Transcript EE513 Audio Signals and Systems Digital Signal Processing (Analysis) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.

EE513
Audio Signals and Systems
Digital Signal Processing (Analysis)
Kevin D. Donohue
Electrical and Computer Engineering
University of Kentucky
Spectra
The spectrum of the impulse response indicates the following
filter/system properties:
 resonance and frequency sensitivities through the magnitude spectrum
 delay and dispersive properties through the phase spectrum.
The discrete Fourier transform (DFT) computes complex
samples of the spectrum.
 For deterministic signals both the phase and magnitude are important
for characterizing the signal or response.
 For stationary noise processes the square of the DFT magnitudes can
be averaged from independent segments to create a power spectral
density (PSD).
 An efficient implement for computing the DFT is referred to as the
fast Fourier transform (FFT). These terms are sometimes used
interchangeably.
Discrete Fourier Transform
In order to take a DFT of a signal, a finite window (time interval) must be
extracted from the original sampled time signal:
0.8
1000
0.6
DFT Magnitude Squared
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0.2
0
-0.2
-0.4
-0.6
800
600
400
200
-0.8
3.2
3.4
3.6
3.8
4
4.2
4.4
Samples
4.6
4.8
5
5.2
4
x 10
0
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
Hz (Normalized Fs = 1)
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0.3
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4
0.6
0.2
0
DFT Phase
-0.2
DFT Phase in Radians
3
0.4
2
1
0
-1
-2
-0.4
-3
-0.6
4.34
4.345
4.35
4.355
4.36
4.365 4.37
Samples
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4.385
4.39
4
x 10
-4
-0.5
-0.4
-0.3
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-0.1
0
0.1
Hz (Normalized Fs = 1)
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The process can be repeated with overlapping windows covering entire signal.
 If the signal is deterministic, the DFTs for small consecutive intervals must be displayed to
show the changes in spectrum over time (Spectrogram).
 If the signal is random with statistics that do not change over time, the DFT magnitudes can
be averaged (Power Spectral Density (PSD)).
0.5
DFT and Inverse DFT
DFT points are evaluated at discrete frequency samples
(frequency sampling). The DFT for an N point sequence is
defined as:
Xˆ k  
N 1
2k 

x(n) exp  jn

N 

n 0

for 0  k  N
This inverse DFT is a similar computation to the DFT
except the complex conjugate of the kernel is used:
1
xn  
N
N 1
 2n 
ˆ
X (k ) exp jk

N 

k 0

for 0  n  N
DFT with W notation
Let WN equal the complex sinusoid with the lowest nonzero
frequency (k = 1):
2 

