=Paper=
{{Paper
|id=None
|storemode=property
|title=Classification of Voice Signals through Mining Unique Episodes in Temporal Information Systems: A Rough Set Approach
|pdfUrl=https://ceur-ws.org/Vol-928/0280.pdf
|volume=Vol-928
|dblpUrl=https://dblp.org/rec/conf/csp/PancerzPWW12
}}
==Classification of Voice Signals through Mining Unique Episodes in Temporal Information Systems: A Rough Set Approach==
https://ceur-ws.org/Vol-928/0280.pdf
Classification of Voice Signals through Mining
Unique Episodes in Temporal Information
Systems: A Rough Set Approach
Krzysztof Pancerz, Wieslaw Paja, Mariusz Wrzesień, and Jan Warchol
1
University of Information Technology and Management
Sucharskiego Str. 2, 35-225 Rzeszów, Poland
{kpancerz,wpaja,mwrzesien}@wsiz.rzeszow.pl
2
Medical University of Lublin
Jaczewskiego Str. 4, 20-090 Lublin, Poland
jan.warchol@umlub.pl
Abstract. Classification of voice signals in a time domain for detecting
some disturbances can be made through mining unique episodes in tem-
poral information systems. In ideal case, a voice signal is periodic and
signal shapes in each period are the same. In case of larynx diseases, some
disturbances in a voice signal can be distinguished. Selected time win-
dows (sequences of signal samples) of a voice signal constitute a temporal
information system. Appearing unique episodes in such a system can in-
dicate disturbances in the signal. In the paper, we show the methodology,
based on a rough set approach, of mining unique episodes in temporal
information systems as well as its application for classification of voice
signals.
Keywords: voice signal classification, temporal information systems,
episodes, rough sets
1 Introduction
Temporal data mining is an important issue in computer science research (cf.
[4]). There exists a huge literature concerning this topic. One of the frequently
examined problems is mining episodes defined as partially ordered sets of events
for consecutive and fixed-time intervals in sequences. Most of studies consider
analyzing simple or complex sequences to find frequent episodes (cf. [2], [3]), i.e.,
collections of events occurring frequently together. In our research, we consider
a slightly different problem. We assume that a set of episodes of the same length
is given. Our task is to find episodes which, in some sense (i.e., according to a
given criterion), clash with other episodes in the set. In our case, the criterion is
the so-called consistency factor defined in terms of rough sets.
In the paper, classification of voice signals in a time domain is considered.
A voice signal can be treated as a time series, i.e., a sequence of signal samples.
The main idea of the proposed approach is based on recognition of temporal
patterns and their replications in the fragment of a voice signal being examined
(cf. [15], [16]). It allows detecting all non-natural disturbances in articulation of
selected phonemes by patients. Preliminary observations showed that significant
replication disturbances in time appear for patients with the clinical diagnosis
of disease.
In [15], [16], there have been used neural networks with the capability of ex-
tracting the phoneme articulation pattern for a given patient (articulation is an
individual patient feature) and the capability of assessment of its replication in
the whole examined signal. In this case, the process consists of two stages: train-
ing and testing a neural network. The approach similar to the cross-validation
strategy is used. One time window is taken for training the neural network and
the remaining ones for testing the neural network. The network learns a selected
time window. If the remaining windows are similar to the selected one in terms
of the time patterns, then for such windows an error generated by the network
in a testing stage is small. If significant replication disturbances in time appear
for patients having some problems with the voice organ, then an error generated
by the network is greater. In this case, the time pattern is not preserved in the
whole signal. Therefore, the error generated by the network reflects non-natural
disturbances in the patient phonation.
In this paper, instead of neural networks, the technique, based on a rough
set approach, of mining unique episodes in temporal information systems is pro-
posed. As distinct from the approach based on neural networks, there is only
one step in the proposed approach. This step consists of mining unique episodes.
If episodes representing selected time windows of a voice signal of a given pa-
tient are similar (there is a lack of non-natural disturbances), then a special
coefficient, called a consistency factor, calculated for these episodes is close to
1. If significant replication disturbances in time appear for patients with the
larynx disease, then some episodes with consistency factors deviating from 1 are
identified.
To represent time windows of voice signals, we use temporal information
systems, i.e., information systems whose rows (objects) are ordered in time.
