Bài giảng Xử lý tín hiệu số: Chapter 4 - Hà Hoàng Kha
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Chapter
p 4
FIR filtering and Convolution
Ha Hoang Kha, Ph.D.Click to edit Master subtitle style
Ho Chi Minh City University of Technology
@
Email:
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Content
Block p
processingg methods
Convolution: direct form, convolution table
Convolution: LTI form,, LTI table
Matrix form
Flip-and-slide form
Overlap-add block convolution method
Sample processing methods
FIR filtering in direct form
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Introduction
Block processing methods: data are collected and processed in blocks.
FIR filtering of finite
finite-duration
duration signals by convolution
Fast convolution of long signals which are broken up in short segments
DFT/FFT spectrum computations
Speech analysis and synthesis
Image processing
Sample processing methods: the data are processed one at a timewith each input sample being subject to a DSP algorithm which
transforms it into an output sample.
Real-time applications
Digitall audio
d effects
ff
processing
Digital control systems
Adaptive signal processing
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1. Block Processing method
The collected signal samples x(n), n=0, 1,…, L-1, can be thought as a
block:
x=[x
[ 0, x1, …, xL-1]
The duration of the data record in second: TL=LT
Consider a casual FIR filter of order M with impulse response:
h=[h0, h1, …, hM]
The length (the number of filter coefficients): Lh=M+1
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11.1.
Direct form
The convolution in the direct form:
y ( n) = ∑ h( m) x ( n − m)
m
For DSP implementation,
p
, we must determine
The range of values of the output index n
The precise range of summation in m
Find index n:
index of h(m)
Æ
0≤m≤M
index of x(n-m)
x(n m) Æ 0≤n-m≤L-1
0≤n m≤L 1
Æ 0 ≤ m ≤ n ≤m+L-1 ≤ M+L-1
0 ≤ n ≤ M + L −1
Lx=L input samples which is processed by the filter with order M
M=L
Lx + M
yield the output signal y(n) of length L y = L + M
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1Direct
form
Find index m:
index of x(n-m)
(
)
index of h(m)
Æ
0≤m≤M
Æ 0≤n-m≤L-1 Æ n+L-1≤ m ≤ n
max ( 0, n − L + 1) ≤ m ≤ min ( M, n )
The direct form of convolution is given as follows:
y ( n) =
min( M , n )
∑
h( m) x ( n − m) = h ∗ x
m = max(0,
(0 n − L +1)
with 0 ≤ n ≤ M + L − 1
Thus,, y is longer
g than the input
p x byy M samples.
p This property
p p y
follows from the fact that a filter of order M has memory M and
keeps each input sample inside it for M time units.
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Example
Consider the case of an order-3 filter and a length of 5-input signal.
Find the output
o tp t ?
h=[h0, h1, h2, h3]
x=[x0, x1, x2, x3, x4 ]
yy=h*x=[y
h x [y0, y1, y2, y3, y4 , y5, y6, y7 ]
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1.2. Convolution table
It can be observed that
y ( n) =
∑ h(i) x( j )
i, j
i+ j =n
Convolution table
The convolution
table is convenient
for quick calculation
b h
by
hand
d because
b
iit
displays all required
operations
compactly.
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Example
Calculate the convolution of the following filter and input signals?
h=[1, 2, -1, 1],
x=[1, 1, 2, 1, 2, 2, 1, 1]
Solution:
S l i
sum of the values along anti-diagonal line yields the output y:
y=[1,
=[1 3,
3 3,
3 5,
5 3,
3 7,
7 4,
4 3,
3 3,
3 0,
0 1]
Note that there are Ly=L+M=8+3=11 output samples.
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1.3. LTI Form
LTI form of convolution:
y ( n ) = ∑ x ( m) h ( n − m)
m
Consider the filter h=[h0, h1, h2, h3] and the input signal x=[x0, x1, x2,
x3, x4 ]. Then, the output is given by
y (n) = x0 h(n) + x1h(n − 1) + x2 h(n − 2) + x3 h(n − 3) + x4 h(n − 4)
We can represent the input and output signals as blocks:
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1.3. LTI Form
LTI form of convolution:
LTI form of convolution provides a more intuitive way to under
stand the linearity and time-invariance
time invariance properties of the filter.
filter
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Example
Using the LTI form to calculate the convolution of the following
filter and inp
inputt signals?
h=[1, 2, -1, 1],
x=[1, 1, 2, 1, 2, 2, 1, 1]
Solution:
S l i
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1.3. Matrix Form
Based on the convolution equations
we can write y = Hx
x is the column vector of the Lx input
p samples.
p
y is the column vector of the Ly =Lx+M put samples.
H is a rectangular matrix with dimensions (Lx+M)xLx .
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1.3. Matrix Form
It can be
b observed
b
d th
thatt H has
h th
the same entry
t along
l
each
h diagonal.
di
l
Such a matrix is known as Toeplitz matrix.
Matrix representations of convolution are very useful in some
applications:
Image processing
Advanced DSP methods such as parametric spectrum estimation and adaptive
filtering
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Example
Using the matrix form to calculate the convolution of the following
filter and input signals?
h=[1, 2, -1, 1],
x=[1, 1, 2, 1, 2, 2, 1, 1]
Solution: since Lx=8, M=3 Æ Ly=Lx+M=11, the filter matrix is
11x8 dimensional
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1.4. Flip-and-slide form
The output at time n is given by
yn = h0 xn + h1 xn −1 + ... + hM xn − M
Flip-and-slide
li
d lid fform off convolution
l i
The flip-and-slide form shows clearly the input-on and input-off
transient and steady-state
steady state behavior of a filter.
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1.5. Transient and steady-state behavior
M
From LTI convolution: y(n) = ∑h(m)x(n − m) = h0 xn + h1xn−1 + ... + hM xn−M
m=0
The output is divided into 3 subranges:
Transient
T
i andd steady-state
d
fil outputs:
filter
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1.6. Overlap-add block convolution method
As the input signal is infinite or extremely large, a practical approach
is to divide the longg input
p into contiguous
g
non-overlapping
pp g blocks of
manageable length, say L samples.
Overlap
Overlap-add
add block convolution method:
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Example
Using the overlap-add method of block convolution with each bock
length L=3, calculate the convolution of the following filter and
input signals? h=[1, 2, -1, 1],
x=[1, 1, 2, 1, 2, 2, 1, 1]
Solution: The input is divided into block of length L
L=33
The output of each block is found by the convolution table:
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Example
The output of each block is given by
Following
F ll i from
f
time
i invariant,
i
i
aligning
li i the
h output blocks
bl k according
di
to theirs absolute timings and adding them up gives the final results:
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2. Sample processing methods
The direct form convolution for an FIR filter of order M is given by
Introduce the internal states
Sample
p p
processingg algorithm
g
Fig: Direct form realization
off Mth
M h order
d fil
filter
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Sample
processing
methods
convenient for real-time
real time applications
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Example
Consider the filter and input given by
Using the sample processing algorithm to compute the output and
show the input-off transients.
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Example
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Example
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Hardware realizations
The FIR filtering algorithm can be realized in hardware using DSP
chips, for example the Texas Instrument TMS320C25
MAC: Multiplier
Accumulator
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