and pure synthesis techniques dedicated to sonic
2.3.1 Analysis-synthesis methods
This group of methods aims to synthesize original sonic
textures from the analysis of an input texture. We can
distinguish three class of analysis/synthesis techniques:
first, the methods inspired of the computer graphics
field for visual textures synthesis, second the methods
derived from granular synthesis and finally techniques
based on source-filter modelling of the sonic textures.
2.3.2. Methods inspired from visual textures synthesis
In [5] and [17], Bar-Joseph & al. proposed a method
suited for both visual and sonic textures synthesis,
relying on statistical learning and resampling of a tree
representing the wavelet transform of an input texture.
From the original tree, new random trees with the same
statistical characteristics are generated and then
transformed back to produce new sonic textures,
statistically similar and perceptually close to the original
sound. Parker & Chan [27] suggested another technique
close to this approach, originally devised for visual
textures synthesis [31], where the input texture is
represented as a Gaussian pyramid.
2.3.3. Grain-based methods
In granular synthesis [28], original complex sounds are
created in combining small audio chunks (“grains”)
obtained by segmenting an audio source. Granular
synthesis is not well suited for analysis/synthesis
process because grains are randomly sliced, which
prevents from preserving the original structure of the
sound. Hoskinson [20] and Lu [23] proposed similar
algorithms to split up an audio source into variable sized
“natural grains”, relying on frame-based analysis of
wavelets and mel-frequency cepstral coefficients
(MFCC) respectively. Similarity and transition
probability between each segment are calculated for use
in the synthesis step, in which the grains are recombined
into a continuous stream with following the transition
probabilities to avoid audible discontinuities. Cardle [9,
10] developed an improved version of both Hoskinson’s
and Bar-Joseph’s algorithms, by weighting the
appearance of each grain in the synthesized sound to
add high-level user-control over the synthesis process.
2.3.4. Source-filter approaches
This class of methods adopt a source-filter approach to
model sonic textures. Analysis aims at capturing
properties of both excitation and filter. Athineos and
Ellis [4] suggest a method based on a double linear
prediction, named synthesis by cascade time- and
frequency- domain linear prediction (CTFLP). In order
to render most precisely the characteristic short-term
temporal structure of sonic texture, the temporal
envelope of the signal is captured by a linear prediction
step in the spectral domain so that the microfluctuations
of the original texture are faithfully reproduced. Zhu and
Wise [32] presented a extended version of the CTFLP
synthesis, where sonic textures are considered as a mix
of a background “din”, synthesized by filtered noise,
and a foreground micro events sequence following a
probabilistic distribution in their occurrence.
2.4. Algorithms and implementations: pure synthesis
2.4.1. Noise filtering techniques
Colorization of white noise by filtering techniques
allows creating sonic textures in many various ways.
For instance, in the “Filtering String” instrument, we
used the shape of a slow-moving string to control the
gains in a filter bank with noise as sound input [2]. The
coloration given by frequency resonances and
fluctuations in the sound due to motion of the string
enables to generate textures with complex but natural
2.4.2. Functional Iteration Synthesis
Di Scipio [16] proposed an original pure synthesis
method to create chaotic sonic textures. Functional
Iteration Synthesis (FIS) is a derivative of the wave
terrain synthesis, where wave terrains are generated by
iterations of non-linear functions, which give them
extremely complex – quite chaotic - relief. The resulting
sonic textures present acoustic turbulences and are
closed to environmental sounds like rain or
thunderstorms. A digital musical instrument derived
from Di Scipio’s algorithm is presented in section 3.2.
2.5. Gesture and sensors typology
2.5.1. Gesture typologies
It is common sense to use a simple typology, which can
be simplified as follows [11] :
- selection gestures: change a preset
- decision gestures: trigger an event
- modulation gestures: specify a curve
- accompanying gesture: do nothing
As we shall see, most gestures are combinations of
different categories. For example crossing a plane can
be part of a modulation gesture, but the crossing itself
may be a decision gesture. Selection gestures and
decision gestures are associated when it comes to hit
certain zones and make a sound immediately.