The temporal information systems may lead us to dynamic information systems
proposed by Z. Suraj (see [14]). The dynamic information systems represent the
knowledge of states of systems and transitions between states. The transitions
between states were described by binary transition relations. In this paper, we
use a notion of multistage dynamic information systems proposed in [7]. These
systems enable us to represent multistage transitions among states (observed
sequences of states, also called episodes). Transitions among states are described
by polyadic transition relations. The multistage decision transition systems are
used to represent such relations. To transform a given temporal information
system into the multistage decision transition system we select a set of time
windows. Each time window includes consecutive states constituting a cohesive
part of the temporal information system.
Sequences of temporal consecutive states are called episodes. In the proposed
application, we take into consideration the so-called unique episodes. A unique
episode is defined as an episode which is weakly consistent with the knowledge
extracted from the remaining episodes included in the system. The presented
approach for mining unique episodes is based on extensions of multistage deci-
sion transition systems (see [7], [9]). Such extensions have been used in an ant
based clustering of time series discrete data (see [11]). A consistent extension of
a given multistage decision transition system consists of new transitions among
states, which are totally consistent or consistent only to a certain degree (par-
tially consistent) with the knowledge included in the original multistage decision
transition system. The degree of consistency can be between 0 and 1, 0 for the to-
tal inconsistency and 1 for the total consistency. We assume that the knowledge
included in multistage decision transition systems is expressed by transition rules
(see [10]), which are minimal decision rules understood from the rough set point
of view. To calculate the degree of consistency we use an approach presented in
[9]. That approach originates from the efficient algorithm given in [5] enabling
us to determine which transitions (episodes) in the original multistage decision
transition system generate transition rules not satisfied by the tested episode
from the extension. It is worth noting that we do not calculate any transition
rules in a multistage decision transition system. This is an important property
from the point of view of computational complexity.
2 Basic Notions
Information systems are used to represent some knowledge of elements of a
universe of discourse. An information system is a pair S = (U, A), where U is
a nonempty, finite set of objects, A is a nonempty, finite set of attributes, i.e.,
a : U → Va for a ∈ A, where Va is called a value set of a. A decision system is
a pair S = (U, A), where A = C ∪ D, C ∩ D = ∅, and C is a set of condition
attributes, D is a set of decision attributes. Any information (decision) system
can be represented as a data table whose columns are labeled with attributes,
rows are labeled with objects, and entries of the table are attribute values.
Let S = (U, A) be an information system. Each subset B ⊆ A of attributes de-
termines an equivalence relation on U , called an indiscernibility relation Ind(B),
defined as Ind(B) = {(u, v) ∈ U × U : ∀ a(u) = a(v)}. The equivalence class
a∈B
containing u ∈ U will be denoted by [u]B .
Let X ⊆ U and B ⊆ A. The B-lower approximation BX of X and the B-
upper approximation BX of X are defined as BX = {u ∈ U : [u]B ⊆ X} and
BX = {u ∈ U : [u]B ∩ X 6= ∅}, respectively.
A temporal information system is a kind of an information system S = (U, A),
with a set U of objects ordered in time, i.e., U = {ut : t = 1, 2, . . . , n}, where
ut is the object observed at time instant t. By a time window on S of the
length λ in a point τ we understand an information system S 0 = (U 0 , A0 ), where
U 0 = {uτ , uτ +1 , . . . , uτ +λ−1 }, 1 ≤ τ , τ +λ−1 ≤ n, and A0 is a set of all attributes
from A defined with value sets restricted to those for objects from U 0 . The length
λ of S 0 is defined as λ = card(U 0 ). In the sequel, the set A0 of all attributes in
any time window S 0 = (U 0 , A0 ) of S = (U, A) will be marked, for simplicity, with
the same letter A like in S.
Example 1. Let us consider a simple temporal information system with two at-
tributes marked with a and b. Seven objects (global states) are collected in Table
1. Formally, we have a temporal information system S = (U, A), for which:
– a set of objects (global states) U = {u1 , u2 , . . . , u7 },
– a set of attributes A = {a, b},
– sets of attribute values: Va = {−1, 1}, Vb = {−1, 0, 1}.
Table 1. A temporal information system S
U /A a b
u1 -1 -1
u2 -1 -1
u3 1 1
u4 1 -1
u5 -1 1
u6 -1 0
u7 1 0
3 Methodology
In [14], dynamic information systems have been proposed for a description of con-
current systems. A dynamic information system additionally (in relation to in-
formation systems) includes information about transitions between global states
observed in a given concurrent system. In this section, we recall some crucial
notions concerning multistage decision transition systems and their extensions
(see [7], [9], [10], [14]).