WN  exp  j

N 

WN is effectively a fundamental frequency in a harmonic
expansion, since the frequency domain is sampled in equal
increments. DFT and its inverse can now be written as:
N 1
kn
ˆ
X k    x(n)WN
for 0  k  N
n 0
1 N 1 ˆ
 nk
xn   X (k )WN
N k 0
for 0  n  N
Computation Examples
Compute the DFT for the 4 point and 8 point
signals given below:
x(0), x(1), x(2), x(3)  0, 1,1, 0
x(0), x(1),  x(7)  0, 1,1, 0, 0, 0, 0, 0
What was the effect of extra zeros padded on
the end of the signal in the second example?
Computation Examples
IM
Compute the DFT for
the 4 point 0, 1,1, 0
90
120
60
W34 = j
150
30
W 24 = -1
0
180
W04= 1
210
3
RE
Xˆ k    x(n)W4kn
for 0  k  4
n 0
330
W24=
-j
3
240
300
270
k  0, Xˆ k    x(n)W40  0W40  1W40  1W40  0W40  2
n 0
3
k  1, Xˆ k    x(n)W41n  0W40  1W41  1W42  0W43   j  1  2  135
n 0
3
k  2, Xˆ k    x(n)W42n  0W40  1W42  1W40  0W42  0
n 0
3
k  3, Xˆ k    x(n)W43n  0W40  1W43  1W42  0W41  1  j  2135
n 0
Computation Examples
W68 = j
90
120
60
2
W58 = 1  j 
2
150
W 78=
2
1  j 
2
30
Compute the DFT for the
8 point 0, 1,1, 0, 0, 0, 0, 0
7
W08= 1
180
Xˆ k    x(n)W4kn
n 0
0
W48 = -1
for 0  k  8
7
k  0, Xˆ k    x(n)W80  0W80  1W80  1W80  0W80  0  2
n 0
210
330
2
W38 = 2 1  j 
240
300
270
W28=
-j
7
k  1, Xˆ k    x(n)W81n  0W80  1W81  1W82  0W83  0  .71 j1.71  1.85  67.5
2
1
n 0
 1  j 
W8 = 
2
7
k  2, Xˆ k    x(n)W82 n  0W80  1W82  1W84  0W86  0  .  1  j  2  135
n 0
7
k  3, Xˆ k    x(n)W83n  0W80  1W83  1W86  0W81  0  .71 j 0.29  0.76157.5
n 0
7
k  4, Xˆ k    x(n)W84 n  0W80  1W84  1W80  0W84  0  0
n 0
7
k  5, Xˆ k    x(n)W85n  0W80  1W85  1W82  0W87  0  .71 j 0.29  0.76  157.5
n 0
7
k  6, Xˆ k    x(n)W86 n  0W80  1W86  1W84  0W82  0  1  j  2135
n 0
7
k  7, Xˆ k    x(n)W87 n  0W80  1W87  1W86  0W85  0  .71 j1.71  1.8567.5
n 0
Computation Examples
 What symmetries exist in the WN factor in the DFT
computation that suggest a more efficient computation than
a direct multiply and summation? The implementation that
takes advantage of these symmetries is called a Fast Fourier
Transform (FFT).
 If the sequence is a power of 2, the FFT is most efficient.
A direct implementation of the DFT requires N2 complex
multiplications. An FFT requires Nlog2N complex
multiplications.
 Compute the ratio between complex multiplications for the
FFT and DFT for a 128 point sequence. Compute the
same ratio for a 2048 point sequence.
 See http://www.cmlab.csie.ntu.edu.tw/cml/dsp/training/coding/transform/fft.html
For more details on the FFT derivation
Homework(3.1)
Derive each of the symmetry relationships
below for the WN factor:
WNk  WN N k  for N  1
WNnk  WN( N n ) k  for any n, k   and N  1
*
N

 k 
 2

N
WNk  W
for N  1, and N even
WNnk  WN2 nk for any n, k  , N  1 and N even
2
WNnk WNnk   N
N 1
n 0
*
for any k  , N  1
Frequency Sampling, FS to DFT
Time sample the following FS equations to show it
equivalence to the DFT.
x(t ) 

1
ˆ
X
[
k
]
exp(
j
2

kf
t
)
for
x
(
t
)

x
(
t

T
),
T


o
fo
k  
1
Xˆ [k ]   x(t ) exp( j 2kfot )dt
TT
Sample time axis with Ts where NTs = T
DFT Harmonics (N=4)
x(n) with kernel for k= 0
x(n) with kernel for k= 1
1
1
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0
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-0.5