Accompanying gestures, as the name indicates
accompany other gestures. It has been shown on
acoustic instruments [7] that such gestures are natural,
that they sometimes have an acoustic meaning but that
they can also only help the human performer who feels
happy with them. Such gestures are very important
when it comes to associate gestures and sounds: a
gesture oriented towards texture must be handy,
comfortable and in this sense it can provide some
extraneous information that only helps the performer to
feel at ease.
2.5.2. Sensor typology
Though many subdivisions can be made, the main
division is between
- contact sensors,
- free sensors.
In the first category, we may have all sensors added to
an acoustic instrument (“augmented instrument”) or
alternate devices, which are peripherals diverted from
their initial purpose. This is the case for tablets,
joysticks etc… which give a manual feedback or an
assisted one (force feedback). Free-motion sensors
allow the user/performer to produce non-constrained
gestures. They usually are based on video systems. We
also personally put the sensing of magnetic markers
within this category, when the weight is not too heavy.
Now we give two personal realisations at LMA and
UCL that show the difference between them.
2.5.3. Contact devices (tablets and joysticks)
At LMA we have been working on a project named
“creative gesture in computer music” where several
digital musical instruments have been designed using
digital tablets and/or joysticks. The precision of such
devices is great, they have been interfaced properly with
Max-MSP in order to be in the core of a digital musical
instrument. Constrained gestures help positioning a
point, make “similii-writing” gestures (such as vibrato).
The gestures are efficient, but not always beautiful or
demonstrative. In most cases it is important to retrieve
an intention from gesture in order to match it to an
Figure 3. Vibrato gesture with the right hand.
2.5.4. Video and Gesture Recognition (UCL)
For a long time, many researches have been followed in
image analysis and video segmentation fields, and
notably at UCL [13]. Several systems are thus now able
to extract from a video the position of hands, feet, facial
characteristics and many more features (Fig. 4). These
captured data are used as input parameters for several
types of application, among these the gesture
Figure 4. Example of data captured by a video segmentation
At the Communications laboratory (UCL), the
approach taken in research on gesture recognition is
oriented toward recognizing the intentions of the person
who performs the gestures, in order to analyze his or her
global behaviour during long periods of time. The goal
is about understanding the meaning and semantics of the
gesture rather than simply giving a name to a recognized
gesture. The treatment of position data is based on a
Probabilistic Finite State Machine, modelled by a
Dynamic Bayesian Network that is decomposed in
many levels, each level treating a different level of
abstraction, whereas the raw data form the lowest level.
This gives the model the capability to deal with simple
gestures made by single parts of the body, but also
complex gestures performed by the whole body and
over longer periods of time.
Utilization of gesture recognition knowledge in
gesture-based musical creation environment already
exists, especially in DIST-InfoMus lab (University of
Genova, Italy) [8] where research has been conducted
on the analysis of gesture expressiveness to drive
musical creation. The UCL novel approach could bring
a real additional value for applications in digital musical
instruments driven by free gestures analysis. Indeed, it
offers the possibility to build gestural control of such
instruments relying more on the semantics level of the
gestures rather than the syntactic one, as it is done until
2.6. Examples (not from the authors)
The sound synthesis techniques are quite old [30].
However very few has been implemented for the design
of musical instruments using sonic textures. The link
between texture and gesture is still a author-centered
decision. Human-Computer interaction has also made a
great use of “sonic icons”, some of them being textures,
and it is worth seeing how they can be used. However
we shall not present here a state of the art in these two
domains, but merely show some samples of what is
2.6.1. Digital music instruments and textures
Many peripherals can drive MIDI synthesisers, and of
course some presets yield sonic textures. The question
there is to know which parameters can be controlled in
real-time and what kind of mapping should be used. As
an example, the Meta-instrument [15] has all the
degrees of freedom to govern any sound, especially
synthetic textures or digital audio effects giving rise to
“The Hands from Michel Waiswisz is another
“classical” instrument now, at least in the hands of his
inventor, and a large use of sampling techniques (with
pitch shifting and time stretching or indexing) makes
sounds alive, even in a theatrical sense.