A multistage transition system is a pair M T S = (U, T ), where U is a nonempty
set of states and T ⊆ U k is a polyadic transition relation, where k > 2. A
multistage dynamic information system is a tuple M DIS = (U, A, T ), where
S = (U, A) is an information system called the underlying system of M DIS,
U is called a set of global states of M DIS, and M T S = (U, T ) is a multistage
transition system.
A multistage transition system is created on the basis of a temporal infor-
mation system according to Algorithm 1.
Example 2. Let us take into consideration a temporal information system S
from Example 1. We can create a multistage transition system on the basis of S.
Assuming λ = 3 (a number of consecutive objects from U taken for a polyadic
transition relation), after performing Algorithm 1, we obtain a multistage transi-
tion system M T S = (U, T ), such that a set of global states U = {u1 , u2 , . . . , u7 },
and a polyadic transition relation
T = {(u1 , u2 , u3 ), (u2 , u3 , u4 ), (u3 , u4 , u5 ), (u4 , u5 , u6 ), (u5 , u6 , u7 )}.
Algorithm 1: Algorithm for creating a multistage transition system on
the basis of a temporal information system.
Input : A temporal information system S = (U, A), where
U = {ut : t = 1, 2, . . . , n}, λ - a number of consecutive objects from U
taken for a polyadic transition relation.
Output: A multistage transition system M T S = (U, T ) corresponding to S.
T ← ∅;
for each τ = 1 . . . n − λ + 1 do
Get a sequence uτ , uτ +1 , . . . , uτ +λ−1 of consecutive objects from U ;
T ← T ∪ (uτ , uτ +1 , . . . , uτ +λ−1 );
end
Create M T S = (U, T );
Let M DIS = (U, A, T ) be a multistage dynamic information system, where
T ⊆ U k . Each element (u1 , u2 , . . . , uk ) ∈ T , where u1 , u2 , . . . , uk ∈ U , is called
an episode in M DIS.
Let M T S = (U, T ) be a multistage transition system. A multistage decision
transition system is a pair M DT S = (UT , A1 ∪ A2 ∪ . . . ∪ Ak ), where each t ∈ UT
corresponds exactly to one element of the polyadic transition relation T whereas
attributes from the set Ai determine global states of the i-th domain of T , where
i = 1, 2, . . . , k. Each object in a multistage decision transition system represents
one episode in a given multistage dynamic information system M DIS.
Example 3. Let us take into consideration a temporal information system S from
Example 1. For the system S, we can create five time windows of length 3. All
sequences of objects in time windows define a polyadic transition relation T (see
Example 2). On the basis of T , we define a multistage decision transition system
M DT S shown in Table 2. We have five episodes t1 , t2 , t3 , t4 , and t5 . We can
say that attributes from the set A1 = {a1 , b1 } determine global states at time
instant τ , attributes from the set A2 = {a2 , b2 } determine global states at time
instant τ + 1 and attributes from the set A3 = {a3 , b3 } determine global states
at time instant τ + 2.
Table 2. A multistage decision transition system M DT S
UT /A1 ∪ A2 ∪ A3 a1 b1 a2 b2 a3 b3
t1 -1 -1 -1 -1 1 1
t2 -1 -1 1 1 1 -1
t3 1 1 1 -1 -1 1
t4 1 -1 -1 1 -1 0
t5 -1 1 -1 0 1 0
For a given multistage decision transition system, we can consider its ele-
mentary decision transition subsystems defined as follows.
An elementary decision transition subsystem of a multistage decision transi-
tion system M DT S = (UT , A1 ∪ A2 ∪ . . . ∪ Ak ) is a decision transition system
DT S(i, i + 1) = (UT , Ai ∪ Ai+1 ), where: i ∈ {1, ..., k − 1}.
Example 4. Let us take into consideration a multistage decision transition sys-
tem M DT S from Example 3. For M DT S, we obtain two elementary decision
transition subsystems DT S(1, 2) and DT S(2, 3) shown in Table 3.
