Development of a System to Generate Artificial Ambiance
based on Entropy
Hidefumi Ohmura
1
, Takuro Shibayama
2,3
, Masahide Yuasa
4
, Takayuki Hamano
5
and Ryu Nakagawa
6
1
Department of Information Sciences, Tokyo University of Science, Yamazaki 2641, Noda-shi, Chiba, Japan
2
Department of Information Systems and Design, Tokyo Denki University, Ishizaka, Hatoyama-cho, Hikigun, Saitama,
Japan
3
Institute for Music and Acoustics, ZKM, Lorenzstraße 19, D-76135, Karlsruhe, Germany
4
Department of Applied Computer Sciences, Shonan Institute of Technology, Tsujido Nishikaigan 1–1–25, Fujisawa-shi,
Kanagawa, Japan
5
Department of Inter media Art, Tokyo University of the Arts, 5000 Omonma, Toride, Ibaraki, Japan
6
Graduate School of Design and Architecture, Nagoya City University, Chikusa-ku Kitachikusa, 2–1–10, Nagoya-shi, Aichi,
Japan
Keywords:
Ambience, Mood, Emotion, Entropy.
Abstract:
Ambience is an abstract concept of sensory information. If we can control sensory information, then we will
be able to somehow provide ambience as needed. We developed a controllable ambience model using the
entropy of distribution functions based on this hypothesis. We adapted the model to several sensory modes,
such as facial expressions, tones, and unobjective figures written in HTML and JavaScript available on any
browser. We introduce these systems in this paper.
1 INTRODUCTION
In human communities, there are concepts or words
whose meanings are ambiguous, such as ambience,
emotion, mood, and feeling. But despite their ambi-
guity, we somehow understand them. These words
are used on a daily basis but are defined academically
in some fields. For example, in psychology, emotion
is defined as rapid mental changes based on obvious
causes. On the other hand, mood is defined as slow
mental changes based on vague causes. Some people
may feel that there are differences between the con-
ventional and academic meanings of words. There-
fore, engineering systems with these academic mean-
ings may not work well for human communities. To
address this problem, we try to define these concepts
quantitatively in order to maintain their ambiguity.
When the attempt is realized, human-centric applica-
tions will have potential for growth. In this paper, we
focus “ambience” in particular.
We first discuss ambience from some perspective.
Based on these perspectives, we define “ambience”
quantitatively and propose a model for creating artifi-
cial ambiences. Finally, we demonstrate and discuss
applications of the model.
2 DISCUSSION ABOUT
AMBIENCE
2.1 What is Ambience
Ambience is a word with an abstract meaning. Ac-
cording the Oxford Advanced Leaner’s Dictionary,
ambience is “the character and atmosphere of a
place. Atmosphere is “the feeling or mood that you
have in a particular place or situation; a feeling be-
tween two people or in a group of people. Feeling is
“something that you feel through the mind or through
the senses, and mood is “the way a group of people
feel about something; the atmosphere in a place or
among a group of people. In these sentences, though
we can only perceive that an abstract something con-
trols ambience, we understand that ambience is an in-
terhuman something. Thus, ambience can be consid-
ered as the degree of coincidence of human condi-
tions.
On the other hand, Japanese critic Yamamoto dis-
cussed air (Yamamoto, 1983), which is similar to am-
bience, compared towater, which is similar to a power
of a changing the ambience, during World War II in
Ohmura, H., Shibayama, T., Yuasa, M., Hamano, T. and Nakagawa, R.
Development of a System to Generate Artificial Ambiance based on Entropy.
DOI: 10.5220/0006730103330338
In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) - Volume 1, pages 333-338
ISBN: 978-989-758-275-2
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
333
Japan. Members of a community create air. However,
when an authority pours cold water on the air, this
breaks up the air suddenly and creates new air. In his
opinion, we can see that air (ambience) is affected by
the degree of coincidence of members’ something.
2.2 Ambience of Music
As mentioned above, we discussed ambiences as in-
volving two or more people. Here, let us focus ambi-
ence that consists of another medium—music (Lanza,
2004).
