BREAKING ACCESSIBILITY BARRIERS
Computational Intelligence in Music Processing for Blind People
Wladyslaw Homenda
Faculty of Mathematics and Information Science
Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland
Keywords:
Knowledge processing, music representation, music processing, accessibility, blindness.
Abstract:
A discussion on involvement of knowledge based methods in implementation of user friendly computer pro-
grams for disabled people is the goal of this paper. The paper presents a concept of a computer program that
is aimed to aid blind people dealing with music and music notation. The concept is solely based on com-
putational intelligence methods involved in implementation of the computer program. The program is build
around two research fields: information acquisition and knowledge representation and processing which are
still research and technology challenges. Information acquisition module is used for recognizing printed music
notation and storing acquired information in computer memory. This module is a kind of the paper-to-memory
data flow technology. Acquired music information stored in computer memory is then subjected to mining im-
plicit relations between music data, to creating a space of music information and then to manipulating music
information. Storing and manipulating music information is firmly based on knowledge processing methods.
The program described in this paper involves techniques of pattern recognition and knowledge representation
as well as contemporary programming technologies. It is designed for blind people: music teachers, students,
hobbyists, musicians.
1 INTRODUCTION
In this paper we attempt to study an application of
methods of computational intelligence in the real life
computer program that is supposed to handle music
information and to provide an access for disabled peo-
ple, for blind people in our case. The term com-
putational intelligence, though widely used by com-
puter researchers, has neither a common definition
nor it is uniquely understood by the academic com-
munity. However, it is not our aim to provoke a dis-
cussion on what artificial intelligence is and which
methods it does embed. Instead, we rather use the
term in a common sense. In this sense intuitively un-
derstood knowledge representation and processing is
a main feature of it. Enormous development of com-
puter hardware over past decades has enabled bring-
ing computers as tools interacting with human part-
ners in an intelligent way. This required, of course,
the use of methods that firmly belong to the domain of
computational intelligence and widely apply knowl-
edge processing.
Allowing disabled people to use computer facili-
ties is an important social aspect of software and hard-
ware development. Disabled people are faced prob-
lems specific to their infirmities. Such problems have
been considered by hardware and software produc-
ers. Most important operating systems include in-
tegrated accessibility options and technologies. For
instance, Microsoft Windows includes Active Acces-
sibility techniques, Apple MacOS has Universal Ac-
cess tools, Linux brings Gnome Assistive Technol-
ogy. These technologies support disabled people and,
also, provide tools for programmers. They also stim-
ulate software producers to support accessibility op-
tions in created software. Specifically, if a computer
program satisfies necessary cooperation criteria with
a given accessibility technology, it becomes useful for
disabled people.
In the age of information revolution development
of software tools for disabled people is far inadequate
to necessities. The concept of music processing sup-
port with a computer program dedicated to blind peo-
ple is aimed to fill in a gap between requirements and
tools available. Bringing accessibility technology to
blind people is usually based on computational in-
telligence methods such as pattern recognition and
knowledge representation. Music processing com-
32
Homenda W. (2007).
BREAKING ACCESSIBILITY BARRIERS - Computational Intelligence in Music Processing for Blind People.
In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 32-39
DOI: 10.5220/0001644700320039
Copyright
c
SciTePress
puter program discussed in this paper, which is in-
tended to contribute in breaking the accessibility bar-
rier, is solely based on both fields. Pattern recognition
is applied in music notation recognition. Knowledge
representation and processing is used in music infor-
mation storage and processing.
1.1 Notes on Accessibility for Blind
People
The population of blind people is estimated to up to
20 millions. Blindness, one of most important disabil-
ities, makes suffering people unable to use ordinary
computing facilities. They need dedicated hardware
and, what is even more important, dedicated software.
In this Section our interest is focused on accessibility
options for blind people that are available in program-
ming environments and computer systems.
An important standard of accessibility options for
disabled people is provided by IBM Corporation.
This standard is common for all kinds of personal
computers and operating systems. The fundamen-
tal technique, which must be applied in blind people
aimed software, relies on assigning all program func-
tions to keyboard. Blind people do not use mouse or
other pointing devices, thus mouse functionality must
also be assigned to keyboard. This requirement al-
lows blind user to learn keyboard shortcuts which ac-
tivates any function of the program (moreover, key-
board shortcuts often allow people with good eyesight
to master software faster then in case of mouse usage).