Table 3. Elementary decision transition subsystems: (a) DT S(1, 2) and (b) DT S(2, 3)
UT /A1 ∪ A2 a1 b1 a2 b2 UT /A2 ∪ A3 a2 b2 a3 b3
t1 -1 -1 -1 -1 t1 -1 -1 1 1
t2 -1 -1 1 1 t2 1 1 1 -1
a) b)
t3 1 1 1 -1 t3 1 -1 -1 1
t4 1 -1 -1 1 t4 -1 1 -1 0
t5 -1 1 -1 0 t5 -1 0 1 0
Any nontrivial extension of a given multistage decision transition system
M DT S = (UT , A1 ∪ A2 ∪ . . . ∪ Ak ) includes episodes existing in M DT S as well
as new ones added to M DT S.
Let DT S(i, i + 1) = (UT , Ai ∪ Ai+1 ) be the elementary decision transition
subsystem. For each attribute a ∈ Ai and the new episode t∗ , we can transform
DT S(i, i + 1) into the system with irrelevant values of attributes. If a(t∗ ) 6= a(t),
where t ∈ UT , then we replace a(t) by the value * (denoting an irrelevant value).
This means that we create a new system for which appropriate sets of attribute
values are extended by the value *. The transformed system can be treated
as an incomplete system. Therefore, instead of an indiscernibility relation and
equivalence classes, we use a characteristic relation and characteristic sets (cf.
[1]). For the transformed elementary decision transition subsystem DT S(i, i +
1) = (UT , Ai ∪ Ai+1 ), we define a characteristic relation R(Ai ). R(Ai ) is a binary
relation on UT defined as follows:
R(Ai ) = {(t, v) ∈ UT2 : ∃ a(t) 6= ∗ ∧ ∀ (a(t) = ∗ ∨ a(t) = a(v)) }. (1)
a∈Ai a∈Ai
For each episode t∗ from the extension of M DT S, we define a consistency
factor of t∗ (see [8], [9]). The consistency factor ξM DT S (t∗ ) of the episode t∗ with
the knowledge included in M DT S is calculated as:
{Ai Xava : Ai Xava 6= ∅ ∧ a(t∗ ) 6= va })
S S
k−1 card(
a∈Ai+1 va ∈Va
Y
ξM DT S (t∗ ) = 1 − , (2)
i=1
card(UT )
where
– Xava = {t ∈ UT : a(t) = va },
– Ai Xava = {t ∈ UT : KAi (t) 6= ∅ ∧ KAi (t) ⊆ Xava } is an Ai -lower approxima-
tion of Xava ,
– KAi (t) = {v ∈ UT : (t, v) ∈ R(Ai )} is a characteristic set.
From the point of view of rough set theory, we have that a lower approxima-
tion consists of episodes for which values of global states described by attributes
from Ai (i.e., global states at time instant τ ) uniquely define values of global
states described by attributes from Ai+1 (i.e., global states at next time instant
τ + 1). It means that, for such episodes, we can define certain transition rules
(rules describing transitions between states), see [9]. If we add a new episode for
which Ai defines the same values of a global state as for episodes belonging to
some lower approximation, but the next global state is different, then this new
episode does not satisfy a certain transition rule valid in a given multistage deci-
sion transition system M DT S. We say that such a new episode is not consistent
with the knowledge included in M DT S and expressed by transition rules. It can
be consistent only partially.
Example 5. Let us take into consideration an elementary decision transition sub-
system DT S(1, 2) from Example 4. Taking Xa12 = {t2 , t3 }, we have A1 Xa12 =
{t3 }, where A1 = {a1 , b1 }, because:
– For the episode t3 , A1 describes the state a1 (t) = 1 and b1 (t) = 1, where
t ∈ UT , and this state uniquely defines the next state in which a2 (t) = 1.
– For the episodes t1 and t2 , A1 describes the state a1 (t) = −1 and b1 (t) = −1,
where t ∈ UT , and this state does not uniquely define the next state, i.e., for
the next state we have a2 (t) = −1 (for the episode t1 ) or a2 (t) = 1 (for the
episode t2 ).
– Episodes t4 and t5 are out of interest, because, for these episodes, in the next
state a2 (t) 6= 1.
Therefore, we have, for example, that:
– A transition rule IF a1 (t) = 1 and b1 (t) = 1, THEN a2 (t) = 1 is a certain
rule in DT S(1, 2).
– A transition rule IF a1 (t) = −1 and b1 (t) = −1, THEN a2 (t) = 1 is not a
certain rule in DT S(1, 2).
