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e
VGGish
n
etwo
r
k
,
wh
ich
h
as
b
ee
n
p
r
e
-
tr
ain
e
d
u
s
in
g
a
lar
g
e
d
ataset
o
f
au
d
io
k
n
o
wn
as
au
d
io
s
et
,
an
d
c
o
m
b
in
es
it
with
a
n
eu
r
al
n
etwo
r
k
k
n
o
w
n
as
a
b
id
ir
ec
tio
n
al
g
ated
r
ec
u
r
r
en
t
u
n
it
(
B
iGR
U
)
[
7
]
,
[
8
]
.
W
h
en
tr
ain
in
g
th
e
m
o
d
el,
VGGish
p
ar
am
eter
s
will
b
e
f
r
o
ze
n
in
o
r
d
er
to
p
r
eser
v
e
p
r
etr
ain
i
n
g
f
o
r
k
n
o
wled
g
e,
wh
ile
th
e
B
iG
R
U
lay
er
s
wi
ll
b
e
f
in
etu
n
ed
with
lu
n
g
s
o
u
n
d
d
ata.
T
h
is
s
tu
d
y
in
ten
d
s
to
cr
ea
te
b
etter
d
etec
tio
n
o
f
r
esp
ir
ato
r
y
d
is
ea
s
e
[
9
]
o
f
lu
n
g
s
o
u
n
d
s
.
T
h
e
d
esig
n
ad
d
r
ess
es sev
er
al
is
s
u
es a
s
s
o
ciate
d
with
im
b
alan
ce
d
d
ata
i
n
a
m
ed
ical
ap
p
licatio
n
,
a
n
d
m
a
y
im
p
r
o
v
e
class
ic
d
etec
tio
n
o
f
d
if
f
er
en
t
l
u
n
g
co
n
d
itio
n
s
[
1
0
]
.
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
a
n
ew
au
t
o
m
ated
class
if
icatio
n
m
eth
o
d
f
o
r
p
u
lm
o
n
ar
y
d
is
e
ase
d
etec
tio
n
f
r
o
m
s
ig
n
als
ex
tr
ac
ted
f
r
o
m
lu
n
g
s
o
u
n
d
s
wh
ich
u
s
es
em
p
ir
ical
wav
elet
tr
an
s
f
o
r
m
an
d
s
tate
-
of
-
th
e
-
a
r
t
f
ea
tu
r
e
ex
tr
ac
tio
n
[
1
1
]
.
T
h
is
ap
p
r
o
ac
h
em
p
lo
y
ed
s
ev
er
al
class
if
ier
s
,
in
clu
d
in
g
th
e
u
s
e
o
f
lig
h
t
g
r
ad
ien
t
b
o
o
s
tin
g
m
ac
h
i
n
e
(
L
GB
M)
,
wh
ich
lead
s
ap
p
licab
le
h
ig
h
d
etec
tio
n
ac
r
o
s
s
d
is
ea
s
e
clas
s
if
icatio
n
,
s
u
ch
as
asth
m
a,
p
n
e
u
m
o
n
ia,
an
d
ch
r
o
n
ic
o
b
s
tr
u
ctiv
e
p
u
lm
o
n
a
r
y
d
is
ea
s
e
(
C
OPD)
[
1
2
]
.
T
ec
h
n
ical
c
o
n
tr
ib
u
tio
n
s
:
i)
d
e
v
elo
p
ed
a
n
o
v
el
p
r
ep
r
o
ce
s
s
in
g
m
eth
o
d
(
ad
ap
tiv
e
em
p
ir
ical
s
to
ck
well
-
tr
an
s
f
o
r
m
(
AE
ST)
)
t
o
en
h
a
n
ce
r
esp
ir
ato
r
y
s
o
u
n
d
s
ig
n
als
.
ii)
u
tili
ze
d
Mel
-
f
r
eq
u
en
c
y
c
ep
s
tr
al
co
ef
f
icien
ts
(
MFC
C
)
an
d
Me
l
-
s
p
ec
tr
o
g
r
a
m
s
f
o
r
ef
f
ec
tiv
e
f
ea
tu
r
e
ex
t
r
ac
tio
n
an
d
s
elec
tio
n
.
iii)
i
n
tr
o
d
u
ce
d
s
ca
lab
le
co
n
v
o
l
u
tio
n
al
g
ey
s
er
n
etwo
r
k
(
SC
GN)
f
o
r
ac
cu
r
ate
lu
n
g
d
i
s
ea
s
e
cla
s
s
if
icatio
n
.
iv
)
en
s
u
r
ed
r
o
b
u
s
t,
s
ca
lab
le
p
er
f
o
r
m
an
ce
with
r
ea
l
-
w
o
r
ld
ap
p
licab
ilit
y
u
s
in
g
I
n
ter
n
at
io
n
al
C
o
n
f
er
en
ce
o
n
B
io
m
e
d
ical
an
d
Hea
lth
I
n
f
o
r
m
atics
(
I
C
B
HI
)
d
ataset.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
Var
io
u
s
s
im
u
latio
n
to
o
ls
an
d
r
esear
ch
wo
r
k
s
h
av
e
p
r
e
v
io
u
s
l
y
ex
is
ted
in
th
e
liter
atu
r
e
th
at
is
b
ased
o
n
th
e
ar
tific
ial
in
tellig
en
ce
(
AI
)
f
r
am
ewo
r
k
f
o
r
m
u
lti
-
s
tag
e
lu
n
g
d
is
ea
s
e
d
etec
tio
n
with
au
d
io
s
ig
n
als
[
1
3
]
–
[
1
5
]
.
I
t
in
co
r
p
o
r
ates
lu
n
g
ca
n
ce
r
with
co
m
p
u
ted
to
m
o
g
r
a
p
h
y
(
CT
)
im
ag
es
en
h
an
ce
m
en
t,
ex
tr
ac
tio
n
o
f
f
ea
tu
r
es,
ca
teg
o
r
izatio
n
u
s
in
g
r
eg
i
o
n
o
f
in
ter
est
(
R
OI
)
,
an
d
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
(
R
NN)
-
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
DL
m
o
d
el
[
1
6
]
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
ac
h
iev
es h
ig
h
ac
cu
r
ac
y
,
o
u
tp
er
f
o
r
m
in
g
ex
i
s
tin
g
ap
p
r
o
ac
h
es
in
d
is
ea
s
e
d
etec
tio
n
.
I
t
is
s
h
o
wn
a
m
u
ltich
an
n
el
DL
m
eth
o
d
f
o
r
u
s
in
g
c
h
est
X
-
r
ay
s
to
id
en
tify
lu
n
g
co
n
d
itio
n
s
lik
e
p
n
eu
m
o
n
ia
a
n
d
t
u
b
er
c
u
lo
s
is
[
1
7
]
–
[
1
9
]
.
T
h
e
m
eth
o
d
f
u
s
es
f
ea
tu
r
e
f
r
o
m
E
f
f
icien
tNetB
0
,
B
1
,
an
d
B
2
m
o
d
els,
p
r
o
ce
s
s
es
th
em
th
r
o
u
g
h
f
u
lly
co
n
n
ec
ted
la
y
er
s
,
an
d
class
if
ies
d
is
ea
s
es
u
s
in
g
a
s
tack
ed
en
s
em
b
le
lea
r
n
in
g
class
if
ier
.