For instance, Drag and Drop, the typical mouse oper-
ation, should be available from keyboard. Of course,
keyboard action perhaps will be entirely different then
mouse action, but results must be the same in both
cases. Concluding, well design computer program
must allow launching menus and context menus, must
give access to all menu options, toolbars, must allow
launching dialog boxes and give access to all their el-
ements like buttons, static and active text elements,
etc. These constraints need careful design of program
interface. Ordering of dialog box elements which are
switched by keyboard actions is an example of such a
requirement.
Another important factor is related to restrictions
estimated for non disabled users. For instance, if ap-
plication limits time of an action, e.g. waiting time for
an answer, it should be more tolerant for blind people
since they need more time to prepare and input re-
quired information.
Application’s design must consider accessibility
options provided by the operating system in order to
avoid conflicts with standard options of the system.
It also should follow standards of operating system’s
accessibility method. An application for blind people
should provide conflict free cooperation with screen
readers. It must provide easy-to-learn keyboard in-
terface duplicating operations indicated by pointing
devices.
Braille display is the basic hardware element of
computer peripherals being a communicator between
blind man and computer. It plays roles of usual
screen, which is useless for blind people, and of con-
trol element allowing for a change of screen focus, i.e.
the place of text reading. Braille display also commu-
nicates caret placement and text selection.
Braille printer is another hardware tool dedicated
to blind people. Since ordinary printing is useless for
blind people, Braille printer punches information on
special paper sheet in form of the Braille alphabet of
six-dots combinations. Punched documents play the
same role for blind people as ordinary printed docu-
ments for people with good eyesight.
Screen reader is the basic software for blind peo-
ple. Screen reader is the program which is run in
background and which captures content of an active
window or an element of a dialog box and commu-
nicates it as synthesized speech. Screen reader also
keeps control over Braille display communicating in-
formation that is simultaneously spoken.
Braille editors and converters are groups of com-
puter programs giving blind people access to comput-
ers. Braille editors allow for editing and control over
documents structure and contents. Converters trans-
late ordinary documents to Braille form and oppo-
sitely.
1.2 Notes on Software Development for
Blind People
Computers become widely used by disabled people
including blind people. It is very important for blind
people to provide individuals with technologies of
easy transfer of information from one source to an-
other. Reading a book becomes now as easy for
blind human being as for someone with good eye-
sight. Blind person can use a kind of scanning equip-
ment with a speech synthesizer and, in this way, may
have a book read by a computer or even displayed at a
Braille display. Advances in speech processing allow
for converting printed text into spoken information.
On the other hand, Braille displays range from linear
text display to two dimensional Braille graphic win-
dows with a kind of gray scale imaging. Such tools
allow for a kind of reading or seeing and also for edit-
ing of texts and graphic information.
Text processing technologies for blind people are
now available. Text readers, though still very ex-
BREAKING ACCESSIBILITY BARRIERS - Computational Intelligence in Music Processing for Blind People
33
pensive and not perfect yet, becomes slowly a stan-
dard tool of blind beings. Optical character recogni-
tion, the heart of text readers, is now well developed
technology with almost 100% recognition efficiency.
This perfect technology allows for construction of
well working text readers. Also, current level of de-
velopment of speech synthesis technology allows for
acoustic communicating of a recognized text. Having
text’s information communicated, it is easy to provide
tools for text editing. Such editing tools usually use a
standard keyboard as input device.
Text processing technologies are rather exceptions
among other types of information processing for blind
people. Neither more complicated document analy-
sis, nor other types of information is easily available.
Such areas as, for instance, recognition of printed mu-
sic, of handwritten text and handwritten music, of ge-
ographical maps, etc. still raise challenges in theory
and practice. Two main reasons make that software
and equipment in such areas is not developed for blind
people as intensively as for good eyesight ones. The
first reason is objective - technologies such as geo-
graphical maps recognition, scanning different forms
of documents, recognizing music notation are still not
well developed. The second reason is more subjec-
tive and is obvious in commercial world of software
publishers - investment in such areas scarcely brings
profit.
2 ACQUIRING MUSIC
INFORMATION
Any music processing system must be supplied with
music information. Manual inputs of music symbols
are the easiest and typical source of music processing
systems. Such inputs could be split in two categories.
One category includes inputs form - roughly speaking
- computer keyboard (or similar computer peripheral).
Such input is usually linked to music notation editor,
so it affects computer representation of music nota-
tion. Another category is related to electronic instru-
ments. Such input usually produce MIDI commands
which are captured by a computer program and col-
lected as MIDI file representing live performance of
music.