Let us add a new episode shown in Table 5 to DT S(1, 2). The episode t∗ does
Table 4. A new episode added to DT S(1, 2)
UT /A1 ∪ A2 a1 b1 a2 b2
t∗ 1 1 -1 -1
not satisfy a rule IF a1 (t) = 1 and b1 (t) = 1, THEN a2 (t) = 1. It is not totally
consistent with the knowledge included in DT S(1, 2).
Algorithm 2: Algorithm for mining unique episodes in a given multistage
decision transition system
Input : A multistage decision transition system
M DT S = (UT , A1 ∪ A2 ∪ . . . ∪ Ak ), a threshold value θ ∈ [0, 1]
determining uniqueness of episodes in M DT S.
Output: A set ΥT ⊆ UT of unique episodes in M DT S with respect to θ.
ΥT ←− ∅;
for each t ∈ UT do
Create M DT S 0 by removing t from UT in M DT S;
Compute ξM DT S 0 (t) according to Formula 2;
if ξM DT S 0 (t) ≤ θ then
ΥT ←− ΥT ∪ t;
end
end
On the basis of consistency factors calculated for episodes in a given mul-
tistage decision transition system, we can determine which episodes are unique
ones in the considered system.
Definition 1. Let M DT S = (UT , A1 ∪ A2 ∪ . . . ∪ Ak ) be a multistage decision
transition system, t ∈ UT , M DT S 0 be a multistage decision transition system
emerged from M DT S by the removal of t, and θ ∈ [0, 1] be a threshold value
determining uniqueness of t. The episode t is called a unique episode with respect
to θ if and only if ξM DT S 0 (t) ≤ θ.
The process of mining unique episodes may be automated, see Algorithm 2.
Example 6. Let us take into consideration the elementary decision transition
subsystems DT S(1, 2) and DT S(2, 3) from Example 4. M DT S 0 is a multistage
decision transition system emerged from M DT S by the removal of the episode
t1 . Analogously DT S 0 (1, 2) and DT S 0 (2, 3) are elementary decision transition
subsystems emerged from DT S(1, 2) and DT S(2, 3), respectively, by the removal
of t1 . We transform DT S 0 (1, 2) and DT S 0 (2, 3) into the systems DT S 00 (1, 2) and
DT S 00 (2, 3), respectively, with irrelevant values of attributes (see Table 5).
Table 5. Elementary decision transition subsystems transformed into systems with
irrelevant values of attributes: (a) DT S 0 (1, 2) and (b) DT S 0 (2, 3)
UT /A1 ∪ A2 a1 b1 a2 b2 UT /A2 ∪ A2 a2 b2 a3 b3
t2 * * 1 1 t2 1 1 1 -1
a) t3 1 1 1 -1 b) t3 1 * -1 1
t4 1 * -1 1 t4 * 1 -1 0
t5 * 1 -1 0 t5 * 0 1 0
We obtain the following non-empty lower approximations:
– A1 Xa12 = {t2 },
– A1 Xb12 = {t2 , t4 },
– A2 Xa−1
3 = {t3 },
2 0
– A Xb3 = {t4 , t5 },
– A2 Xb13 = {t3 }.
The remaining lower approximations, i.e.,
A1 Xa−1 1 −1 1 0 2 1 2 −1
2 , A Xb2 , A Xb2 , A Xa3 , A Xb3 ,
are empty.
Let a threshold value θ determining uniqueness of the episodes be equal to 0.5.
Performing Algorithm 2, we have ξDT S(1,2) (t1 ) = 0.5 and ξDT S(2,3) (t1 ) = 0.25.
Finally, we have that a consistency factor of the episode t1 with the knowledge
included in the multistage decision transition system M DT S 0 is: ξM DT S 0 (t1 ) =
0.125. According to θ = 0.5, we can say that the episode t1 is unique in a
temporal information system S.
4 Classification of Voice Signals
The proposed approach can be used in classification of voice signals in the time
domain for diagnosis of larynx diseases. A voice signal can be treated as a time
series. The classification procedure is as follows. We divide the voice signal of an
examined patient into time windows corresponding to phonemes. Next, we select
randomly a number of time windows. Consecutive signal samples of selected time
windows can be presented in the tabular form as a multistage decision transition
system. This idea is depicted in Figure 1.
In Table 6, we give some example of such a multistage decision transition
system M DT S. Each row of M DT S corresponds to one time window. Each
time window consists of 100 signal samples. Values of signal samples are normal-
ized to the interval [−1.0, 1.0]. Each time window can be treated as an episode.