In 1934, background music (BGM), referred to as
Muzak, was broadcasted to create a mood or ambi-
ence. BGM is also called elevator music. In the past,
elevators were considered dangerous conveyances
similar to airplanes and roller coasters. Therefore,
BGM was played in elevators to ease passengers’
fears and tension. Music was played not just in el-
evators but also other environments, such as factories,
stores, and so on.
In addition, various musicians created music for
specific environments. For example, Erik Satie com-
posed “furniture music” in 1920. He created music
that was meant to remain in the background, just like
furniture in rooms. Needless to say, the purpose of
this task was to create and control ambience.
Brian Eno proposed “ambient music” as a musical
genre and composed “Music for Airports” in 1978.
This music melts into the background of airports and
eases the tension felt by passengers before getting on
airplanes.
As shown in the example, there are many chal-
lenges in creating and controlling ambience using mu-
sic. However, there is no well-established methodol-
ogy for creating ambience with music.
2.3 Ambience as an Aggregate of Senses
Ambience depends on various human perceptions.
Ambience is created by both single and multiple
modes of perception. In the previous examples,
modes that perceive the dispositions of members as
well as sounds create ambiences. To give a further
example, the ambience of a painting is created by the
relationships between colors, compositions of objects
in the painting, and/or the artist.
In this study, we define ambience as an aggregate
of the various aspects of human perception.
2.4 Ambience as Relativity
Ambience is created by the presence or absence of
perception. For example, a situation in which mem-
bers conform to something creates ambience. If a
member opposes this, the situation changes and a new
ambience is created. The reason is because “rela-
tivity” between agreement and disagreement creates
different situations, and members can understand the
difference. Humans are sensitive to change, which
is evident in their physiology. This phenomenon
is observed in electroencephalography (EEG) called
mismatch negativity (MMN) (N¨a¨at¨anen et al., 1978).
EEG is electrical recorded signals in the brain. An
event related potential (ERP) is a particular EEG re-
sponse to a stimulus event. A listener is kept stimu-
lated by sound (e.g., 400 Hz tone for 1 s), and then
abruptly stimulated by another sound (e.g., 600 Hz
tone for 1 s). Comparing the ERPs for the two stim-
uli, we find differences at 300 ms after the onset of
the stimulus. This difference, i.e., MMN, shows that
humans continuously search for perceptual deviations
in the environment at both the conscious and uncon-
scious levels.
Although there is relativity of modes, i.e., between
pitches in this example, relativities between modes
create ambience. Let us consider two modes: im-
age and music. Multimedia that consists of a sad im-
age and sad music creates a sad ambience because of
the consistency between modes. On the other hand,
multimedia that consists of a sad image and happy
music creates a curious ambience because of the in-
consistency between modes. Moreover, the relativity
between perceptual modes in music affects ambience
(Ohmura et al., 2013). Complexities of perceptions
in rhythm and melody may be divided into different
musical genres. When both rhythmic and melodic
perceptions are simple, the music sounds like a chil-
dren’s song or nursery rhythm. When rhythmic per-
ception is simple and melodic perception is complex,
the music sounds like jazz. When both rhythmic and
melodic perceptions are complex, the music sounds
like contemporary music. As we have mentioned, we
consider how changes in perceptual mode affect am-
bience.
In this study, we consider that humans perceive
using various senses and pick up ambience from tem-
poral changes and relativities.
3 COMPUTATIONAL AMBIENCE
3.1 Ambience as Deviation
It is said that deviation is one of the important el-
ements of emotion, from theoretical and empirical
points of view. In a musical study, Meyer pointed
out that deviations of expectation arouse emotions
HAMT 2018 - Special Session on Human-centric Applications of Multi-agent Technologies
334
!"#$%&'()*
+&,"-(./01%2&
Figure 1: The optimal complexity model (modified from
(Berlyne, 1970)).
(Meyer, 1956), based on Dewey’s theory that empha-
sizes the conflict or opposition of tendencies (Dewey,
1894). Continuing Meyer’s theory, Narmour de-
fined the relation between expectation and its real-
ization/deviation as the implication-realization theory
(Narmour, 1990). Moreover, Huron proposed an ex-
tended theory known as ITPRA (Huron, 2006).