An instrument really dedicated to textures is the
“filtering string” designed by Couturier [2]. Here the
principle is to have a graphical object, namely a string
which has a dynamical behaviour (like a mushy series of
masses and springs) which is on one side related to
gesture (one applies forces via a 2D touch tablet) and on
the other one to sound (the shape of the string is applied
to an equaliser to filter a noise signal). Here we really
get into a musical concept, which is even enhanced by a
proper spatialisation [14].
Though many articles have been written on a possible
“granulation” of sound samples, instruments really
using such algorithms are few. Loic Kessous has
designed such an instrument named Arpgran [22] where
an excellent mapping between peripheral data and
parameters for analysis-synthesis allows a musical
feeling (Fig. 5).
Figure 5. Graphical interface for the Arpgan instrument
Dancers equipped with sensors (or video captured) are
subjected to experiments where the sonic soundscape is
either synthetic or natural. Video settings can also help
as devices that zoom. Travelling effects in soundscapes
which are more easily rendered when images
correspond to sounds (for example an artificial fire in
both the image and the sound).
The simulation of DJ scratching can be considered as a
texture-making [6] and devices simulating shakers of
every sort linked to grainy sounds such as the one
provided by the percolate toolkit (rain stick, shakers,
etc…) can be considered as textural instruments.
Many other examples can be given, but the goal, as
said before, is only to give a hint of the possibilities.
2.6.2 HCI and textures
Human Computer Interaction has shown the power of
sound, and especially of textures in some of its
The first one is the use of sonic icons. The relationship
between some actions and some sound is quite
straightforward: sounds are triggered by actions.
Some actions can also be “sonified” in another way.
For example scales around a computer window can be
sonorised, which can help blind people or users whose
the vision is already attached to a task [21].
Projects like Sound Object have shown the importance
of ecological relevance in the sonification of computer
processes, and the sound ball can be truly considered as
a musical instrument. An interesting aspect is the use of
textures (Fig. 6) in collaborative environments [25].
Though music is not the intended goal, the rumour
engendered by the throwing of virtual objects can be
part of a new ecology of sounds.
Figure 6. Surface scratching in MullerFelde’s thesis
Nevertheless, though the emotive part comes
sometimes in account (for example for alarms) the
semantic side is often bigger than the aesthetical part,
and music is a by-product rather than an essential part of
these HCI systems.
In this section we develop experiments we have
personally done using instrumental gestures to interact
with sonic textures. We shall see in this section the term
« ecological gestures », which describes familiar
gestures that people use in their daily life (though
writing or using hammers are ecological gestures though
they are learned). The term ecology is relevant, since it
has been used in acoustic ecology, and has been defined
in the context of perception by researchers such as
Gibson [18]. The term « ecological gestures » means
gestures properly linked to an anatomical comfort and a
proper cerebral effort. A good example of « gestes
écologiques » in HCI can be found in [24].
We now present some experiments the authors have
been conducting at LMA using simple synthesis
algorithms written in Max-MSP.
3.1. Examples (LMA I)
3.1.1. Using filtered noise
An interesting class of sounds comes from the filtering
of a noise source. The reason for this is that we have at
least two sorts of sounds that are easily mimicked by
such source-filter algorithm: windy sounds, and
whispering. Analog music has done a great use of noise
generators and voltage controlled filters, so that we are
really prepared for the sonic experience. But very few
experiments have tried to link such sounds to gestures.