M DT S includes information about five episodes (time windows). Each episode
is described by 100 attributes, a1 , a2 , ..., a100 . The attribute ai , where i =
1, 2, . . . , 100, determines the value of the i-th signal sample of a given time win-
dow. In this example, we have formally M DT S = (UT , A1 ∪A2 ∪. . .∪A99 ∪A100 ),
where UT = {t1 , t2 , t3 , t4 , t5 },A1 = {a1 }, A2 = {a2 }, A3 = {a3 }, ..., A99 = {a99 },
and A100 = {a100 }.
Next, we transform each episode in M DT S into the so-called delta represen-
tation, i.e., values of samples have been replaced with differences between values
of current samples and values of previous samples. After transformation, each
episode is a sequence consisting of three values: -1 (denoting decreasing), 0 (de-
noting a lack of change), 1 (denoting increasing) (cf. [11]). This transformation
enables us to obtain a multistage decision transition system with discrete values.
For example, after the transformation of M DT S given in Table 6, we obtain a
new multistage decision transition system M DT S ∗ shown in Table 7.
Fig. 1. Creating a multistage decision transition system for a voice signal
Table 6. A multistage decision transition system M DT S representing selected time
windows of a voice signal
UT /A1 ∪ A2 ∪ A3 ∪ . . . ∪ A99 ∪ A100 a1 a2 a3 . . . a99 a100
t1 1.00 1.00 0.98 . . . -0.08 -0.11
t2 0.94 0.94 0.95 . . . -0.12 -0.09
t3 0.92 0.94 0.91 . . . -0.14 -0.15
t4 0.95 0.95 0.93 . . . -0.11 -0.12
t5 1.00 0.99 0.93 . . . -0.13 -0.14
In the transformed multistage decision transition system M DT S ∗ , we search
for unique episodes using Algorithm 2. If time windows, into which a voice signal
is divided, are similar (there are no disturbances), then the unique episodes are
not present in M DT S ∗ . If significant replication disturbances in time appear for
patients with the larynx disease, then time windows differ from each other and
unique episodes appear in M DT S ∗ . Hence, the result of searching for unique
episodes is an indicator used to classify patient voice signals according to larynx
diseases.
In Table 8, we present selected results of experiments carried out using the
proposed approach. Data were collected by J. Warchol [18]. The results of search-
ing for unique episodes in voice signals of six patients (three from a control group
and three with confirmed pathology) are shown. A table includes values of con-
sistency factors of five episodes calculated for selected patients.
Table 7. A transformed multistage decision transition system M DT S ∗
UT /A2 ∪ A3 ∪ . . . ∪ A100 a2 a3 . . . a100
t1 0 -1 . . . -1
t2 0 1 ... 1
t3 1 -1 . . . -1
t4 0 -1 . . . -1
t5 -1 -1 . . . -1
Table 8. Consistency factors of five episodes calculated for selected patients
Episode (time window) / Patient p1 p2 p3 p4 p5 p6
t1 0.93 0.85 0.91 0.85 0.68 0.92
t2 0.85 0.97 0.91 0.87 0.80 0.68
t3 0.89 0.92 0.85 0.62 0.76 0.94
t4 0.86 1.00 0.89 0.84 0.87 0.92
t5 0.85 0.94 0.83 0.87 0.82 0.83
Let a threshold value determining uniqueness of episodes θ = 0.80. For pa-
tients p1 , p2 , and p3 , there are no unique episodes. These patients belong to the
control group. For patient p4 , one unique episode (time window t3 ) is detected.
For patient p5 , three unique episodes (time windows t1 , t2 , and t3 ) are detected.
For patient p6 , one unique episode (time window t2 ) is detected. Patients p4 , p5 ,
and p6 had larynx disease confirmed by clinicians.
The presented methodology has been implemented in the LARDISS system
- a tool for computer aided diagnosis of laryngopathies [17]. The tool is created
for the Java platform.
5 Conclusions
In the paper, we have shown how to use multistage decision transition systems
and their extensions defined in [7] and [9] for mining unique episodes in temporal
information systems as well as how to apply the proposed approach for classi-
fication of voice signals in a time domain for detecting some disturbances. The
presented approach is based on algorithms with polynomial time complexity. In
the future we plan to propose a modified consistency factor to improve a quality
of classification of voice signals.
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