W
iley
et
a
l.
[
2
0
]
h
a
v
e
p
r
o
p
o
s
ed
DL
ar
ch
itectu
r
e
t
h
at
class
if
ies
lu
n
g
co
n
d
itio
n
s
an
d
n
o
r
m
al
X
-
r
ay
s
p
u
lm
o
n
a
r
y
ed
em
a
.
W
an
g
et
a
l.
[
2
1
]
h
a
v
e
p
r
o
p
o
s
ed
a
n
ew
f
r
am
ewo
r
k
th
at
d
iv
id
es
co
n
tin
u
o
u
s
r
ec
o
r
d
in
g
s
in
to
d
is
cr
ete
ev
en
ts
f
o
r
lu
n
g
s
o
u
n
d
lu
n
g
tex
tu
r
e
class
if
icatio
n
ev
en
t d
etec
tio
n
lik
e
in
h
alatio
n
,
cr
ac
k
les,
an
d
r
h
o
n
ch
i
u
tili
zin
g
a
tem
p
o
r
al
co
n
v
o
lu
ti
o
n
al
n
etwo
r
k
(
T
C
N)
co
m
b
in
e
d
with
a
f
u
s
io
n
s
tr
ateg
y
.
3.
P
RO
P
O
SE
D
M
E
T
H
O
DO
L
O
G
Y
Fig
u
r
e
1
r
ep
r
esen
ted
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
f
o
r
au
to
m
a
tic
lu
n
g
d
is
ea
s
e
d
etec
tio
n
i
n
v
o
lv
es
f
o
u
r
k
ey
s
tag
es:
p
r
ep
r
o
ce
s
s
in
g
,
f
ea
tu
r
e
ex
tr
ac
tio
n
,
f
ea
tu
r
e
s
elec
tio
n
,
an
d
class
if
icatio
n
.
R
esp
ir
ato
r
y
s
o
u
n
d
s
ig
n
als
ar
e
p
r
ep
r
o
ce
s
s
ed
u
s
in
g
th
e
AE
ST,
f
o
llo
wed
b
y
th
e
ex
tr
ac
ti
o
n
o
f
MFC
C
an
d
Me
l
-
s
p
ec
tr
o
g
r
am
s
.
T
h
e
s
elec
ted
f
ea
tu
r
es
ar
e
th
e
n
class
if
ied
u
s
in
g
a
SC
GN
,
wh
ich
ad
d
r
ess
es
ch
allen
g
es
lik
e
im
b
alan
ce
d
d
atasets
an
d
en
s
u
r
es
ac
cu
r
ate
r
esu
lts
.
SC
GN
u
s
ed
f
o
r
class
if
icatio
n
a
n
d
g
ey
s
er
i
n
s
p
ir
ed
o
p
tim
izatio
n
a
lg
o
r
ith
m
(
GOA)
u
s
ed
.
T
h
e
d
ataset
u
s
ed
f
o
r
e
x
p
er
im
en
tati
o
n
is
I
C
B
HI
.
3
.
1
.
Da
t
a
s
et
ex
plo
r
a
t
io
n
T
h
e
lu
n
g
s
o
u
n
d
class
if
icatio
n
with
m
u
lti
f
ea
tu
r
es
[
2
2
]
in
clu
d
es
lu
n
g
s
o
u
n
d
r
ec
o
r
d
in
g
s
f
o
r
p
u
lm
o
n
ar
y
d
is
ea
s
es,
last
in
g
1
0
to
9
0
s
ec
o
n
d
s
an
d
s
am
p
led
at
d
if
f
er
e
n
t
f
r
e
q
u
en
cies.
T
h
e
I
C
B
HI
d
ataset
is
wid
ely
r
ec
o
g
n
ize
d
as
o
n
e
o
f
t
h
e
d
atasets
in
th
e
ar
ea
o
f
m
ed
ical
h
ea
lth
i
n
f
o
r
m
atio
n
,
esp
ec
ially
u
s
ed
b
y
p
eo
p
le
r
esear
c
h
i
n
g
r
esp
ir
ato
r
y
p
u
lm
o
n
a
r
y
s
o
u
n
d
a
n
aly
s
is
.
I
t
was
o
r
ig
in
ally
cr
ea
t
ed
as
p
ar
t
o
f
th
e
I
C
B
HI
2
0
1
7
,
to
h
elp
ad
v
an
ce
th
e
d
ev
elo
p
m
e
n
t
o
f
m
ac
h
in
e
lear
n
in
g
an
d
class
if
icatio
n
f
o
r
th
e
d
iag
n
o
s
is
o
f
b
r
ea
th
in
g
co
n
d
itio
n
s
.
T
h
e
d
ataset
in
clu
d
es
a
v
ar
iety
o
f
s
o
u
n
d
r
ec
o
r
d
in
g
s
co
llected
f
r
o
m
1
2
8
p
atien
ts
with
a
r
an
g
e
o
f
d
if
f
er
en
t
p
u
lm
o
n
ar
y
co
n
d
itio
n
s
,
s
u
ch
as C
OPD,
ast
h
m
a,
b
r
o
n
ch
iecta
s
is
,
an
d
u
p
p
e
r
r
esp
ir
ato
r
y
tr
ac
t in
f
ec
tio
n
s
.
3
.
2
.
Sig
na
l
prepro
ce
s
s
ing
enha
ncem
ent
t
hro
ug
h
AE
ST
L
u
n
g
u
ltra
s
o
u
n
d
s
p
ec
tr
o
s
co
p
y
[
2
3
]
im
p
r
o
v
es
r
esp
ir
ato
r
y
s
ig
n
al
q
u
ality
th
r
o
u
g
h
ad
ap
tiv
e
w
in
d
o
win
g
,
wh
ich
twee
k
s
th
e
win
d
o
w
s
ize
b
u
t
is
s
till
b
ased
o
n
th
e
lo
ca
l f
r
eq
u
e
n
cy
co
n
ten
t.
T
h
is
is
a
p
er
f
ec
t
m
eth
o
d
o
lo
g
y
f
o
r
n
o
n
-
s
tatio
n
ar
y
s
ig
n
als,
s
in
ce
is
p
er
m
its
v
ar
iab
le
f
r
e
q
u
e
n
cy
d
etec
tio
n
.
T
h
e
AE
S
T
is
also
s
u
p
er
io
r
t
o
th
e
s
h
o
r
t
-
tim
e
Fo
u
r
ie
r
tr
an
s
f
o
r
m
(
STFT
)
,
as
it
p
r
o
v
id
es
a
b
etter
tim
e
-
f
r
eq
u
en
cy
r
ep
r
esen
tatio
n
o
f
r
ep
r
esen
tatio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
6
:
106
-
1
1
5
108
I
t
ca
p
tu
r
es
tr
an
s
ien
t
f
ea
tu
r
es
an
d
is
r
esis
tan
t
to
n
o
is
e.
T
h
i
s
ad
ap
tab
ilit
y
is
cr
u
cial
f
o
r
a
n
aly
zin
g
c
o
m
p
lex
r
esp
ir
ato
r
y
s
o
u
n
d
s
.