Although these findings came from musical study,
when emotional states are regarded as ambiences,
then ambiences depend on deviations.
3.2 Complexity and Ambience
Deviation from an expectation occurs from disappear-
ing or decreasing patterns which are created by hu-
mans. These situations are regarded as increasing
uncertainty and complexity. With regard to com-
plexity, Berlyne proposed the optimized complexity
model (Berlyne, 1970), which shows the relationship
between hedonic emotion and complexity (1). This
relationship is expressed by an inverse U function (x-
axis: complexity, y-axis: hedonic value).
Putting aside the difference between positive and
negative, stimuli that elicit ambiences can be con-
trolled and created by adjusting complexity.
3.3 Entropy and the Information
Theory
An environment where deviations from expectations
occur frequently is regarded as high uncertainty,
and such an environment can be expressed quantita-
tively using information theory (Shannon and Weaver,
1949). Information theory calculates how communi-
cation acts as a function of information.
When an event i occurs, the amount of information
I is defined as
I = log p
i
(1)
where p
i
is the probability of event i. When p
i
de-
creases, I increases. That is, the amount of informa-
tion increases. When n events occur with the proba-
bilities p
1
, p
2
, ..., p
n
, the expected values are calcu-
lated as
H =
n
i=1
p
i
log p
i
(2)
The value H represents the number of times informa-
tion is delivered, i.e., the degree of uncertainty and
complexity. H is the entropy or average amount of
information.
Therefore, by controlling entropy we can create
frequency of deviations, i.e., control artificial ambi-
ences.
3.4 Meaning and Ambience
In information theory, there are no meanings of
events. For example, giventwo persons and two situa-
tions: in the first situation, one person is happy while
the other one is angry; in the second situation, one
person is laughing while the other one is smiling. The
entropies of these two situations have the same val-
ues. That is, information theory dose not distinguish
between “laughing, “smiling, and “angry, and re-
gards them as independent events. In ambience, the
relationships between events are important because
the two examples aboveare not same ambience. In or-
der to distinguish these, we defined the relationships
between events computationally.
Some information on perceptions were defined
from psychological considerations or physiological
features. For example, the relationship of emotion
was proposed as a circumplex model by psycholog-
ical consideration (Russell, 1980). The relationship
of musical notes in music was defined as twelve tones
by physiological features.
When the relationships between some events are
not defined, the designer of the ambience must define
the relationships between them.
In this study, we develop generative artificial am-
bience systems based on these considerations.
4 GENERATIVE ARTIFICIAL
AMBIENCE SYSTEMS
4.1 Method for Artificial Ambience
We proposegenerativeartificial ambience systems us-
ing one or more perceptual modes. Basically, we de-
fined quantitative relationships and gave each event
Development of a System to Generate Artificial Ambiance based on Entropy
335
a probability using functions. Functions are defined
based on the normal distribution for these systems. A
number of social and natural phenomena follow nor-
mal distributions; thus, we adopted the normal distri-
bution for the system. We can calculate the probabil-
ities of events using the Gaussian function based on
the normal distribution. The x-axis represents the re-
lationships between events. The y-axis represents the
scale of probabilities. The Gaussian function is repre-
sented as
f(x) =
1
2πσ
2
exp
(xµ)
2
2σ
2
(3)
This equation describes the normal distribution with
average µ and variance σ
2
. Setting each event to x,
each probability is calculated from µ and σ.
Adjusting the variance σ
2
of the Gaussian func-
tion, users can control the edge shape of the function.
Upon decreasing the variance σ
2
, the shape of the
function becomes flat, resembling a uniform distribu-
tion. In this state, the entropy reaches a maximum
that prevents the user from predicting the next event.
Upon increasing the variance σ
2
, the function peaks.
In this state, the entropy reaches a minimum, allowing
the user to predict the next event easily. Using Gaus-
sian function, we can treat σ
2
like entropy. We imple-
mented the method using HTML and JavaScript
1
.