Besides manual inputs we can distinguish inputs
automatically converted to human readable music for-
mats. The two most important inputs of automatic
conversion of captured information are automatic mu-
sic notation recognition which is known as Opti-
cal Music Recognition technology and audio mu-
sic recognition known as Digital Music Recognition
technology. In this paper we discuss basics of auto-
matic music notation recognition as a source of input
information feeding music processing computer sys-
tem.
2.1 Optical Music Recognition
Printed music notation is scanned to get image files in
TIFF or similar format. Then, OMR technology con-
verts music notation to the internal format of com-
puter system of music processing. The structure of
automated notation recognition process has two dis-
tinguishable stages: location of staves and other com-
ponents of music notation and recognition of music
symbols. The first stage is supplemented by detect-
ing score structure, i.e. by detecting staves, barlines
and then systems and systems’ structure and detecting
other components of music notation like title, com-
poser name, etc. The second stage is aimed on finding
placement and classifying symbols of music notation.
The step of finding placement of music notation sym-
bols, also called segmentation, must obviously pre-
cede the step of classification of music notation sym-
bols. However, both steps segmentation and classifi-
cation often interlace: finding and classifying satellite
symbols often follows classification of main symbols.
2.1.1 Staff Lines and Systems Location
Music score is a collection of staves which are printed
on sheets of paper, c.f. (Homenda, 2002). Staves are
containers to be filled in with music symbols. Stave(s)
filled in with music symbols describe a part played
by a music instrument. Thus, stave assigned to one
instrument is often called a part. A part of one in-
strument is described by one stave (flute, violin, cello,
etc.) or more staves (two staves for piano, three staves
for organ).
Staff lines location is the first stage of music no-
tation recognition. Staff lines are the most charac-
teristic elements of music notation. They seem to be
easily found on a page of music notation. However, in
real images staff lines are distorted raising difficulties
in automatic positioning. Scanned image of a sheet
of music is often skewed, staff line thickness differs
for different lines and different parts of a stave, staff
lines are not equidistant and are often curved, espe-
cially in both endings of the stave, staves may have
different sizes, etc., c.f. (Homenda, 1996; Homenda,
2002) and Figure 1.
Having staves on page located, the task of system
detection is performed. Let us recall that the term sys-
tem (at a page of music notation) is used in the mean-
ing of all staves performed simultaneously and joined
together by beginning barline. Inside and ending bar-
ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics
34
Figure 1: Examples of real notations subjected to recogni-
tion.
lines define system’s structure. Thus, detection of sys-
tems and systems’ structure relies on finding barlines.
2.1.2 Score Structure Analysis
Sometimes one stave includes parts of two instru-
ments, e.g. simultaneous notation for flute and oboe
or soprano and alto as well as tenor and bass. All
staves, which include parts played simultaneously, are
organized in systems. In real music scores systems
are often irregular, parts which not play may be miss-
ing.
Each piece of music is split into measures which
are rhythmic, (i.e. time) units defined by time sig-
nature. Measures are separated from each other by
barlines.
The task of score structure analysis is to locate
staves, group them into systems and then link respec-
tive parts in consecutive systems. Location of bar-
lines depicts measures, their analysis split systems
into group of parts and defines repetitions.
2.1.3 Music Symbol Recognition
Two important problems are raised by symbol recog-
nition task: locating and classifying symbols. Due
to irregular structure of music notation, the task of
finding symbol placement decides about final sym-
bol recognition result. Symbol classification could
not give good results if symbol location is not well
done. Thus, both tasks are equally important in music
symbols recognition.
Figure 2: Printed symbols of music notation - distortions,
variety of fonts.
Since no universal music font exits, c.f. Figure 1,
symbols of one class may have different forms. Also
size of individual symbols does not keep fixed pro-
portions. Even the same symbols may have different
sizes in one score. Besides usual noise (printing de-
fects, careless scanning) extra noise is generated by
staff and ledger lines, densely packed symbols, con-
flicting placement of other symbols, etc.
A wide range of methods are applied in music
symbol recognition: neural networks, statistical pat-
tern recognition, clustering, classification trees, etc.,
c.f. (Bainbridge and Bell, 2001; Carter and Bacon,
1992; Fujinaga, 2001; Homenda and Mossakowski,
2004; McPherson, 2002). Classifiers are usually ap-
plied to a set of features representing processed sym-
bols, c.f. (Homenda and Mossakowski, 2004). In next
section we present application of neural networks as
example classifier.