B
y
u
s
in
g
l
u
n
g
s
o
u
n
d
s
ig
n
als
d
is
ea
s
es
m
ay
b
e
class
if
ied
an
d
p
r
e
d
icted
.
Var
iatio
n
s
in
lu
n
g
s
o
u
n
d
s
m
ay
d
e
p
en
d
o
n
th
e
h
e
alth
co
n
d
itio
n
s
an
d
ag
e
o
f
t
h
e
p
atien
ts
.
T
h
e
Gau
s
s
ian
w
in
d
o
w
s
[
(
i
(
t
)
]
ca
n
b
e
r
ep
r
esen
ted
as si
g
n
al
co
n
s
id
er
atio
n
with
r
esp
ec
t to
th
e
i
n
itial tim
e
p
er
io
d
i
(
t
)
is
s
h
o
wn
in
(
1
)
.
[
(
(
)
]
=
∫
(
+
)
−
2
2
2
2
∞
−
∞
(
1
)
W
h
er
e
‘
I
(
α
+
t
)
’
is
th
e
tim
e
-
s
h
if
te
d
s
ig
n
al,
‘
’
is
th
e
f
r
e
q
u
en
c
y
in
d
e
x
,
‘
α
’
is
th
e
s
h
if
tin
g
o
f
th
e
s
ig
n
al
with
r
esp
ec
t
to
th
e
tim
e
p
er
io
d
‘
t
’
,
‘
x
’
is
a
p
o
s
itiv
e
v
ar
iab
le
to
id
e
n
t
if
y
th
e
s
ig
n
al
d
if
f
er
en
ce
s
ac
co
r
d
in
g
to
t
h
e
tim
e
s
lo
ts
to
id
en
tify
th
e
lu
n
g
d
is
ea
s
e
s
ev
er
ity
in
p
atien
ts
,
‘
w
’
is
th
e
wav
elen
g
th
o
f
th
e
g
en
e
r
ated
s
ig
n
al
with
r
esp
ec
t
to
th
e
in
itial tim
e
p
er
io
d
‘
i
(
t
)
’
an
d
th
e
ter
m
‘
e
−
2
π
2
x
2
f
2
’
r
ep
r
esen
ts
th
e
f
r
eq
u
en
cy
-
d
ep
en
d
en
t G
au
s
s
ian
win
d
o
w.
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
B
i
o
m
e
d
i
c
a
l
a
n
d
H
e
a
l
t
h
I
n
f
o
r
m
a
t
i
c
s
(
I
C
B
H
I
)
D
a
t
a
s
e
t
A
d
a
p
t
i
v
e
E
m
p
i
r
i
c
a
l
S
t
o
c
k
w
e
l
l
-
T
r
a
n
s
f
o
r
m
(
A
E
S
T
)
M
F
C
C
a
n
d
M
u
l
s
p
e
c
t
r
u
m
F
e
a
t
u
r
e
s
S
c
a
l
a
b
l
e
C
o
n
v
o
l
u
t
i
o
n
a
l
G
e
y
s
e
r
N
e
t
w
o
r
k
(
S
C
G
N
)
f
o
r
C
l
a
s
s
i
f
i
c
a
t
i
o
n
S
c
a
l
a
b
l
e
C
o
n
v
o
l
u
t
i
o
n
a
l
N
u
r
e
l
N
e
t
w
o
r
k
(
C
N
N
)
G
e
y
s
e
r
-
i
n
s
p
i
r
e
d
O
p
t
i
m
i
z
a
t
i
o
n
A
l
g
o
r
i
t
h
m
(
G
O
A
)
N
o
i
s
e
r
e
m
o
v
a
l
S
i
g
n
a
l
n
o
r
m
a
l
i
z
a
t
i
o
n
B
a
n
d
-
p
a
s
s
f
i
l
t
e
r
i
n
g
A
n
t
e
r
i
o
r
L
e
f
t
A
n
t
e
r
i
o
r
R
i
g
h
t
L
a
t
e
r
a
l
L
e
f
t
L
a
t
e
r
a
l
R
i
g
h
t
P
o
s
t
e
r
i
o
r
L
e
f
t
T
r
a
c
h
e
a
P
o
s
t
e
r
i
o
r
R
i
g
h
t
Fig
u
r
e
1
.
Ov
e
r
all
p
r
o
p
o
s
ed
m
e
th
o
d
o
lo
g
y
s
ch
em
atic
r
e
p
r
esen
t
atio
n
3
.
3
.
M
F
CC
a
nd
M
el
-
s
pect
ru
m
f
o
r
a
ud
io
s
ig
na
l f
ea
t
ure
ex
t
ra
ct
io
n
T
h
e
p
r
o
ce
s
s
b
eg
in
s
with
ex
tr
ac
tin
g
au
d
io
f
r
o
m
a
v
id
eo
f
ile.
On
ce
th
e
au
d
io
is
ex
tr
ac
ted
,
MFC
C
i
s
co
m
p
u
ted
u
s
in
g
a
lib
r
ar
y
lik
e
lib
r
o
s
a.
Me
l
-
s
p
ec
tr
o
g
r
am
s
ca
p
tu
r
e
tem
p
o
r
al
ch
a
n
g
es
in
p
itch
an
d
o
th
er
au
d
i
o
p
r
o
p
er
ties
,
allo
win
g
th
e
m
o
d
el
to
b
e
r
esil
ien
t
to
n
o
is
e
[
2
4
]
.
T
h
e
s
ig
n
al
v
ar
iatio
n
s
with
r
esp
ec
t
to
th
e
tim
e
p
er
io
d
ar
e
s
h
o
wn
in
(
2
)
.
=
(
,
)
(
2
)
W
h
er
e,
‘
T
co
m
b
i
n
e
’
d
en
o
tes
th
e
C
o
m
b
in
ed
s
ig
n
als,
‘
c
on
c
e
n
t
r
a
te
’
r
e
p
r
esen
ts
to
co
n
n
ec
t
two
p
ictu
r
es
alo
n
g
t
h
eir
h
eig
h
t,
‘
T
m
f
cc
’
d
en
o
tes
t
h
e
MFC
C
f
ea
tu
r
es
an
d
‘
T
m
el
’
d
en
o
tes
th
e
Me
l
s
p
ec
tr
u
m
.
All
f
ea
tu
r
es
e
x
tr
ac
ted
f
r
o
m
th
e
clu
s
ter
k
er
n
el
R
ee
d
-
Xiao
li
(
C
KR
X)
m
o
d
el.
3
.
4
.
SCG
N
f
o
r
o
ptim
ized
cla
s
s
if
ica
t
io
n
T
h
e
S
C
GN
i
s
a
n
o
p
t
i
m
iz
e
d
DL
[
2
5
]
a
r
c
h
i
t
e
c
t
u
r
e
d
e
s
i
g
n
e
d
f
o
r
e
f
f
i
c
i
e
n
t
a
n
d
a
c
c
u
r
a
t
e
c
l
as
s
if
i
c
a
t
i
o
n
o
f
r
e
s
p
i
r
a
t
o
r
y
s
o
u
n
d
s
i
g
n
a
ls
.