We describe the details of the systems in the next
section
4.2 Generative Ambience System using
Simple Facial Expressions
We adopted simple facial expressions for the sys-
tem. These facial expressions consist of figures of
the eyes and mouth. Although the faces are simple,
they can express emotions. The relationship between
emotions was defined as a circumplex model (Rus-
sell, 1980). In this model, emotions were mapped
in two-dimensional space with arousal and valence
as the positive and negative axis, respectively. We
adopted figures of facial expressions as relationships
in the two-dimensional emotional space in this sys-
tem, as shown in figure 2.
In this system (figure 3), when users push the play
button, each face in the 7 × 7 matrix starts to change
depending on its probability at arbitrary time inter-
vals. The y-axis of the distribution function means
circumplex, and both ends can connect. Arbitrary
time intervals are set with Interval slider.
1
http://sites.google.com/site/hidefumiohmura/home/
program/icaart2018
!"##$!
%&''&() *"+,)
-./'$!
%+00#)
-'&12"+!
3+0"2".4!5.#+0"2".4)
Figure 2: Relationships between simple figures of facial ex-
pressions.
Figure 3: Generative ambience system using simple facial
expressions.
4.3 Generative Ambience System using
Sounds
We adopted sine waves as tones to the system. We
adopted the physical ratio of frequencyas the relation-
ship. The relationships of double (2:1) or half (1:2)
in frequency are called octaves, the relationships of
3:2 or 3:4 are called the perfect fifth, and the relation-
ships of 4:3 or 2:3 are called the perfect fourth. Using
these relationships and ignoring octaves, we can get
twelve notes as a circumplex model. The relationship
is called the circle of fifths or fourths. Clockwise ro-
tation means 3/2 × n, anti-clockwise rotation means
2/3 × n, where n is an arbitrary frequency. In this
system, the range of frequency is in four octaves.
When a user pushes the play button (see figure 4),
the system produces sound that depends on the model
at arbitrary time intervals. Arbitrary time intervals are
set with Interval slider. Note that the values are set
with Value slider.
HAMT 2018 - Special Session on Human-centric Applications of Multi-agent Technologies
336
Figure 4: Generative ambience system using sounds.
!"!
#!"!
$%&"!
'&"!
Figure 5: Relationships between abstract figures.
4.4 Generative Ambience System using
Abstract Figures
In order to get rid of meaning, we adopted abstract
figures to the system (figure 6). Basically, this system
is similar to the system using simple facial expres-
sions. However, the figures are lines in circles that do
not express meaning. The relationships between these
figures are angles (see figure 5).
Figure 6: Generative ambience system using abstract fig-
ures.
Figure 7: Generative ambience system using sounds and
abstract figures.
4.5 Generative Ambience System using
Sounds and Abstract Figures
In order to express multiple modes, we adopted
both sounds and abstract figures to the system (fig-
ure 7). This system creates ambience based on
the comparison between entropies of sounds and ab-
stract figures, and generates them synchronously or
asynchronously. Abstract figures: lines are con-
trolled by three modes—angle, x-position, and y-
position. Sounds: pitches are controlled by a percep-
tual mode—pitches.
4.6 Discussion
Using each system, we find that ambiences change
depending on the entropies. The system using sim-
ple facial expressions generates concrete meanings.
However, when the entropy is high, we cannot un-
derstand the concrete meanings. We really consider
that phenomenon as ambience. The other systems do
not have concrete meanings. Therefore, in order to
understand ambience, we need to compare other out-
puts. It is not clear how concrete/abstract meanings
affect ambience. It will be interesting to determine
what kinds of ambience other distribution functions
create. We plan to conduct experiments on these in
the future.
The elements of these systems, such as sounds
and figures, are simple. When we adopt complex ele-
ments, such as agents to the system, we may develop
a more interesting system. We will try this in future
work for a multi-agent system.
Development of a System to Generate Artificial Ambiance based on Entropy
337
5 CONCLUSION
In this study, we defined ambience as an aggregate
of various senses of human perception. We devel-
oped generative ambience systems using these rela-
tionships and a normal distribution based on informa-
tion entropy. This system created ambience with mul-
timodal perception, such as sound, faces, and figures.
We plan to conduct experiments for the various ambi-
ences in the future.
ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant
Numbers JP17K12808, JP16H01744, JP15H03175.
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