We use a Max-MSP patch, and a Max Mathews’s
drum (also called radio-baton), which has the advantage
of providing x,y,z in terms of Midi codes. Using the
sound to gesture strategy, one has to invent gestures that
can “symbolize” the sound we want to hear. Here are
three different uses of the same algorithm, with different
gesture strategies.
The gesture in the first instrument is a combination of
a decision gesture and a modulation gesture: the
initiation of the sound comes when a baton hits the
surface. The x position of the hit point determines some
parameters of the filters. The way the sound is generated
depends upon the y position, which acts as an index in
different tables, including the amplitude function. The
way the sound ends depends upon the gesture. This
gesture is very intuitive, because in fact we very rapidly
use the “percussion-resonance” mental scheme. If we
have a good combination of filtering values, one can
have a musical instrument tuned to certain frequencies,
for example harmonics of a drone. This is the way is has
been employed in the real time version of “le Souffle du
doux” (Fig.7).
Figure 7. Start and unwrapping of a filtered noise
3.1.2. Breathing gesture
Here we find another metaphor: breathing is the
alternation of two windy sounds, one for inhaling and
one for exhaling. One sound is linked to the right hand,
and the other to the left hand. We rediscover here what
are accompanying gestures: not everything is important
for the control, in fact we can even trigger the sound
when the y coordinate crosses a line (with a special
hysteresis algorithm in order not to retrigger the sound
due to some jitter of the gesture sensing) (Fig.8).
By subtly varying the coefficients of the filters, one
can give the impression of a soft or deep breathing.
Ideally this could be put as an additional value assigned
to a controller (e.g. a foot pedal). However in the
musical configuration it was meant for, a counter was
incremented each time an alternation was done.
Figure 8. Alternative gestures for a breathing effect
Though the mapping may look very primitive, the
gestures are very natural and one really has the
impression of being part of a sonic process
3.1.3. Metaphor of the Demiurge (prince of the wind)
Figure 9. 3D exploration of sonic textures
Here we are in a 3D space where amplitude, central
frequency and bandwidth are directly mapped to the
x,y,z coordinates (Fig.9). Two sticks are used with the
same algorithms. Strangely enough, this instrument
immediately creates a “pedagogy” of gestures:
trajectories are found that express different feelings, or
expressions of sounds. One is really a creator”, hence
the metaphor of the Demiurge,
3.1.4. Drone textures and stick gestures
A drone sound is created by adding three oscillators
with very different values that are waveshaped in a
specific way. This gives a choir effect on a simple
sound, so the harmonics are beating in a kind of
anarchic way. The mapping itself uses the vertical
position as an index for distortion (the closer the stick,
the more distorted the sound). The horizontal position of
the other stick is directly linked to the frequency
discrepancy between the oscillators (Fig. 10).
Figure 10. A 3D movement, but only one coordinate is
used for each stick
It is very interesting to note that, although the vertical
coordinate only is linked to the sonic process, one feels
the need to play in 3D. This can be explained by the fact
that a sine (or cosine) is only the projection of a circle,
and that a circle may be more « ecological » than a hand
oscillation when it comes to slow frequencies. Once
again the sonic feedback immediately tells the
performer the good regions for the two sticks. Initially
devised for the proper imitation of an electroacoustic
piece, the device incites to exploit the sonic material and
reinvent new curves, new ways of playing. This is well
known, but a musical instrument is not only a gestural
control of a process, it is an « sonically output oriented »
loop, the goal is to make a sound, and the rest is part of
the loop.
3.2 Examples (LMA II) : Textures scratcher
In this section, we describe another digital musical
instrument recently developed at the LMA. Unlike the
previous instruments that were based on a noise filtering
approach, the “texture scratcher” does not rely on
source-filter model but rather acts directly on the source
itself in order to create “chaotic textures”. Following the
typology proposed by Hanna [19], sounds produced by
this instrument would belong either to the “pseudo-
periodic noise” or the “impulsive noise” class according
the mode chosen.