SC
GN
h
a
s
b
e
e
n
d
e
s
i
g
n
e
d
as
a
n
i
d
e
al
a
r
c
h
i
t
e
c
t
u
r
e
f
o
r
h
i
g
h
-
d
i
m
e
n
s
io
n
a
l
d
a
t
a
s
et
s
w
h
e
r
e
c
l
a
s
s
i
f
ic
a
t
i
o
n
is
a
p
r
i
m
a
r
y
g
o
a
l
.
SC
G
N
e
n
a
b
le
s
e
f
f
i
ci
e
n
t
co
m
p
u
t
a
t
i
o
n
a
l
p
e
r
f
o
r
m
a
n
c
e
v
i
a
i
t
s
u
s
e
o
f
s
c
a
l
a
b
l
e
c
o
n
v
o
l
u
t
i
o
n
a
l
l
a
y
e
r
(
s
)
,
w
h
i
c
h
a
l
l
o
w
f
o
r
s
y
s
t
e
m
a
ti
c
h
i
e
r
a
r
ch
i
c
a
l
a
n
a
l
y
s
is
o
f
i
n
p
u
t
d
at
a
.
F
u
r
t
h
e
r
m
o
r
e
,
SC
GN
e
m
p
l
o
y
s
a
d
y
n
a
m
i
c
s
c
a
l
i
n
g
m
e
t
h
o
d
t
o
d
e
t
e
r
m
i
n
e
t
h
e
a
m
o
u
n
t
o
f
d
e
p
t
h
a
n
d
c
o
m
p
l
e
x
i
t
y
t
o
u
s
e
f
o
r
s
p
e
c
i
f
ic
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
r
tifi
cia
l in
tellig
en
ce
fr
a
mewo
r
k
fo
r
…
(
B
a
n
d
r
ed
d
i V
e
n
ka
ta
S
esh
u
ku
ma
r
i
)
109
c
l
a
s
s
i
f
ic
a
t
i
o
n
p
r
o
b
l
e
m
s
o
l
v
i
n
g
.
S
C
G
N
l
e
v
e
r
a
g
e
s
g
e
y
s
e
r
-
t
y
p
e
a
c
t
i
v
a
ti
o
n
f
u
n
c
t
i
o
n
s
as
a
m
e
a
n
s
o
f
m
i
ti
g
a
t
i
n
g
b
o
t
h
v
a
n
i
s
h
i
n
g
g
r
a
d
i
e
n
ts
a
n
d
g
r
a
d
i
en
t
p
r
o
p
a
g
a
t
i
o
n
i
s
s
u
e
s
w
h
e
n
c
o
n
s
t
r
u
c
t
i
n
g
d
e
e
p
n
e
tw
o
r
k
s
.
3
.
4
.
1
.
Sca
la
ble
co
nv
o
lutio
na
l
neura
l net
wo
rk
T
h
e
en
h
an
ce
m
e
n
t
o
f
t
h
e
a
u
d
io
s
ig
n
al
t
h
at’
s
tar
g
ete
d
f
o
r
f
u
tu
r
e
asp
ec
ts
,
s
u
ch
as
r
ec
o
g
n
izin
g
t
h
e
p
r
ev
alen
t
f
ea
tu
r
es
i
n
a
s
am
p
le
o
f
a
u
d
io
,
is
a
m
eth
o
d
o
f
im
p
r
o
v
in
g
t
h
e
a
u
d
io
s
ig
n
al
th
r
o
u
g
h
AE
ST
p
r
ep
r
o
ce
s
s
in
g
.
Featu
r
e
ex
tr
ac
tio
n
u
s
es
Me
l
-
s
p
ec
tr
o
g
r
am
s
a
n
d
MFC
C
to
ex
tr
ac
t
r
elev
an
t
f
ea
tu
r
es
o
f
th
e
au
d
io
/s
o
u
n
d
s
am
p
le
to
r
ea
ch
a
p
o
in
t
wh
er
e
y
o
u
ca
n
cl
ass
if
y
th
e
f
ea
tu
r
e
an
d
class
if
y
th
e
s
am
p
le
with
SC
GN
wh
ich
h
as b
ee
n
p
r
o
v
en
to
b
e
ef
f
ec
tiv
e
f
o
r
class
if
icatio
n
o
f
lu
n
g
d
is
ea
s
es.
T
h
e
ar
ch
it
ec
tu
r
e
o
f
th
e
SC
GN
is
d
ef
in
ed
b
y
th
e
ac
tiv
atio
n
f
u
n
ctio
n
o
f
th
e
ex
p
o
n
en
tial lin
ea
r
u
n
it
(
E
L
U)
.
I
t is d
ef
in
e
d
in
(
3
)
.
(
)
=
{
>
0
−
1
≤
0
(
3
)
W
h
er
e
‘
x
’
is
is
a
p
o
s
itiv
e
v
ar
ia
b
le
to
i
d
en
tify
th
e
s
ig
n
al
d
if
f
e
r
en
ce
s
ac
co
r
d
in
g
to
th
e
tim
e
s
lo
ts
to
id
en
tif
y
th
e
lu
n
g
d
is
ea
s
e
s
ev
er
ity
in
p
atien
ts
.
T
h
e
o
u
tp
u
t
is
th
e
s
am
e
as
th
e
in
p
u
t
f
o
r
p
o
s
itiv
e
v
alu
es
o
f
‘
x
’.
T
h
e
o
u
tp
u
t
is
ca
lcu
lated
b
y
ter
m
‘
e
x
−
1
’
f
o
r
n
o
n
-
p
o
s
itiv
e
v
alu
es
o
f
‘
x
’
,
wh
er
e
th
e
ex
p
o
n
e
n
tial
f
u
n
ctio
n
‘
e
x
’
is
u
s
ed
o
n
th
e
in
p
u
t.
T
h
e
class
if
icatio
n
o
f
au
d
io
s
ig
n
als
f
r
o
m
lu
n
g
d
is
ea
s
e
th
r
o
u
g
h
th
e
u
s
e
o
f
an
o
u
tp
u
t
lay
er
id
en
tifie
s
th
o
s
e
au
d
io
s
ig
n
als
as
tr
ac
h
ea
(
Tc
)
,
p
o
s
ter
io
r
le
ft
(
Pl
)
,
an
d
m
a
n
y
o
t
h
er
ca
teg
o
r
i
es.
T
h
e
in
clu
s
io
n
o
f
th
is
lay
er
will
h
elp
to
r
ed
u
ce
th
e
p
r
o
b
lem
o
f
im
b
alan
ce
d
d
a
tasets
an
d
tr
ain
in
g
in
s
tab
ilit
y
.
T
h
e
ar
ch
itectu
r
e
p
r
esen
ted
in
t
h
e
im
p
lem
en
tatio
n
ca
n
ef
f
ec
tiv
ely
class
if
y
th
e
c
o
m
p
lex
m
ed
ical
a
u
d
io
s
ig
n
als o
f
lu
n
g
d
is
ea
s
es a
cc
u
r
ately
.
3
.
5
.