2.2 Neural Networks as Symbol
Classifier
Having understood the computational principles of
massively parallel interconnected simple neural pro-
cessors, we may put them to good use in the design of
practical systems. But neurocomputing architectures
are successfully applicable to many real life problems.
The single or multilayer fully connected feedforward
or feedback networks can be used for character recog-
nition, c.f. (Homenda and Luckner, 2004).
Experimental tests were targeted on classification
of quarter, eight and sixteen rests, sharps, flats and
naturals, c.f. Figure 2 for examples music symbols.
To reduce dimensionality of the problem, the images
were transformed to a space of 35 features. The
method applied in feature construction was the sim-
plest one, i.e. they were created by hand based on un-
derstanding of the problem being tackled. The list of
features included the following parameters computed
for bounding box of a symbol and for four quarters
of bounding box spawned by symmetry axes of the
bounding box:
• mean value of vertical projection,
BREAKING ACCESSIBILITY BARRIERS - Computational Intelligence in Music Processing for Blind People
35
• slope angle of a line approximating vertical pro-
jection,
• slope angle of a line approximating histogram of
vertical projection;
• general horizontal moment m
10
,
• general vertical moment m
01
,
• general mixed moment m
11
.
The following classifiers were utilized: backprop-
agation perceptron, feedforward counterpropagation
maximum input network and feedforward counter-
propagation closest weights network. An architecture
of neural network is denoted by a triple input - hidden
- output which identifies the numbers of neurons in in-
put, hidden and output layers, respectively, and does
not include bias inputs in input and hidden layers. The
classification rate for three symbols on music nota-
tion: flats, sharps and naturals ranges between 89%
and 99 9% , c.f. (Homenda and Mossakowski, 2004).
Classifier applied: backpropagation perceptron, feed-
forward counterpropagation maximum input network
and feedforward counterpropagation closest weights
network. An architecture of neural network is denoted
by a triple input - hidden - output which identifies the
numbers of neurons in input, hidden and output lay-
ers, respectively, and does not include bias inputs in
input and hidden layers.
3 REPRESENTING MUSIC
INFORMATION
Acquired knowledge has to be represented and stored
in a format understandable by the computer brain,
i.e. by a computer program - this is a fundamen-
tal observation and it will be exploited as a subject
of discussion in this section. Of course, a com-
puter program cannot work without low level sup-
port - it uses a processor, memory, peripherals, etc.,
but they are nothing more than only primitive elec-
tronic tools and so they are not interesting from our
point of view. Processing of such an acquired im-
age of the paper document is a clue to the paper-to-
memory data transfer and it is successfully solved for
selected tasks, c.f. OCR technology. However, doc-
uments that are more complicated structurally than
linear (printed) texts raise the problem of data ag-
gregation in order to form structured space of infor-
mation. Such documents raise the problem of ac-
quiring of implicit information/knowledge that could
be concluded from the relationships between infor-
mation units. Documents containing graphics, maps,
technical drawings, music notation, mathematical for-
mulas, etc. can illustrate these aspects of difficulties
of paper-to-computer-memory data flow. They are re-
search subjects and still raise a challenge for software
producers.
Optical music recognition (OMR) is considered as
an example of paper-to-computer-memory data flow.
This specific area of interest forces specific methods
applied in data processing, but in principle, gives a
perspective on the merit of the subject of knowledge
processing. Data flow starts from a raster image of
music notation and ends with an electronic format
representing the information expressed by a scanned
document, i.e. by music notation in our case. Several
stages of data mining and data aggregation convert the
chaotic ocean of raster data into shells of structured
information that, in effect, transfer structured data
into its abstraction - music knowledge. This process is
firmly based on the nature of music notation and mu-
sic knowledge. The global structure of music notation
has to be acquired and the local information fitting
this global structure must also be recovered from low
level data. The recognition process identifies struc-
tural entities like staves, group them into higher level
objects like systems, than it links staves of sequen-
tial systems creating instrumental parts. Music nota-
tion symbols very rarely exist as standalone objects.