This digital instrument is based on the gesturalized
exploration of a visual space. It consists of a real-time
adaptation of the Functional Iteration Synthesis (FIS)
[16] implemented with Max/MSP driven by an
advanced gestural control using a graphical tablet and a
joystick. FIS is a special case of wave terrain synthesis
where terrains are generated by iteration of non-linear
functions. This section is divided in three parts: first part
introduces the original algorithm proposed by Di Scipio
to create sonic textures from fractal wave terrains. The
second part describes our implementation, and
especially highlights the two mapping strategies we
have developed for the exploration of the terrains.
Finally, considerations on musical applications and
future research direction for this instrument are evoked
in the last part.
3.2.1 Creation of the wa ve terrains by Functional Iteration
Functional Iteration Synthesis (FIS) is a part of the wide
class of wave terrain synthesis (WTS) [29], where the
sound waveform corresponds to an orbit traced on a
three-dimensional surface (the wave terrain).
Characteristics of sounds produced with this type of
synthesis techniques depend on both terrain properties
and orbit velocity.
In Functional Iteration Synthesis, Di Scipio proposes to
use fractal images as wave terrain to take advantage of
their very dense and complex relief. In this intention, he
suggests building terrains by iteration of non-linear
functions, and takes the example, that we have followed
in our instrument, of iteration of sinus function.
Given (x,y,zn) the coordinates of the points composing
the n-th wave terrain and Ix and Iy the definition domains
of x and y respectively. The creation of the initial wave
terrain is achieved by the following expression, where
the elevation z0 of each point is computed from its two
other coordinates x and y:
z0 (x,y) = sin(x*y) = f0(x,y) (1)
with x Ix, y Iy and z0 [-1;1]
Next terrains are then calculated from (1) by an
iterative process:
z1 (x,y) = sin (x*z0) = sin (x*(sin(x*y)) = f1(x,y)
z2 (x,y) = sin (x*z1) = sin (x*(sin (x*(sin(x*y))) = f2(x,y)
until the n-th terrain, corresponding to the n-th
iteration :
zn (x,y) = sin (x*zn-1)= fn(x,y) (2)
Sound signal s(t) is finally obtained by tracing an orbit
on the n-th terrain, that is done by varying (x,y)
according to time in (2) :
s(t) = zn(x(t), y(t)) (3)
Three parameters, namely the definition domains Ix and
Iy for both x and y dimensions and the number of
iterations achieved n, are necessary to define a wave
terrain. Typical values used in our instrument are Ix=[-
π/2, π/2], Iy=[1,4], and n<10.
Wave terrains can be represented either in two or three
dimensions (Fig. 11), the third coordinate (elevation z)
being symbolized in grey level in 2D image. For the
visualization of the terrain in our instrument we have
chosen the 2D representation, more usable for very
dense relief.
Figure 11. Two representations of the same wave terrain in 2
and 3 dimensions (x [-π/2; π/2], y [2 ; 4], n= 3).
As illustrated in Fig.12, each new iteration provides a
new wave terrain with more and more complex relief.
Also this technique allows creating very complex
terrains, potentially worthwhile for wave terrain
synthesis, by a quite simple process involving a limited
number of specified parameters.
Figure 12. Representations of the 5th and 7th iteration (x [-
π/2; π/2], y [3 ; 4]). The larger the number o f iteration is, the
more complex the terrain becomes.
In our instrument, wave terrains are computed and
displayed in real-time thanks to Jitter, an additional
library of objects enabling manipulation of matrix data
and dedicated to image processing in Max/MSP. The
terrains are displayed on an “interactive pen display”;
developed by Wacom, this is an improved 15’’ graphical
tablet that integrates display functionalities. Thus, we
use this “tablet-screen” to make the user able to trace
orbit directly on the image of terrain (Fig. 14).