G
ey
s
er
-
ins
pired o
ptim
iza
t
io
n a
lg
o
ri
t
hm
T
h
e
GOA
is
a
n
atu
r
al
a
n
alo
g
u
e
f
o
r
o
p
tim
izatio
n
th
at
tak
es
its
in
s
p
ir
atio
n
f
r
o
m
h
o
w
g
ey
s
er
s
b
eh
av
e
wh
en
th
ey
er
u
p
t
an
d
u
s
es
th
o
s
e
p
r
in
cip
les
to
id
en
tif
y
o
p
tim
al
s
o
lu
tio
n
s
.
T
h
e
GOA
u
s
es
th
e
p
er
io
d
ic
b
eh
a
v
io
r
an
d
r
is
k
in
ess
o
f
g
ey
s
er
er
u
p
tio
n
s
to
s
o
lv
e
d
if
f
icu
lt
p
r
o
b
le
m
s
b
y
tak
i
n
g
ad
v
a
n
tag
e
o
f
th
e
way
p
r
ess
u
r
e
b
u
ild
s
u
p
in
a
g
e
y
s
er
b
e
f
o
r
e
r
elea
s
in
g
t
h
at
p
r
ess
u
r
e.
T
h
e
wa
y
th
e
GOA
m
im
ics
th
e
cy
clica
l
b
u
ild
in
g
-
up
-
a
n
d
-
r
elea
s
e
o
f
p
r
ess
u
r
e
in
g
ey
s
er
s
also
g
iv
es
it
th
e
ab
ilit
y
to
co
n
tin
u
o
u
s
ly
escap
e
f
r
o
m
lo
ca
l
o
p
tim
a,
u
s
in
g
co
n
tin
u
o
u
s
p
r
o
ce
s
s
es
to
h
elp
it
ex
p
lo
r
e
th
e
en
tire
g
lo
b
al
s
p
ac
e.
T
h
e
GOA
is
d
esig
n
ed
to
in
co
r
p
o
r
ate
r
an
d
o
m
n
ess
,
wh
ich
allo
ws
it
to
av
o
id
f
allin
g
i
n
to
l
o
ca
l
o
p
tim
a
r
ep
ea
te
d
ly
a
n
d
to
ex
p
lo
r
e
th
e
wid
t
h
o
f
th
e
g
l
o
b
al
s
o
lu
tio
n
s
p
ac
e.
T
h
e
cy
clica
l
ch
ar
ac
t
er
o
f
th
e
GOA
p
r
o
ce
s
s
also
en
s
u
r
es
th
at
th
er
e
will
b
e
a
b
alan
ce
b
etwe
en
th
e
tim
e
s
p
en
t
lear
n
in
g
ab
o
u
t th
e
o
p
tim
al
s
o
lu
tio
n
an
d
th
e
tim
e
s
p
en
t e
x
p
l
o
itin
g
th
e
o
p
tim
al
s
o
lu
tio
n
in
t
h
e
o
p
tim
i
za
tio
n
p
r
o
ce
s
s
.
4.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
T
h
e
ap
p
r
o
ac
h
o
f
d
etec
tin
g
lu
n
g
im
p
air
m
e
n
t
u
tili
ze
s
AE
ST
-
ex
ten
d
in
g
a
u
d
io
,
MFC
C
-
b
ased
au
d
io
f
ea
tu
r
es,
an
d
a
r
o
b
u
s
t
an
d
s
ca
lab
le
SC
GN
r
ein
f
o
r
ce
d
with
R
esNex
t.
T
esti
n
g
was
co
n
d
u
c
ted
u
s
in
g
th
e
I
C
B
HI
d
ataset,
with
p
h
ase
-
2
d
e
m
o
n
s
tr
atin
g
r
eliab
le,
s
ca
lab
le,
ac
cu
r
a
te,
a
n
d
r
o
b
u
s
t
d
iag
n
o
s
tics
.
T
h
e
SC
GN
was
co
m
b
in
ed
with
th
e
R
esNex
t
s
lid
in
g
d
o
u
b
le
p
ar
tial
r
ein
f
o
r
ce
m
en
t
n
etwo
r
k
to
ad
d
r
ess
im
b
a
lan
ce
d
d
atasets
an
d
u
n
s
tab
le
tr
ain
in
g
,
ac
tin
g
to
c
r
e
ate
a
f
r
am
ewo
r
k
th
at
was
b
o
th
r
o
b
u
s
t
an
d
r
eliab
le
in
class
if
icatio
n
o
u
tp
u
t.
S
y
s
tem
r
eq
u
ir
em
e
n
ts
in
clu
d
ed
Py
th
o
n
3
.
1
2
.
7
an
d
h
ig
h
c
o
m
p
u
tatio
n
al
ca
p
ac
ity
,
an
d
a
s
tab
le
m
o
d
el
ev
alu
atio
n
was
estab
lis
h
ed
b
ased
u
p
o
n
s
y
s
te
m
ce
r
tific
atio
n
o
f
2
0
e
p
o
ch
s
,
i
n
d
icativ
e
o
f
b
o
th
th
e
p
o
s
s
ib
le
s
ca
lab
ilit
y
alo
n
g
s
id
e
o
p
er
atio
n
al
v
iab
ilit
y
in
a
p
p
r
o
p
r
iat
e
m
ed
ical
d
iag
n
o
s
tic
p
r
ac
ti
ce
.
Atten
tio
n
f
o
r
war
d
is
n
o
w
t
o
th
e
ap
p
licatio
n
o
f
f
r
am
ewo
r
k
ch
ar
ac
ter
is
tics
to
eith
er
y
ield
p
r
ac
tical
o
r
ef
f
icien
t p
o
s
s
ib
ilit
y
with
in
th
e
m
ed
ica
l d
iag
n
o
s
tic.
4
.
1
.
F
e
a
t
ure
ex
t
r
a
ct
io
n
re
s
ults o
f
t
wo
a
ud
io
f
iles
Fig
u
r
e
2
m
ig
h
t d
e
p
ict
th
e
f
ea
t
u
r
es e
x
tr
ac
ted
f
r
o
m
Fig
u
r
e
2
(
a
)
au
d
io
f
ile
1
an
d
Fig
u
r
e
2
(
b
)
a
u
d
io
f
ile
2
(
e.
g
.
,
f
r
eq
u
e
n
cy
,
am
p
litu
d
e,
o
r
o
th
er
s
p
ec
tr
al
ch
a
r
ac
ter
is
tics
)
f
r
o
m
th
e
two
a
u
d
io
f
iles
.
T
h
e
x
-
ax
is
f
o
r
b
o
t
h
g
r
ap
h
s
m
ig
h
t d
ep
ict
tim
e
(
o
r
f
r
eq
u
en
c
y
)
,
wh
ile
th
e
y
-
ax
is
m
i
g
h
t d
ep
ict
m
a
g
n
itu
d
e
(
o
r
o
th
er
f
ea
tu
r
es).
Fig
u
r
e
3
m
ig
h
t
d
ep
ict
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
o
f
f
in
e
-
tu
n
in
g
a
m
o
d
el'
s
p
ar
am
eter
s
to
m
in
im
ize
er
r
o
r
s
o
r
m
a
x
im
ize
p
er
f
o
r
m
an
ce
.