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0
0
1
2
Samples
3
4
-1
0
1
2
Samples
x(n) with kernel for k= 2
1
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0.5
0
0
-0.5
-0.5
0
1
2
Samples
4
x(n) with kernel for k= 3
1
-1
3
3
4
-1
0
1
2
Samples
3
4
DFT Harmonics (N=8)
x(n) with kernel for k= 0
x(n) with kernel for k= 3
1
0.8
x(n) with kernel for k= 6
1
1
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0.5
0
0
-0.5
-0.5
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0.4
0.2
0
0
2
4
Samples
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8
-1
0
2
x(n) with kernel for k= 1
4
Samples
6
8
-1
1
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0
0
0
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-0.5
-0.5
2
4
Samples
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8
-1
0
2
x(n) with kernel for k= 2
1
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0
0
-0.5
-0.5
0
2
4
Samples
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8
6
8
x(n) with kernel for k= 5
1
-1
4
Samples
6
8
-1
0
2
4
Samples
4
Samples
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8
6
8
x(n) with kernel for k= 7
1
0
2
x(n) with kernel for k= 4
1
-1
0
-1
0
2
4
Samples
FFT Spectral Estimates
Distortion and error result from the truncated
and frequency sampled signal because:
Lost energy from truncated time portion will
distribute error over the signal.
Truncating signal at the endpoints can introduce a
sharp transition that may not be part of the signal.
Sampling signal in frequency produces periodicity
in time, which results in circular time-domain
convolution from multiplying spectra in frequency
domain.
Frequency Sampling Effect
The DFT can be applied to a finite duration signal. If signal support is
infinite, part of it must be truncated. The DFT definition is applied to the
finite number of points to obtain a frequency sampled spectrum, which
results in a periodic extension of the signal in the time window.
Sampled signal for DFT analysis
Sampled signal for DFT analysis
1
1
Truncate Signal 30 Seconds
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0
-0.2
0
-0.2
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-0.4
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-0.6
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-1
0
5
10
15
20
25
Truncate Signal 150 Samples
0.8
Amplitude
Amplitude
0.8
30
Time in Seconds
35
40
45
50
-1
0
50
100
150
Time in Sample Index
200
250
Frequency Sampling Effect
The DFT representation implies the time domain signal was
periodic (sampling in time produces periodic spectra, likewise
sampling in frequency produces periodic time segments) as
would be the case for Fourier series.
Periodic Extension from Frequency Sampling
1
0.8
Original
150 point signal
0.6
Amplitude
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0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-150
-100
-50
0
50
100
150
Time in Sample Index
200
250
300
Circular Convolution
Multiplication in frequency is convolution in time. For
discrete signals, this can be performed with the fft
command in Matlab on the filter coefficients h(n) and input
signal x(n), then the ifft command on their frequency
domain product:

Time
Domain
Frequency
Domain
y(n)  x(n) * h(n) 
 x(m)h(n  m)
m 0
N 1
kn
ˆ
X k    x(n)WN
n 0
kn
ˆ
H k    h(n)WN
Yˆ (k )  Xˆ (k )Hˆ (k )
Time
Domain
N 1
1 N 1 ˆ
yn    Y (k )WNnk
N k 0
n 0
Linear Convolution Example
Linear
convolution
implemented
in the time
domain.
Note the
convolved
signal length
is greater
than either of
the originals.
Why?
Filter Impulse Response 128 Points
0.3
0.2
0.1
0
-0.1
20
40
60
80
100
120
140
100
120
140
Signal 128 Points
2
0
-2
-4
20
40
60
80
Linear Convolution in Time Domain 148 Points
0.5
0
-0.5
-1
-1.5
20
40
60
80
100
120
140
Circular Convolution Example
Convolution
implemented
in the
frequency
domain.
Note the
convolved
signal length
is the same as
the originals.
Why?
Why is there
an apparent
artifact at the
beginning of
the signal?
Filter Impulse Response 128 Points
0.3
0.2
0.1
0
-0.1
20
40
60
80
100
120
140
100
120
140
Signal 128 Points
2
0
-2
-4
20
40
60
80
Circular Convolution Implemented in Frequency Domain 128 Points
0.5
0
-0.5
-1
-1.5
20
40
60
80
100
120
140
Circular Convolution Example
Convolution
implemented
in the
frequency
domain with
zero padding.
Why is the
apparent
artifact from
the nonzero
pad example
no longer
present?
Filter Impulse Response 256 Points by Zero Padding
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0.2
0.1
0
-0.1
50
100
150
200
250
Signal 256 Points by Zero Padding
2
0
-2
-4
50
100
150
200
250
Circular Convolution Implemented in Frequency Domain 256 Points
0.5
0
-0.5
-1
-1.5
50
100
150
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250
Homework(3.2)
Use the FFT to filter a 32 point square pulse s[n]:
1
s[n]  
0
for 29  n  16
elsewhere
(index n starts a 0)
where the FIR filter has impulse response h[n]:
 .6n for12  n  0
h[n]  
for 32  n  12
0
a) Use the fft command to take signals into frequency domain without
padding with zeros. Take inverse fft to obtain time domain signal. Plot the
filtered signals and explain what you observe.
b) Use the fft command and pad with zeros to double the signal lengths and
repeat part (a)
FFT for Signal Analysis
The FFT can be applied directly to finite duration
energy signals to examine the spectral content of
the signal.
For Matlab’s FFT:
 FFT(X) is the discrete Fourier transform (DFT) of
vector X. For matrices, the FFT operation is
applied to each column.
 FFT(X,N) is the N-point FFT, padded with zeros
if X has less than N points and truncated if it has
more.
FFT of a Rayleigh Ring
Write a Matlab script to Generate a tone
modulated by a Rayleigh envelope. Plot the
signal, its spectra, and play the sound.
% Simulate a Rayleigh envelope ring, truncate it, take its FFT and examine
% its spectral content
fr = 250; % Frequency of the ring
sigma = .05; % effective duration TIMES 2 of ring
fs = 8000; % Sampling frequency
dur = .25; % signal duration in seconds
nfft = 4096;
t = [0:round(dur*fs)-1]/fs; % Create time axis
ring = (t/sigma).*exp((-t.^2)/(sigma^2)).*sin(2*pi*t*fr); % Generate Ring signal
figure(1)
plot(t, ring) % Plot ring
title('Rayleigh Envelope Ring Signal')
xlabel('Seconds')
ylabel('Amplitude')
soundsc(ring,fs) % Play sound
FFT of a Rayleigh Ring
spec0 = fft(ring); % No Zero pad
spec1 = fft(ring,nfft); % Zero pad (or truncate) to NFFT points
spec2 = fft(ring,2*nfft); % Zero pad to 2*NFFT
% Create frequency axes for each of the spectra
faxis0 = fs*[0:length(spec0)-1]/length(spec0);
faxis1 = fs*[0:length(spec1)-1]/length(spec1);
faxis2 = fs*[0:length(spec2)-1]/length(spec2);
% Plot spectrum with zero padded version on the same graph and compare
figure(2) % Magnitude
plot(faxis0, abs(spec0),'k',faxis1, abs(spec1),'r--',faxis2, abs(spec2),'g.')
legend([int2str(length(t)) ' point FFT'], [int2str(nfft) ' point FFT'], [int2str(2*nfft) ' point FFT'])
title('Magnitude ffts with zero padding')
xlabel('Hertz')
ylabel('Magnitude')
figure(3) % Phase
plot(faxis0, 180*phase(spec0)/pi,'k',faxis1, 180*phase(spec1)/pi,'r--',faxis2, 180*phase(spec2)/pi,'g.')
legend([int2str(length(t)) ' point FFT'], [int2str(nfft) ' point FFT'], [int2str(2*nfft) ' point FFT'])