3.2.2. Generation of the trajectories
From the algorithm proposed by Di Scipio, we have built
a digital musical instrument by adding real-time user-
interaction based on an advanced gestural control. The
gestural control we built for this instrument is inspired
by the metaphor of a surface scratching, and it allows the
user to control the trajectories traced upon the terrain.
Two modes of exploration of the terrain are possible
(Fig. 13), either by means of linear trajectories (« direct
mode ») or looping trajectories (« parametric mode »).
Figure 13. Examples of orbits generated by direct control
(linear orbit, above) and parametric control (looping orbit,
a- Direct mode
In direct mode, the orbit corresponds to the actual
movement drawn by the user on the tablet as if he or she
were scrubbing a « sonic surface ». This mode is based
on a uni-manual gestural control. Practically the user
traces a trajectory with a stylet on the tablet where the
2D image of a wave terrain is displayed (Fig. 14); every
30 ms, a pair of coordinates (x,y) corresponding the
position of the stylet on the tablet is captured, rescaled to
the terrain dimensions, and transmitted to Max-MSP. A
44100 Hz sampled trajectory is then generated in
Max/MSP by linear interpolation between two
successive captured positions.
The way we generate sound waveform from orbit and
terrain data is quite different from in traditional wave
terrain synthesis techniques: instead of reading values in
a bidimensional table associated to the terrain, we
instantaneously compute each sample of the sound signal
from the expression (3) for each couple (x,y) constituting
the orbit. This is made possible because we know the
mathematical expression defining the terrain. By this
way our approach is much closer to a waveshaping
synthesis method [1], than a traditional wave terrain
synthesis method. In waveshaping synthesis, the output
signal is the result of a function f applied to the result of
another function g; we can also consider our method as a
particular case of “iterative waveshaping”, and in this
approach, the terrain as a graphical representation of the
iterated waveshaping function. This induces an
interesting property on the synthesized sound: indeed,
according non-linear distorsion synthesis theory, the
distorsion of a sinusoid by a k-order polynomial gives a
k-order harmonic signal. In the case of an “iterative
distorsion”, the signal presents after n iterations a kn
order harmonic structure. Consequently, even after a
small number of iterations, spectral structure of sounds
produced by this algorithm will be characterized by a lot
of foldover components. These foldover components
give a very peculiar “crunchy” character to the sonic
Figure 14. Unimanual gestural control used in direct mode
In the direct mode, spectral features of the textures are
directly linked to the hand motion: slow gestures will
create low frequency textures with a pseudo-rhythmic
structure, whereas faster gestures will enlarge the
spectral content of the sound and induce lot of spectral
aliasing to render chaotic textures.
b- Parametric mode
Parametric mode differs from direct mode by the fact
that terrains are no longer explored by linear orbits but
by rather circular/elliptic orbits that loop on themselves
at a controllable frequency (Fig.13). The user governs
the overall position of the circle on the terrain by moving
its center and varying its radius thanks to an additional
peripheral (a joystick). This mode is called parametric
because the generation of the trajectories is not anymore
done directly but by means of control parameters.
In this mode, coordinates (x,y) of the points
composing the orbit are computed in Max/MSP from the
parametric equation of a circle :
x=α+R*cos(ωt) (4)
where R is the radius and (α,β) the coordinates of the
center of the circle. (ωt) is governed by a saw tooth
function varying between 0 et 2π to move periodically
around the circle. The velocity of the orbit is also
directly dependent on the frequency f of this sawtooth
function. The sound signal is then obtained from the
expression (3) of elevation z for each couple (x,y)
calculated with circle equation (4). A similar process
makes it possible to generate elliptic orbits.
A bimanual gestural control allows the user to move
upon the terrain by varying each parameter used for the
construction of the trajectory: the coordinates (α,β) of
the center of the orbit are given by the position of the
pen on the tablet, whereas the radius R and the velocity
(by the way of frequency f) are increased or decreased
by front/back and twist movement of a joystick
respectively (Fig.15).