An
o
p
tim
izatio
n
m
eth
o
d
co
u
ld
b
e
a
g
r
a
d
ien
t
d
escen
t
m
eth
o
d
(
th
er
e'
s
m
a
n
y
v
ar
ian
ts
o
f
g
r
ad
ien
t
d
escen
t
s
u
ch
as
s
to
ch
asti
c
g
r
a
d
ien
t
d
escen
t,
m
in
i
-
b
atch
g
r
ad
ien
t
d
escen
t)
.
T
h
is
wo
u
ld
iter
ate
o
v
er
th
e
m
o
d
el,
o
n
ly
u
p
d
atin
g
th
e
weig
h
ts
b
ased
o
n
th
e
g
r
ad
ien
t d
ir
ec
tio
n
o
f
th
e
co
s
t
f
u
n
ctio
n
.
T
h
er
e
ar
e
o
th
er
o
p
tim
izer
s
th
a
t
wo
u
ld
u
til
ize
a
n
o
tio
n
o
f
m
o
m
en
tu
m
wh
ich
m
ig
h
t
s
m
o
o
th
u
p
d
ates
an
d
h
elp
escap
e
lo
c
al
m
in
im
a
d
u
r
in
g
o
p
tim
izatio
n
"f
in
e
-
tu
n
in
g
".
T
h
er
e
ar
e
also
r
eg
u
lar
izatio
n
p
a
r
am
eter
s
(
e.
g
.
,
weig
h
t d
ec
ay
)
th
at
ca
n
h
elp
p
r
ev
e
n
t
o
v
er
f
itti
n
g
,
an
d
th
o
s
e
wo
u
l
d
ju
s
t
ca
u
s
e
th
e
o
p
tim
izer
to
also
c
o
n
s
id
er
weig
h
t
d
ec
a
y
wh
ile
o
p
tim
izin
g
th
e
m
o
d
el.
T
h
er
e
will
b
e
g
r
ap
h
s
th
at
d
e
p
ict
th
e
m
etr
ics
d
u
r
in
g
tr
ain
in
g
t
h
at
s
h
o
w
m
etr
ics
in
d
icatin
g
th
e
lo
s
s
is
d
ec
r
ea
s
in
g
,
o
r
th
e
ac
cu
r
ac
y
,
f
o
r
e
x
am
p
le,
i
s
in
cr
ea
s
in
g
o
v
er
ea
ch
ep
o
ch
o
f
tr
ain
in
g
.
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5
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No
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1
,
Feb
r
u
ar
y
2
0
2
6
:
106
-
1
1
5
110
(
a)
(
b
)
Fig
u
r
e
2
.
E
x
tr
ac
ted
f
ea
t
u
r
es in
(
a)
au
d
io
f
ile
1
a
n
d
(
b
)
a
u
d
io
f
ile
2
Fig
u
r
e
3
.
C
o
m
p
a
r
is
o
n
with
ex
i
s
tin
g
o
p
tim
izatio
n
Fig
u
r
e
4
g
iv
es
a
co
m
p
ar
is
o
n
o
f
th
e
SC
GN
m
o
d
el
ag
ain
s
t
t
h
e
o
th
er
m
o
d
els
s
u
ch
as
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
,
d
ee
p
lear
n
i
n
g
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
DL
C
NN)
,
an
d
lin
ea
r
wav
e
n
eu
r
o
n
s
(
L
W
N)
.
SC
GN
d
em
o
n
s
tr
ates
v
er
y
g
o
o
d
p
e
r
f
o
r
m
an
ce
ac
c
o
r
d
i
n
g
to
th
e
d
ata
s
h
o
wn
:
F1
-
s
co
r
e=
0
.
9
4
;
ac
cu
r
ac
y
=0
.
9
5
;
p
r
ec
is
io
n
=0
.
9
3
;
an
d
r
ec
all=0
.
9
4
.
T
h
e
n
ex
t
b
est
p
er
f
o
r
m
e
r
is
th
e
DL
C
NN
m
o
d
el
with
an
ac
cu
r
ac
y
o
f
0
.
8
8
in
all
f
o
u
r
m
etr
ics
lis
ted
.
T
h
e
th
ir
d
b
est
p
er
f
o
r
m
e
r
is
th
e
L
W
N
m
o
d
el,
wh
ich
p
er
f
o
r
m
e
d
m
o
d
er
ately
at
an
ac
cu
r
ac
y
o
f
0
.
8
2
.
T
h
e
ANN
m
o
d
el
p
er
f
o
r
m
ed
th
e
wo
r
s
t,
with
an
ac
cu
r
ac
y
o
f
0
.
7
5
,
in
d
icatin
g
th
at
th
is
m
o
d
el
is
n
o
t
as
ef
f
ec
tiv
e
as th
e
o
th
e
r
th
r
ee
.
Fi
g
u
r
e
5
illu
s
tr
ates th
e
r
esu
lts
o
f
tr
ain
in
g
an
d
v
alid
ate
co
m
p
ar
i
s
o
n
af
ter
2
0
ep
o
c
h
s
.
Fig
u
r
e
5
(
a
)
s
h
o
ws
tr
ain
in
g
ac
cu
r
ac
y
=0
.
9
5
;
test
in
g
ac
c
u
r
ac
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=0
.
9
7
;
th
is
s
h
o
ws
v
e
r
y
g
o
o
d
f
it
f
o
r
b
o
th
tr
ain
i
n
g
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d
test
d
ata.
Fig
u
r
e
5
(
b
)
s
h
o
ws tr
ain
in
g
lo
s
s
=0
.
9
1
; te
s
tin
g
lo
s
s
=0
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8
5
d
em
o
n
s
tr
atin
g
v
er
y
g
o
o
d
g
en
e
r
aliza
tio
n
.
T
ab
le
1
d
escr
ib
es
th
e
f
r
eq
u
e
n
cy
r
an
g
es
an
d
k
ey
ch
a
r
ac
te
r
is
tics
o
f
lu
n
g
s
o
u
n
d
s
r
ec
o
r
d
ed
f
r
o
m
d
i
f
f
er
en
t
an
ato
m
ical
lo
ca
tio
n
s
.
E
ac
h
s
ite
is
ass
o
ciate
d
with
s
p
ec
if
ic
s
o
u
n
d
p
atter
n
s
,
s
u
ch
as
p
itch
,
f
r
eq
u
e
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cy
,
an
d
a
n
ad
v
en
titi
o
u
s
s
o
u
n
d
lik
e
wh
ee
z
in
g
o
r
c
r
ac
k
les.
Fig
u
r
e
4
.
E
x
is
tin
g
n
etwo
r
k
co
m
p
ar
is
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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tif
I
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tell
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SS
N:
2252
-
8
9
3
8
A
r
tifi
cia
l in
tellig
en
ce
fr
a
mewo
r
k
fo
r
…
(
B
a
n
d
r
ed
d
i V
e
n
ka
ta
S
esh
u
ku
ma
r
i
)
111
(
a)
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)
Fig
u
r
e
5
.
T
r
ain
in
g
a
n
d
v
alid
atio
n
th
r
o
u
g
h
(
a)
ac
c
u
r
ac
y
cu
r
v
e
an
d
(
b
)
lo
s
s
cu
r
v
e
T
ab
le
1
.