title('Phase ffts with zero padding')
xlabel('Hertz')
ylabel('Degrees')
FFT of a Rayleigh Ring
ffts with zero padding
2000 point FFT
4096 point FFT
8192 point FFT
100
(Zero Padding Effects)
90
80
Amplitude
70
Rayleigh Envelope Ring Signal
0.5
60
50
40
0.4
30
20
0.2
10
0.1
0
210
0
220
230
240
250
260
270
Hertz
Phase ffts with zero padding
-0.1
-0.2
50
-0.3
0
280
290
2000 point FFT
4096 point FFT
8192 point FFT
-0.4
-50
-0.5
0
0.05
0.1
0.15
Seconds
0.2
0.25
Degrees
Amplitude
0.3
-100
-150
-200
-250
200
210
220
230
240
250 260
Hertz
270
280
290
300
Homework(3.3)
Create a signal consisting of 2 sine waves with amplitude 1
and sampled at 4000 Hz. Set one with frequency 250 Hz
and the other with 254 Hz.
a) Take the DFT using window length of 0.05, 0.25 and
0.5 seconds. Describe what you see and make
generalizations about the impact of signal length on
frequency resolution.
b) Repeat part (a) using zeros padding so that each signal
length is effectively 1 seconds. Describe what you see and
make generalization about the impact zero padding on
frequency resolution.
FFT of Extended Non-Stationary Signals
 When the signal is long with changing dynamics,
as would be the case for a speech or music signal,
the FFT must be applied to short segments of
these signals.
 To reduce truncation effects, a tapering is used to
bring the ends of the segment close to zero a the
points of truncation.
 To ensure that information is not lost in the
truncation and tapering, an overlap between signal
segments is used.
Impact of Windowing
1.5
0.5
0
-0.5
xw[n; m]  x[n]g[n  m]
-1
-1.5
0
100
200
300
Samples
400
500
100
200
400
500
3
2
1
amplitude
where x[n] is a signal of
infinite extend, n is the
sample index, g[n] is a
finite widow.
Hamming Window 2
1
Amplitude
The windowed DFT is the
product of the original
signal and the windowing
function:
Hamming Window 1
0
-1
-2
-3
0
300
samples
Impact of Windowing
4
amplitude
In the frequency domain this
becomes convolution with the
window DFT:
Hamming window
2
0
-2
-4
0
100
200
300
samples
400
500
400
500
4
N 1
Xˆ w [k ; m]   Xˆ [l ]Gˆ [k  l ] exp( j 2km)
Boxcar window
2
0
l 0
-2
-4
0
100
200
300
0.35
Hamming window
0.3
0.3
Boxcar window
Boxcar window
Not windowed (very long segment)
0.25
Magnitude
Magnitude
0.25
Hamming window
0.2
0.15
0.15
0.1
0.1
0.05
0.05
0
0.02
0.04
0.06
0.08
0.1
Not windowed (very long segment)
0.2
0
0
0.1
0.2 Hertz
0.3
0.4
0.5
Spectrogram for Signal Analysis
For a signal that deterministically changes spectral properties over
time, the DFT over local time windows must be computed and
indexed. This function is referred to as the spectrogram:
N 1
Xˆ N [k ; m]   x[n  m T]g[n]WNnk
n 0
where m is the time index, T is the sample increment to the next
window position, which effectively slides the N point analysis window
over consecutive (often overlapping) segments of the signal x[n]. The
DFTs are plotted along the y-axis, while the x-axis is time (usually
corresponding to the short-time window centers. The result is a
function that shows how the spectrum changes over time.
The following parameters must be set properly for a usable analysis:
window length N, window type, window increment (overlap between
consecutive windows), number of FFT points.
Spectrogram for Signal Analysis
The Matlab spectrogram command is appropriate for analyzing long
deterministic or non-stationary signals.
For Matlab’s SPECTROGRAM:
>> S = SPECTROGRAM(X,WINDOW,NOVERLAP,NFFT,Fs)
calculates the spectrogram for the signal sampled at Fs in vector X.
SPECTROGRAM splits the signal into overlapping segments,
windows each with the WINDOW vector and forms the columns of S
with their zero-padded, length NFFT discrete Fourier transforms.
Thus each column of S contains an estimate of the short-term, timelocalized frequency content of the signal X (positive frequencies only).
Time increases linearly across the columns of S, from left to right.
Spectrogram for Signal Analysis
If plotting, it is convenient to use the optional output arguments:
[S,F,T] = SPECTROGRAM(A, WINDOW, NOVERLAP, NFFT, Fs)
returns a column of frequencies F for each time in T at which the
spectrum was computed. F has length equal to the number of rows of
S, and T has length equal to the number of columns. If you leave Fs
unspecified, SPECTROGRAM assumes a default of 2 Hz. Optional
output parameters F and T are the frequency and time axes useful for
plotting the spectrum:
>> imagesc(T, F, abs(S))
Spectrogram of Frequency Sweep
Write a Matlab script to Generate a frequency sweep from
20 Hz to 1.9 kHz, where Fs is 4 kHz. Then plot the
magnitude of the spectrogram and play the sound.
% Simulate a frequency sweep signal and take its spectrogram and examine
% its spectral content over time
% Signal Parameters
flow = 20; % Starting frequency of sweep in Hertz
fend = 1900; % Ending frequency of sweep in Hertz
dt = 4; % Time duration of sweep in seconds
fs = 4000; % Sampling frequency
% Spectrogram Parameters
wlen = 128;
% Length of actual point extracted from signal segment
nfft = 1024;
% number of FFT point (zero padding)
olap = floor(wlen/2); % Points of overlap between segments
wn = hamming(wlen);
% Create tapering window (also try boxcar - square window)
Spectrogram of Frequency Sweep
% Generate signal
t = [0:round(dt*fs)-1]/fs; % Create time axis
fsw = flow + ((fend-flow)/2)*[0:length(t)-1]/length(t); % Create Frequency ramp - Why divid by 2?
swp = sin(2*pi*t.*fsw); % Generate sweep signal
soundsc(swp,fs) % Play sound
% Create Spectrogram
[b,faxis,taxis] = spectrogram(swp,wn,oplap,nfft,fs);
% Plot over time and frequency
figure(1); imagesc(taxis, faxis, abs(b)) % Plot spectrogram
axis('xy') % Flip y axis to put zero Hz on bottom
colorbar % Include colorbar to determine color coded magnitudes on graph
title(['Frequency Sweep from ' num2str(flow) ' Hz to ' num2str(fend) ' Hz']); xlabel('Seconds'); ylabel('Hz')
% Just plot at a single time instant
figure(2);
tindex = find(taxis >= dt/2); %Find index of halfway point over the time axis
tindex = tindex(1);
plot(faxis,abs(b(:,tindex)))
title(['Single column from Spectrogram at ' num2str(taxis(tindex)) ' seconds']); xlabel('Hz'); ylabel('Magnitude')
Spectrogram of Frequency Sweep
Frequency Sweep from 20 Hz to 1900 Hz
2000
35
1800
30
1600
25
1400
20
1000
15
800
600
10
400
5
Single column from Spectrogram at 2 seconds
200
0
35
0
0.5
1
1.5
2
Seconds
2.5
3
3.5
30
25
Magnitude
Hz
1200
20
15
10
5
0
0
200
400
600
800
1000
Hz
1200
1400
1600
1800
2000
Homework(3.4)
Use the scale program created in a previous homework problem to
generate a scale for 2 octaves starting at 256 Hz with tones of
amplitude 1 and duration 0.25 seconds. Use a sampling rate of 4kHz.
A) Compute and plot the spectrogram magnitude in dB, labeling all axes
correctly. Use the parameters you think best for estimating the
spectrogram for this signal. Comment on the frequencies of the tones
generated with your program and those identified through the
spectrogram.
B) Quadruple the number of FFT points from that used in (A). Compute
and plot the spectrogram, and explain difference observe from part
(A).
C) Increase the window length by a factor of 4 and set the number of FFT
points to twice the amount of the window length. Compute and plot
the spectrogram, and explain the observed changes.
Random Signal Analysis
If a process is changing over time, while the statistics of
the process do not change over time, the process is referred
to as a stationary process. In this case averaging and
moment computations result in useful characterization.
If the signal’s (or process’) probability density function
never changes over time, then it is referred to as strictsense stationary (SSS), if the first and second moments
remain constant over time, the process is referred to as
wide-sense stationary (WSS).
Power Spectral Density
The Power Spectral Density (PSD) is the average power in
a signal as a function of frequency. This can be estimated
by averaging DFT magnitudes squared over segments of
signals from the same noise process.