Figure 15. Bimanual gestural control used in parametric mode
3.2.4. Evaluation and conclusion
The mappings associated to each mode are of different
natures: in the first case, a direct link exists between the
hand gesture and the position on the terrain; this
mapping relies on an ecological “innate” gesture
(surface scratching). The utilization of the joystick in a
“assistant gesture” in the second mapping adds an
intermediate layer between hand motion and the
construction of the trajectory, that then becomes
Sonic textures obtained by both modes have a very
peculiar “chaotic” character, especially due their many
foldover components. The parametric mode allows
creating different textures than the direct mode,
especially pseudo-pitched textures when frequency f is
in audible range. These are close to certain machine-
noise sounds and well suited to render very nervous and
turbulent sonic ambiances. For further musical
applications, using these sounds as source in a filtering
stage could give interesting results in creating “coloured
chaotic textures”. This is one direction we shall follow
in future research.
3.3 Discussion
Our way to link gesture and sound has been the
following: instead of using devices (computer
peripherals) and asking what can we do with them, we
prefer to think about sounds and ask ourselves: what
kind of gesture would be best to produce this sound with
a new digital musical instrument. This does not mean at
all that we will necessarily try to dance or mimic an
existing instrument, but rather that we will make a
compromise between a natural gesture and an educated
gesture the gesture itself will have to provide, through a
specific mapping- all the values for sound definition. A
natural gesture is what “comes into mind” whenever you
listen to a sound. An educated gesture is a gesture you
can learn, reproduce, vary around it so that the
instrument becomes a “gesture-controlled audio system”
and not a “gestural control of an audio-system”. In terms
of ergonomics and cognitive processes, this implies that
the tool itself is incorporated in the physical and mental
body of the performer.
Musical instruments usually rely on specific
constraints that the material and the construction
provide. This is the part of intervention of the “lutherie”
in the sonic process. This means for example that in
order to get a vibrato on a string one must make the
finger oscillate. On a Theremin, vibrato is obtained by
the vertical variation of height of the hand. On a digital
music instrument such as the photosonic emulator, it is
an oscillating scratching gesture that plays this role.
Sonic textures are however very specific in the way
they can be part of such devices.
a- Sonic textures are rarely based upon the attack-
sustain-release scheme of ordinary sounds. They are
more like sound masses, where the attack part is
diffused all over the sound and replaced by successive
transients. Another specificity of textures is a loose
importance of pitch, except for specific textures. More
generally, control parameters are ambient rather than
descriptive. Macroforms and microforms are sometimes
indistinguishable. For example the sound of a river can
be seen as the succession of events or as a simple
granulation of a sound flow.
To see the difference between macroforms and
microforms, let us consider the difference between a
conductor’s gesture which immediately draws a gestural
sketch of the whole sound, while the way the
percussionist carefully chooses the place where to strike,
and the force he/she will use is typical of a
meso/microlevel. Even more micro is the way one uses
an analog synthesizer to control all the parts of a sound
with the help of knobs. This clearly shows that there are
choices to be made and they sometimes come from the
way the sound is processed.
b- Sonic textures depend on algorithms which are
somewhat different from classical sound. In a source-
filter approximation, we can see that the source itself is
a succession of different accidents and the time
specification of possible transients versus noise is
critical. The modelisation of a proper source is
fundamental. The spectral domain is very important too
as it will impart textures with their real musical
meaning. It is extremely important to have a good
relationship (mapping) between the gesture and the
spectral control.
There is not a unique solution for the gestures that we
can use. Manipulating a white noise through a filter can
lead to diverse instruments because of the structure we
superimpose to the sonic calculation. As an example
sonic objects may either be finite short entities or a long
unique event where the matter of sound is molded.