They almost exclusively belong to structural entities:
staves, systems, parts, etc. So that the mined symbols
are poured into these prepared containers - structural
objects, cf. (Bainbridge and Bell, 2001; Dannenberg
and Bell, 1993; Homenda, 2002; Taube, 1993). Music
notation is a two dimensional language in which the
importance of the geometrical and logical relation-
ships between its symbols may be compared to the
importance of the symbols alone. This phenomenon
requires that the process of music knowledge acquisi-
tion must also be aimed at recovering the implicit in-
formation represented by the geometrical and logical
relationships between the symbols and then at storing
the recovered implicit relationships in an appropriate
format of knowledge representation.
There are open problems of information gain-
ing and representation like, for instance, performance
style, timbre, tone-coloring, feeling, etc. These kinds
of information are neither supported by music no-
tation, not could be derived in a reasoning process.
Such kinds of information are more subjectively per-
ceived rather than objectively described. The prob-
lem of definition, representation and processing of
”subjective kinds of information” seems to be very
interesting from research and practical perspectives.
Similarly, problems like, for instance, human way of
reading of music notation may be important from the
ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics
36
system system system
taktdzielonymiêdzysystemy
takty
Figure 3: Structuring music notation - systems and mea-
sures.
point of view of music score processing, cf. (Goolsby,
1994a; Goolsby, 1994b). Nevertheless, processing of
such kinds of information does not fit framework of
the paper and is not considered.
The process of paper-to-computer-memory mu-
sic data flow is presented from the perspective of a
paradigm of granular computing, cf. (Pedrycz, 2001).
The low-level digitized data is an example of numeric
data representation, operations on low-level data are
numeric computing oriented. The transforming of
a raster bitmap into compressed form, as e.g. run
lengths of black and while pixels, is obviously a kind
of numeric computing. It transfers data from its ba-
sic form to more compressed data. However, the next
levels of the data aggregation hierarchy, e.g. finding
the handles of horizontal lines, begins the process of
data concentration that become embryonic knowledge
units rather than more compressed data entities, cf.
(Homenda, 2005).
3.1 Staves, Measures, Systems
Staves, systems, measures are basic concepts of mu-
sic notation, cf. Figure 3. They define the structure
of music notation and are considered as information
quantities included into the data abstraction level of
knowledge hierarchy. The following observations jus-
tify such a qualification.
A stave is an arrangement of parallel horizontal
lines which together with the neighborhood are the
locale for displaying musical symbols. It is a sort of
vessel within a system into which musical symbols
can be ”poured”. Music symbols, text and graphics
that are displayed on it belong to one or more parts.
Staves, though directly supported by low-level data,
i.e. by a collection of black pixels, are complex ge-
akordy
zdarzeniawertykalne
poprzeczkirytmiczne
nuta
g³ówkanuty
laskanuty
pauza
zdarzeniawertykalne
Figure 4: Structuring music notation - symbols, ensembles
of symbols.
ometrical shapes that represent units of abstract data.
A knowledge unit describing a stave includes such ge-
ometrical information as the placement (vertical and
horizontal) of its left and right ends, staff lines thick-
ness, the distance between staff lines, skew factor,
curvature, etc. Obviously, this is a complex quantity
of data.
A system is a set of staves that are played in paral-
lel; in printed music all of these staves are connected
by a barline drawn through from one stave to next on
their left end. Braces and/or brackets may be drawn
in front of all or some of them.
A measure is usually a part of a system, sometimes
a measure covers the whole system or is split between
systems, cf. Figure 3. A measure is a unit of music
identified by the time signature and rhythmic value
of the music symbols of the measure. Thus, like in
the above cases, a measure is also a concept of data
abstraction level.
3.2 Notes, Chords, Vertical Events,
Time Slices
Such symbols and concepts as notes, chords, verti-
cal events, time slices are basic concepts of music
notation, cf. Figure 4. They define the local mean-
ing of music notation and are considered as informa-
tion quantities included in the data abstraction level
of the knowledge hierarchy. Below a description of
selected music symbols and collections of symbols
are described. Such a collection constitutes a unit of
information that has common meaning for musician.
These descriptions justify classification of symbols to
in the data abstraction level of the knowledge hierar-
chy.
Note - a symbol of music notation - represents ba-
sically the tone of given time, pitch and duration. A
BREAKING ACCESSIBILITY BARRIERS - Computational Intelligence in Music Processing for Blind People
37
note may consist of only a notehead (a whole note) or
also have a stem and may also have flag(s) or beam(s).
The components of a note are information quantities
created at the data concentration and the data aggrega-
tion stages of data aggregation process. This compo-
nents linked in the concept of a note create an abstract
unit of information that is considered as a more com-
plex component of the data abstraction level of the
information hierarchy.