Data
s
et
class
es id
en
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.
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EEE
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C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
E
n
g
in
e
e
rin
g
wa
s
a
wa
rd
e
d
in
2
0
0
8
.
Cu
rre
n
tl
y
,
sh
e
se
rv
e
s
a
s
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
in
th
e
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
a
t
G
ITAM
Un
iv
e
r
sity
,
Visa
k
h
a
p
a
t
n
a
m
,
a
n
d
An
d
h
ra
P
ra
d
e
sh
,
In
d
ia.
Wi
th
2
0
y
e
a
rs o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
at
e
m
a
il
:
b
m
a
d
ired
@g
it
a
m
.
e
d
u
.
J
h
a
n
si
Ye
ll
a
p
u
e
a
rn
e
d
h
e
r
P
h
.
D.
in
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
fr
o
m
Ac
h
a
ry
a
Na
g
a
rju
n
a
U
n
iv
e
rsit
y
,
G
u
n
tu
r,
I
n
d
ia,
in
2
0
1
9
.
S
h
e
c
o
m
p
lete
d
h
e
r
M
.
Tec
h
.
i
n
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
S
y
ste
m
s
En
g
i
n
e
e
rin
g
a
t
An
d
h
ra
U
n
iv
e
rsit
y
i
n
2
0
0
8
.
C
u
rre
n
tl
y
,
sh
e
se
rv
e
s
a
s
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
i
n
th
e
De
p
a
rtme
n
t
o
f
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
in
e
e
ri
n
g
a
t
G
ITAM
Un
iv
e
rsity
,
l
o
c
a
ted
i
n
V
isa
k
h
a
p
a
tn
a
m
,
A
n
d
h
ra
P
ra
d
e
sh
,
I
n
d
ia.
Wi
t
h
o
v
e
r
1
8
y
e
a
rs
o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
a
n
d
1
2
y
e
a
rs
d
e
d
ica
ted
to
re
se
a
rc
h
,
h
e
r
e
x
p
e
rti
se
sp
a
n
s
m
a
c
h
in
e
lea
rn
in
g
,
a
rti
ficia
l
in
telli
g
e
n
c
e
,
d
a
ta
e
n
g
in
e
e
rin
g
,
a
n
d
ima
g
e
p
r
o
c
e
ss
in
g
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jy
e
ll
a
p
u
@
g
it
a
m
.
e
d
u
.
Bo
d
a
p
a
ti
Ve
n
k
a
t
a
Ra
j
a
n
n
a
is
c
u
rre
n
tl
y
wo
rk
in
g
a
s
a
n
a
ss
o
c
ia
te
p
ro
fe
ss
o
r
i
n
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
i
n
e
e
rin
g
a
t
M
LR
I
n
stit
u
te
o
f
Tec
h
n
o
lo
g
y
,
Hy
d
e
ra
b
a
d
,
I
n
d
ia.
He
re
c
e
iv
e
d
B.
Tec
h
.
d
e
g
re
e
in
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
fro
m
Ch
irala
En
g
in
e
e
rin
g
C
o
ll
e
g
e
,
JN
TU,
Ka
k
in
a
d
a
,
In
d
ia,
i
n
2
0
1
0
;
M
.
Tec
h
.
d
e
g
re
e
in
P
o
we
r
El
e
c
tro
n
ics
a
n
d
Dri
v
e
s
fro
m
Ko
n
e
ru
Lak
sh
m
a
iah
E
d
u
c
a
ti
o
n
F
o
u
n
d
a
ti
o
n
,
G
u
n
t
u
r,
I
n
d
ia,
i
n
2
0
1
5
;
a
n
d
P
h
.
D.
i
n
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
a
t
Ko
n
e
r
u
Lak
sh
m
a
iah
Ed
u
c
a
ti
o
n
F
o
u
n
d
a
ti
o
n
,
G
u
n
tu
r
,
In
d
ia,
i
n
2
0
2
1
.
His
c
u
rre
n
t
re
se
a
rc
h
in
c
l
u
d
e
s,
d
y
n
a
m
ic
m
o
d
e
li
n
g
o
f
b
a
tt
e
ries
fo
r
re
n
e
wa
b
le
e
n
e
rg
y
s
to
ra
g
e
,
b
a
tt
e
ry
m
a
n
a
g
e
m
e
n
t
sy
st
e
m
s
(BM
S
)
fo
r
e
lec
tri
c
v
e
h
icle
s
a
n
d
p
o
r
tab
le
e
lec
tro
n
ics
a
p
p
li
c
a
ti
o
n
s,
re
n
e
wa
b
le
e
n
e
r
g
y
so
u
rc
e
s
in
teg
ra
ti
o
n
wit
h
b
a
tt
e
ry
e
n
e
rg
y
sto
ra
g
e
sy
ste
m
s
(BES
S
),
sm
a
rt
m
e
t
e
rin
g
a
n
d
s
m
a
rt
g
rid
s,
m
icro
-
g
rid
s,
a
u
to
m
a
ti
c
m
e
ter
re
a
d
in
g
(AMR)
d
e
v
ice
s,
G
S
M
/G
P
RS
a
n
d
p
o
we
r
li
n
e
c
a
rrier
(P
LC)
c
o
m
m
u
n
ica
ti
o
n
,
a
n
d
v
a
rio
u
s
m
o
d
u
latio
n
tec
h
n
i
q
u
e
s
su
c
h
a
s
Q
P
S
K,
BP
S
K,
ASK,
F
S
K,
OO
K,
a
n
d
G
M
S
K.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
ra
jan
n
a
b
v
2
0
1
2
@
g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
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I
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tell
I
SS
N:
2252
-
8
9
3
8
A
r
tifi
cia
l in
tellig
en
ce
fr
a
mewo
r
k
fo
r
…
(
B
a
n
d
r
ed
d
i V
e
n
ka
ta
S
esh
u
ku
ma
r
i
)
115
Nita
la
k
s
h
e
sw
a
r
a
Ra
o
K
o
l
u
k
u
l
a
o
b
tain
e
d
h
is
P
h
.
D.
in
Co
m
p
u
t
e
r
S
c
ien
c
e
a
n
d
S
y
ste
m
s
En
g
in
e
e
rin
g
a
t
An
d
h
ra
Un
iv
e
rsity
Visa
k
h
a
p
a
tn
a
m
In
d
ia.
He
re
c
e
iv
e
d
h
is
M
.
Tec
h
.
in
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
E
n
g
in
e
e
rin
g
in
2
0
0
9
.
He
is
th
e
to
p
p
e
r
o
f
th
e
sc
h
o
o
l
a
t
S
S
C
lev
e
l
a
n
d
b
a
tch
to
p
p
e
r
a
t
M
.
Tec
h
.
lev
e
l.
No
w,
h
e
is
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
in
De
p
a
rtme
n
t
o
f
CS
E
a
t
G
ITAM
Un
iv
e
rsity
Visa
k
h
a
p
a
tn
a
m
An
d
h
ra
P
ra
d
e
sh
In
d
ia.
He
h
a
s
1
5
p
l
u
s
y
e
a
rs
o
f
tea
c
h
in
g
a
n
d
7
y
e
a
rs
o
f
re
se
a
rc
h
e
x
p
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rie
n
c
e
.
