In mathematics, a rectangular mask short-time Fourier transform has the simple form of short-time Fourier transform . Other types of the STFT may require more computation time than the rec-STFT.
Define its mask function
w
(
t
)
=
{
1
;
|
t
|
≤
B
0
;
|
t
|
>
B
{\displaystyle w(t)={\begin{cases}\ 1;&|t|\leq B\\\ 0;&|t|>B\end{cases}}}
B = 50, x -axis (sec)
We can change B for different signal.
Rec-STFT
X
(
t
,
f
)
=
∫
t
−
B
t
+
B
x
(
τ
)
e
−
j
2
π
f
τ
d
τ
{\displaystyle X(t,f)=\int _{t-B}^{t+B}x(\tau )e^{-j2\pi f\tau }\,d\tau }
Inverse form
x
(
t
)
=
∫
−
∞
∞
X
(
t
1
,
f
)
e
j
2
π
f
t
d
f
where
t
−
B
<
t
1
<
t
+
B
{\displaystyle x(t)=\int _{-\infty }^{\infty }X(t_{1},f)e^{j2\pi ft}\,df{\text{ where }}t-B<t_{1}<t+B}
Property
Rec-STFT has similar properties with Fourier transform
(a)
∫
−
∞
∞
X
(
t
,
f
)
d
f
=
∫
t
−
B
t
+
B
x
(
τ
)
∫
−
∞
∞
e
−
j
2
π
f
τ
d
f
d
τ
=
∫
t
−
B
t
+
B
x
(
τ
)
δ
(
τ
)
d
τ
=
{
x
(
0
)
;
|
t
|
<
B
0
;
otherwise
{\displaystyle \int _{-\infty }^{\infty }X(t,f)\,df=\int _{t-B}^{t+B}x(\tau )\int _{-\infty }^{\infty }e^{-j2\pi f\tau }\,df\,d\tau =\int _{t-B}^{t+B}x(\tau )\delta (\tau )\,d\tau ={\begin{cases}\ x(0);&|t|<B\\\ 0;&{\text{otherwise}}\end{cases}}}
(b)
∫
−
∞
∞
X
(
t
,
f
)
e
−
j
2
π
f
v
d
f
=
{
x
(
v
)
;
v
−
B
<
t
<
v
+
B
0
;
otherwise
{\displaystyle \int _{-\infty }^{\infty }X(t,f)e^{-j2\pi fv}\,df={\begin{cases}\ x(v);&v-B<t<v+B\\\ 0;&{\text{otherwise}}\end{cases}}}
Shifting property(shift along x-axis)
∫
t
−
B
t
+
B
x
(
τ
+
τ
0
)
e
−
j
2
π
f
τ
d
τ
=
X
(
t
+
τ
0
,
f
)
e
j
2
π
f
τ
0
{\displaystyle \int _{t-B}^{t+B}x(\tau +\tau _{0})e^{-j2\pi f\tau }\,d\tau =X(t+\tau _{0},f)e^{j2\pi f\tau _{0}}}
Modulation property (shift along y -axis)
∫
t
−
B
t
+
B
[
x
(
τ
)
e
j
2
π
f
0
τ
]
d
τ
=
X
(
t
,
f
−
f
0
)
{\displaystyle \int _{t-B}^{t+B}[x(\tau )e^{j2\pi f_{0}\tau }]d\tau =X(t,f-f_{0})}
When
x
(
t
)
=
δ
(
t
)
,
X
(
t
,
f
)
=
{
1
;
|
t
|
<
B
0
;
otherwise
{\displaystyle x(t)=\delta (t),X(t,f)={\begin{cases}\ 1;&|t|<B\\\ 0;&{\text{otherwise}}\end{cases}}}
When
x
(
t
)
=
1
,
X
(
t
,
f
)
=
2
B
sinc
(
2
B
f
)
e
j
2
π
f
t
{\displaystyle x(t)=1,X(t,f)=2B\operatorname {sinc} (2Bf)e^{j2\pi ft}}
If
h
(
t
)
=
α
x
(
t
)
+
β
y
(
t
)
{\displaystyle h(t)=\alpha x(t)+\beta y(t)\,}
,
H
(
t
,
f
)
,
X
(
t
,
f
)
,
{\displaystyle H(t,f),X(t,f),}
and
Y
(
t
,
f
)
{\displaystyle Y(t,f)\,}
are their rec-STFTs, then
H
(
t
,
f
)
=
α
X
(
t
,
f
)
+
β
Y
(
t
,
f
)
.
{\displaystyle H(t,f)=\alpha X(t,f)+\beta Y(t,f).}
Power integration property
∫
−
∞
∞
|
X
(
t
,
f
)
|
2
d
f
=
∫
t
−
B
t
+
B
|
x
(
τ
)
|
2
d
τ
{\displaystyle \int _{-\infty }^{\infty }|X(t,f)|^{2}\,df=\int _{t-B}^{t+B}|x(\tau )|^{2}\,d\tau }
∫
−
∞
∞
∫
−
∞
∞
|
X
(
t
,
f
)
|
2
d
f
d
t
=
2
B
∫
−
∞
∞
|
x
(
τ
)
|
2
d
τ
{\displaystyle \int _{-\infty }^{\infty }\int _{-\infty }^{\infty }|X(t,f)|^{2}\,df\,dt=2B\int _{-\infty }^{\infty }|x(\tau )|^{2}\,d\tau }
∫
−
∞
∞
X
(
t
,
f
)
Y
∗
(
t
,
f
)
d
f
=
∫
t
−
B
t
+
B
x
(
τ
)
y
∗
(
τ
)
d
τ
{\displaystyle \int _{-\infty }^{\infty }X(t,f)Y^{*}(t,f)\,df=\int _{t-B}^{t+B}x(\tau )y^{*}(\tau )\,d\tau }
∫
−
∞
∞
∫
−
∞
∞
X
(
t
,
f
)
Y
∗
(
t
,
f
)
d
f
d
t
=
2
B
∫
−
∞
∞
x
(
τ
)
y
∗
(
τ
)
d
τ
{\displaystyle \int _{-\infty }^{\infty }\int _{-\infty }^{\infty }X(t,f)Y^{*}(t,f)\,df\,dt=2B\int _{-\infty }^{\infty }x(\tau )y^{*}(\tau )\,d\tau }
Rectangular mask B 's effect
comparison of different B
From the image, when B is smaller, the time resolution is better. Otherwise, when B is larger, the frequency resolution is better.
We can choose specified B to decide time resolution and frequency resolution.
Advantage and disadvantage
Compare with the Fourier transform
Advantage
The instantaneous frequency can be observed.
Disadvantage
Higher complexity of computation.
Compared with other types of time-frequency analysis:
The rec-STFT has an advantage of the least computation time for digital implementation,
but its performance is worse than other types of time-frequency analysis.
See also
References
Jian-Jiun Ding (2014) Time-frequency analysis and wavelet transform