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on changing the integration variable to
t
=−
t
. Thus, the Fourier transform of the
time reverse of the window w(
t
) is the complex conjugate of
W
(
f
). Given that the
windoww(
t
) is real and even,
W
(
f
) is real. The convolution satisfies
∞
∞
w
n
(
s
)w
∗
n
+
m
(
s
−
τ)
ds
=
w
n
(
s
)w
n
+
m
(τ
−
s
)
ds
−∞
−∞
∞
∞
w
n
+
m
(
u
)w
∗
n
(
u
=
w
n
+
m
(
u
)w
n
(τ
−
u
)
du
=
−
τ)
du
,
(2.377)
−∞
−∞
on changing the integration variable to
u
s
, and utilising the real and even
properties of the windows. Using the foregoing properties and the convolutional
relations (2.269) and (2.274), expression (2.374) becomes
E
S
gg,
n
(
f
)
S
gg,
n
+
m
(
f
)
=
τ
−
I
2
∞
f
1
)
W
2
(
f
1
)
df
1
2
1
=
−
S
gg
(
f
−∞
I
2
∞
b
n
)
W
2
(
f
1
)
df
1
2
1
f
1
)exp
i
2π
f
1
(
b
n
+
m
−
+
S
gg
(
f
−
.
(2.378)
−∞
From (2.360), we recognise that
I
2
∞
f
1
)
W
2
(
f
1
)
df
1
2
E
S
gg,
n
(
f
)
2
1
S
gg
(
f
−
=
,
(2.379)
−∞
the square of the expected value of the sample spectral density estimate. Substitut-
ing in (2.365), we find for the covariance,
cov
S
gg,
n
(
f
),
S
gg,
n
+
m
(
f
)
I
2
∞
b
n
)
W
2
(
f
1
)
df
1
2
1
f
1
)exp
i
2π
f
1
(
b
n
+
m
−
=
S
gg
(
f
−
−∞
∞
b
n
)
W
2
(
f
)
df
2
S
2
gg
(
f
)
I
2
exp
i
2π
f
(
b
n
+
m
−
≈
,
(2.380)
−∞
for spectral densities assumed to vary only slightly over the passband of
W
2
(
f
).
The window w
n
+
m
(
t
), centred on
b
n
+
m
, is displaced along the time axis by
m
Δ
s
from the window w
n
(
t
), centred on
b
n
, where
s
is the separation of successive
windows. Now consider the convolution of the windoww
n
(
t
) with the time reverse
of the windoww
n
+
m
(
t
),
Δ
∞
w
n
(
t
)w
∗
n
+
m
(
t
J
m
(τ)
=
−
τ)
dt
.
(2.381)
−∞
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