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wa
s
g
ra
n
ted
7
p
a
ten
ts
b
y
in
t
e
ll
e
c
tu
a
l
p
ro
p
e
rty
o
f
I
n
d
i
a
a
n
d
2
we
re
p
u
b
li
s
h
e
d
b
y
I
P
In
d
ia
se
rv
ice
s
G
o
v
e
rn
m
e
n
t
o
f
I
n
d
ia.
He
is
a
u
th
o
re
d
se
v
e
n
tex
t
b
o
o
k
s.
He
re
c
e
iv
e
d
b
e
st
y
o
u
n
g
re
se
a
rc
h
sc
h
o
lar
a
wa
rd
in
p
ri
d
e
o
f
I
n
d
ia
a
wa
rd
s
2
0
2
2
.
He
is
th
e
li
fe
m
e
m
b
e
r
o
f
IS
T
E,
IEI
,
a
n
d
IAENG
.
His
c
u
rre
n
t
re
se
a
rc
h
i
n
ter
e
st
in
c
lu
d
e
s
AI,
m
a
c
h
in
e
lea
rn
in
g
,
d
e
e
p
lea
rn
i
n
g
so
ftwa
re
e
n
g
i
n
e
e
rin
g
,
d
a
ta
e
n
g
i
n
e
e
rin
g
,
a
n
d
q
u
a
li
t
y
a
ss
u
ra
n
c
e
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
k
o
l
u
k
u
lan
i
t
la@
g
m
a
il
.
c
o
m
.
S
iv
a
S
a
ira
m
Pra
sa
d
K
o
d
a
li
re
c
e
iv
e
d
h
is
B.
Tec
h
.
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ie
n
c
e
a
n
d
E
n
g
in
e
e
rin
g
fr
o
m
Ac
h
a
ry
a
Na
g
a
rju
n
a
Un
iv
e
rsity
,
G
u
n
t
u
r,
I
n
d
ia,
in
2
0
0
9
,
a
n
d
M
.
E
.
d
e
g
re
e
in
S
o
ftwa
re
S
y
ste
m
s
fro
m
Birl
a
In
stit
u
te
o
f
Tec
h
n
o
lo
g
y
a
n
d
S
c
ien
c
e
(BITS
),
G
o
a
,
In
d
ia,
in
2
0
1
2
.
He
h
a
s
4
y
e
a
rs
o
f
t
e
a
c
h
in
g
a
n
d
1
0
y
e
a
rs
o
f
re
se
a
rc
h
e
x
p
e
rien
c
e
.
He
is
c
u
rre
n
tl
y
wo
rk
i
n
g
a
s
a
ss
istan
t
p
ro
fe
ss
o
r
in
th
e
De
p
a
rtme
n
t
o
f
CS
E
at
S
i
d
d
h
a
rth
a
Ac
a
d
e
m
y
o
f
Hi
g
h
e
r
Ed
u
c
a
ti
o
n
(
d
e
e
m
e
d
to
b
e
u
n
i
v
e
rsity
).
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
s
o
ftwa
re
-
d
e
fin
e
d
n
e
two
rk
i
n
g
(
S
DN
),
P
4
,
a
n
d
m
a
c
h
in
e
lea
rn
in
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
siv
sa
i@g
m
a
il
.
c
o
m
.
J
a
y
a
sr
e
e
Pi
n
a
j
a
l
a
is
c
u
rre
n
t
ly
a
re
se
a
rc
h
sc
h
o
lar
a
n
d
p
u
rsu
i
n
g
h
e
r
P
h
.
D
a
t
G
o
d
a
v
a
ri
G
lo
b
a
l
U
n
iv
e
rsit
y
,
Ra
jam
a
h
e
n
d
ra
v
a
ra
m
.
S
h
e
c
o
m
p
lete
d
h
e
r
B.
Tec
h
.
(I
n
fo
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
)
i
n
2
0
1
1
fro
m
VRS
a
n
d
YRN
Co
l
leg
e
o
f
E
n
g
in
e
e
rin
g
a
n
d
Tec
h
n
o
l
o
g
y
,
Ch
i
ra
la
a
ffil
iate
d
to
JN
TU,
Ka
k
in
a
d
a
.
S
h
e
c
o
m
p
lete
d
h
e
r
M
.
Tec
h
.
(Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
)
in
2
0
1
3
fro
m
Na
ra
sa
ra
o
p
e
ta
En
g
in
e
e
rin
g
C
o
ll
e
g
e
a
ffil
i
a
ted
to
JN
TU,
Ka
k
in
a
d
a
.
S
h
e
h
a
d
e
x
p
e
rien
c
e
i
n
d
iffere
n
t
a
c
a
d
e
m
ic
a
n
d
a
d
m
in
istrativ
e
r
o
les
a
t
v
a
rio
u
s
a
c
a
d
e
m
ic
in
stit
u
tes
fo
r
m
o
re
th
a
n
7
y
e
a
rs.
Cu
rre
n
tl
y
wo
rk
in
g
a
s
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
a
t
Ch
a
it
a
n
y
a
En
g
i
n
e
rin
g
Co
ll
e
g
e
Visa
k
h
a
p
a
tn
a
m
in
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
.
S
h
e
a
tt
e
n
d
e
d
a
n
d
p
re
se
n
ted
p
a
p
e
rs
in
d
iffere
n
t
c
o
n
fe
re
n
c
e
s,
wo
rk
sh
o
p
s
a
n
d
sy
m
p
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s.
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h
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p
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d
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tern
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j
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u
r
n
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ls.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jay
a
sre
e
p
4
@g
m
a
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.
c
o
m
.
J
a
m
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s
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te
p
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n
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c
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rv
in
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th
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a
ti
o
n
a
l
c
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a
ir
p
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fe
ss
o
r
a
t
t
h
e
Dr.
B.
R.
Am
b
e
d
k
a
r
Ch
a
ir,
An
d
h
ra
Un
iv
e
rsit
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u
n
d
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r
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M
in
istr
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Ju
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&
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rm
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o
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d
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e
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Re
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p
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with
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c
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c
a
n
b
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c
o
n
tac
ted
a
t
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m
a
il
:
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e
ss
tep
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n
m
@g
m
a
il
.
c
o
m
.
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la
k
a
la
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m
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Re
d
d
y
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c
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d
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d
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re
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in
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k
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2
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m
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Un
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rsity
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S
o
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th
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o
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a
.
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is c
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rre
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rtme
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Jo
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p
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ll
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B.
R.
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n
g
i
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rin
g
Co
ll
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Hy
d
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ra
b
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d
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In
d
ia.
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c
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rre
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se
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rc
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tere
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lu
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n
teg
ra
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rg
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sy
ste
m
s,
d
istri
b
u
te
d
g
e
n
e
ra
ti
o
n
,
F
ACTS
d
e
v
ice
s,
p
o
w
e
r
c
o
n
v
e
rters
,
a
n
d
t
h
e
ir
a
p
p
li
c
a
ti
o
n
s
t
o
e
n
e
r
g
y
sy
ste
m
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
c
rre
d
d
y
2
2
9
@g
m
a
il
.
c
o
m
.
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