A chord is composed of several notes of the same
duration with noteheads linked to the same stem (this
description does not extend to whole notes due to the
absence of a stem for such notes). Thus, a chord is
considered as data abstraction.
A vertical event is the notion by which a specific
point in time is identified in the system. Musical
symbols representing simultaneous events of the same
system are logically grouped within the same verti-
cal event. Common vertical events are built of notes
and/or rests.
A time slice is the notion by which a specific point
in time is identified in the score. A time slice is a
concept grouping vertical events of the score specified
by a given point in time. Music notation symbols in
a music representation file are physically grouped by
page and staff, so symbols belonging to a common
time slice may be physically separated in the file. In
most cases this is time slice is split between separated
parts for the scores of part type, i.e. for the scores
with parts of each performer separated each of other.
Since barline can be seen as a time reference point,
time slices can be synchronized based on barline time
reference points. This fact allows for localization of
recognition timing errors to one measure and might
be applied in error checking routine.
4 BRAILLE SCORE
Braille Score is a project developed and aimed on
blind people. Building integrated music processing
computer program directed to a broad range of blind
people is the key aim of Braille Score, c.f. (Mo-
niuszko, 2006). The program is mastered by a man.
Both the man and computer program create an inte-
grated system. The structure of the system is outlined
in Figure 5.
The system would act in such fields as:
• creating scores from scratch,
• capturing existing music printings and converting
them to electronic version,
• converting music to Braille and printing it auto-
matically in this form,
Computer
Formatof
Music
Information
ImageFiles
with
Music
Notation
MIDI
Files
begin
recognize
notation
begin
store
recogn.
ondisk
end
end
begin
convert
MIDI
begin
store
convert.
ondisk
end
end
begin
create
edit
correct
begin
music
notation
end
end
Figure 5: The structure of Braille Score.
• processing music: transposing music to different
keys, extracting parts from given score, creating a
score from given parts,
• creating and storing own compositions and instru-
mentations of musicians,
• a teacher’s tool to prepare teaching materials,
• a pupil’s tool to create their own music scores
from scratch or adapt acquired music,
• a hobby tool.
4.1 User Interface Extensions for Blind
People
Braille Score is addressed to blind people, c.f (Mo-
niuszko, 2006). Its user interface extensions allow
blind user to master the program and to perform op-
erations on music information. Ability to read, edit
and print music information in Braille format is the
most important feature of Braille Score. Blind user is
provided the following elements of interface: Braille
ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics
38
notation editor, keyboard as input tool, sound com-
municator.
Blind people do not use pointing devices. In con-
sequence, all input functions usually performed with
mouse must be mapped to computer keyboard. Mas-
sive communication with usage of keyboard requires
careful design of interface mapping to keyboard, c.f.
(Moniuszko, 2006).
Blind user usually do not know printed music no-
tation. Their perception of music notation is based on
Braille music notation format presented at Braille dis-
play or punched sheet of paper, c.f. (Krolick, 1998).
In such circumstances music information editing must
be done on Braille music notation format. Since typi-
cal Braille display is only used as output device, such
editing is usually done with keyboard as input de-
vice. In Braille Score Braille representation of music
is converted online to internal representation and dis-
played in the form of music notation in usual form.
This transparency will allow for controlling correct-
ness and consistency of Braille representation, c.f.
(Moniuszko, 2006).
Sound information is of height importance for
blind user of computer program. Wide spectrum of
visual information displayed on a screen for user with
good eyesight could be replaced by sound informa-
tion. Braille Score provides sound information of two
types. The first type of sound information collabo-
rates with screen readers, computer programs dedi-
cated to blind people. Screen readers could read con-
tents of a display and communicate it to user in the
form of synthesized speech. This type of communica-
tion is supported by contemporary programming en-
vironments. For this purpose Braille Score uses tools
provided by Microsoft .NET programming environ-
ment. The second type of sound information is based
on own Braille Score tools. Braille Score has embed-
ded mechanism of sound announcements based on its
own library of recorded utterances.
5 CONCLUSIONS
The aim of this paper is a discussion on involvement
of computational intelligence methods in implemen-
tation of user friendly computer programs focused on
disabled people. In te paper we describe a concept
of Braille Score the specialized computer program
which should help blind people to deal with music
and music notation. The use of computational intel-
ligence tolls can improve the program part devoted
to recognition and processing of music notation. The
first results with Braille Score show its to be a practi-
cal and useful tool.
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