Gestures associated with either will be of course
different. Even with the same type of sounds, we can
choose for example to link the time axis to a dimension
of gesture (one unwinds the sound) or not (once an
event is triggered, it will proceed to its end and one can
only change other parameters)
Other choices can also lead to a bimanual gesture,
where two component of the sound rendering can be
associated to two concomitant gestures. This is perfectly
illustrated by the “voicer [22], a non-textural
instrument, but for sure this can give some ideas about
the link between a source and a filter.
c- The main specificity of sonic textures is the
symbolic level it makes reference to: most of these
sounds can be called ecological, and the gestures
associated to them will require to be very close to
natural gestures such as scratching, defence-attack
movements, motions in the air. They usually have a
dynamic structure which needs to be recognized and
matched with specific gestures. This is also why they
can be used in choreographic applications
Gestures may be redundant, and it is in this sense that
they can become “ecological”. As an example a free 3D
gesture using a Max-drum is easier, and more sensitive,
while only the height of the tip versus the surface is
considered in the sonic process. But ecological gestures
also have a symbolic correspondence which makes them
vivid. As an example the manipulation of a sword is an
ecological gesture, and it still works without any enemy
(but an obstacle, real or virtual, is needed for striking
sounds). When using such gestures, we come to another
field, which is the one of emotion: many gestures are
connoted with an emotion. Rocking a cradle is not an
innocent gesture. In fact we enter a domain where other
arts have set markers: theatre and dance use gestures
charged with emotion, be they symbolic or not.
“Gracious gestures”, whatever the style, are indeed
ecological gesture because usually they minimize some
jerking, so as to have a smooth side. Contemporary
dance sometimes tries to get out from this but for sure
gestures remain gesture and it is the overall structure
that can become anarchic, not the gestures themselves.
Gesture is not gesticulation. Gesture is constrained by
two things: the first one is the feasibility of this gesture,
and this constraint is linked with ergonomy research.
The second one is that the gesture is linked with the
sonic result, and it is aesthetically constrained. The
definition of a domain and trajectories inside the domain
is a quite unexplored field and it has much to do with
the double meaning of a musical gesture: it is an action
and perception movement.
Although many musical experiments use gestures and
textures, there is no real state of art of this powerful
combination or alliance. While it has brought up some
experiments done in this field, this article is not a state
for art either, and this is a good perspective for the
Finally, the evaluation of such links between gesture
and texture must not let us forget an important thing:
music is an intense process, not limited to algorithms
and mappings; there is an intense implication of
emotion, and one always has to remember that textures
are not merely “soup music” but can be an awakening of
the senses. This means that a musical point of view
always must be the guardian of computer sonic research.
Authors would like to thank Kosta Gaitanis and Pedro
Correa, PhD students at Communications laboratory in
UCL, for their help on the writings of gesture
recognition part. Many of the ideas concerning digital
musical instruments would not have risen without the
help of Jean-Michel Couturier, Loic Kessous and
Vincent Verfaille who successfully passed their PhD at
LMA in the two last years. Many thanks to all the
ConGAS delegates of COST287 action for their helpful
conversations and comments.
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... Non-standard synthesis methods, such as fractal synthesis or chaotic maps, generated by iterating nonlinear functions, are used most often for expressive texture synthesis [17, 32], especially when controlled by gestural input devices [3, 31]. ...
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A technique for the synthesis of complex dynamic spectra is described. The first step in the synthesis method is to distort sine waves with nonlinear transfer functions. The resulting spectra depend upon the input amplitudes and the nature of the distortions. By multiplying one such distorted source by another a spectrum is obtained that is the convolution of the spectra of the distorted sine waves. By varying the amplitudes of the input sine waves one can produce complex spectral evolutions. Harmonic as well as inharmonic spectra can be produced, and control over the formant structure is provided. With one distorted and one pure wave, results similar to those of J. M. Chowning's frequency-modulated technique are produced. With several distorted signals very complex spectral evolutions are possible. Various implementations using a computer or special-purpose digital device are discussed.