2
1 N ˆ
P( f )  E S ( f )   Si ( f )
N n1
2
where E[] is the expected value, S(f) is the data spectrum
plus noise (i.e. modeled as a random variable) and Sˆi ( f ) is
the DFT or short-time FT estimated from independent
signal segments. P ( f ) is the true or underlying spectrum.
PSD Example Colored Noise
A popular approach to spectral estimation is to is to
window a long segment of data into a sequences of shorter
windows (similar to the spectrogram) and average all the
FFT magnitudes together. This is referred to as Welch's or
the hopping window method. The Matlab command
pwelch() implements this method.
Example: Filter white noise through a band-pass filter and
compute the PSD. Compare PSD to the transfer function
magnitude of the filter. Assume a sampling rate of 8kHz
and a 6th order Butterworth filter with passband from 500
to 1500 kHz.
PSD Example Colored Noise
Window Length = 32
1.4
Spectral Estimate
Filter Magnitude Response
1.2
PSD
1
0.8
0.6
0.4
0.2
0
0
500
1000
1500
2000
2500
3000
3500
4000
Hertz
Window Length = 128
1.4
Spectral Estimate
Filter Magnitude Response
1.2
PSD
1
0.8
0.6
0.4
0.2
0
0
500
1000
1500
2000
2500
3000
3500
4000
Hertz
Window Length 512
1.4
Spectral Estimate
Filter Magnitude Response
1.2
1
PSD
fs = 8000; % Sampling Frequency
dur = 20; % Sound duration in seconds
ord = 6;
plen = 32; % PSD segment length
% Bandlimit on the filter
f1 = [500]; % lower bandlimit
f2 = [1500]; % corresponding upper bandlimit
% colors for the plots
col = ['g', 'r', 'b', 'k', 'c', 'b'];
no = randn(1,round(fs*dur)); % Generate noise signal
[b,a] = butter(ord,2*[f1 f2]/fs); % Generate filter
% perform filter operation to get colored noise
cno = filter(b,a,no);
% Compute PSD of noise
[p, fax] = pwelch(cno,hamming(plen),fix(plen/2),2*plen, fs);
figure(1); lh = plot(fax,abs(fs*p/2),col(1)) % Plot PSD
set(lh,'LineWidth',2) % Make line thicker
hold on
% Find filter transfer function
[h,fq] = freqz(b,a,2*plen,fs);
plot(fq,abs(h).^2,col(2))
hold off
% Label figure
xlabel('Hertz', 'Fontsize', 14); ylabel('PSD', 'Fontsize', 14)
0.8
0.6
0.4
0.2
0
0
500
1000
1500
2000
Hertz
2500
3000
3500
4000
Autocorrelation Function
As a measure of how quickly a signal changes on
average, or for the detection of periodicities, the
autocorrelation can be used. It directly measures the
correlation between samples separate in time by a
particular interval or lag.
Define the autocorrelation of a sequence as:
N 1
r(k ) 
 x(n  k )x(n)
n 0
where
x ( n)  0
and  N  k  N
for n  0 and n  N-1
Autocorrelation Example
Repeat previous example for signal generation and
compute the autocorrelation function. Show
autocorrelation for white noise (filter input) and colored
noise (filter output).
% Compute auto correlation of input sequence
mxlag = fs*.02; % Only compute lags up to .1 seconds
[acwno, lagwno] = xcorr(no, mxlag, 'coef');
% Plot lags
figure(2); plot(1000*lagwno/(fs),acwno)
xlabel('milliseconds','Fontsize', 14); ylabel('Correlation coefficient','Fontsize', 14)
% Compute auto correlation of output sequence
mxlag = fs*.02; % Only compute lags up to .1 seconds
[accno, lagcno] = xcorr(cno, mxlag, 'coef');
% Plot lags
figure(3); plot(1000*lagcno/(fs),accno)
xlabel('milliseconds','Fontsize', 14); ylabel('Correlation coefficient','Fontsize', 14)
Autocorrelation Example
Repeat previous example for signal generation and
compute the autocorrelation function. Show
autocorrelation for white noise (filter input) and colored
noise (filter output).
White noise AC
Colored noise AC
1.2
1
0.8
Correlation coefficient
Correlation coefficient
1
0.8
0.6
0.4
0.2
0
-0.2
-20
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-15
-10
-5
0
milliseconds
5
10
15
20
-0.8
-20
-15
-10
-5
0
milliseconds
5
10
15
20
Autocorrelation and PSD
The AC and PSD are DFT pairs. The DFT of the AC is
shown below:
DFT of AC
1
4.5
0.8
4
0.6
3.5
0.4
3
Magnitude
Correlation coefficient
Colored noise AC
0.2
0
2.5
2
-0.2
1.5
-0.4
1
-0.6
0.5
-0.8
-20
-15
-10
-5
0
milliseconds
5
10
15
20
0
0
500
1000
1500
2000
Hertz
2500
3000
3500
4000
Homework(3.5)
Generate 3 seconds of white noise at a sampling rate of 44.1 kHz. Use
the following coefficients in an IIR filter to filter the white noise and
generate pink noise (bnum are numerator and aden are denominator
coefficients).
bnum = [ 0.04957526213389, -0.06305581334498, 0.01483220320740 ]
aden = [ 1.00000000000000, -1.80116083982126, 0.80257737639225]
Coefficients from http://music.columbia.edu/pipermail/music-dsp/2001-November/046099.html
A) Estimate the AC and PSD of the Pink noise and present their plots.
Comment on the observed differences from white noise.
B) Add a sinusoid of frequency 220 Hz to the pink noise signal with
power equal to that of the pink noise (i.e. p = std(pink noise signal) and
sig = sqrt(2)*p*sin()) repeat part (A). Show the plots and explain the
differences observed with the plots from part (A). Does this make
sense since the sine function is not random?
Homework (3.6)
a) Create a tone at 450 Hz with sampling rate 8000, amplitude
0.707, and duration 3 seconds. Add white noise (use the
randn function) with a signal-to-noise ratio of 12 dB. Use
the fir1 command to design a 30 and 120th order band-pass
filter from 400 Hz to 506 Hz. Plot the magnitude response
of the filters. Use filter to filter the signal and listen to the
sound before and after filtering. Plot the signal spectral
magnitudes before and after filtering (use the fft function).
Describe the differences you hear between the signals for
before and after filtering and compare the before and after
filtering spectra.
b) Repeat part (a) comparing a 5th order elliptical filter with
passband ripple of .5 dB and stopband ripple of 30 dB and a
5th order Butterworth filter.
Homework(3.7)
Use Sinc function interpolation to upsample by a factor of
50 the band-limited signal is given in terms of its samples
below. Assume signal is bandlimited to 0.5 Hz. The
sampling rate (B) is 1 Hz.
F = [0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0]
Note:
a) Matlab has a sinc function (see help sinc)
b) A step edge is not bandlimited so the interpolated
bandlimited function will have overshoot and undershoot
between the given samples (i.e. it will not look like a
sharper step).