Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2
nd
Edition, 2013, Elsevier.

The Digital Signal Process (2)
12
There are many real-world DSP applications that
do not require DAC.
Data acquisition and digital information display, speech
recognition, data encoding, and so on.
Similarly,
DSP
applications
that
need
no
ADC
include
CD players, text-to-speech synthesis, and digital tone
generators, among others.
Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2
nd
Edition, 2013, Elsevier.

Basic DSP Examples In Block
13
Diagrams

Digital Filtering
14
Let us consider the situation shown in Figure 3.
Figure 3:
The simple digital filtering block.
A digitized noisy signal obtained from digitizing analog voltages
(sensor output) containing a useful low-frequency signal and noise
that occupies all of the frequency range.
After ADC, the digitized noisy signal
x(n)
where
n
is the sample
number, can be enhanced using digital filtering.
DSP block operates as a simple digital lowpass filter.
Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2
nd
Edition, 2013, Elsevier.

Digital Filtering (2)
15
After processing the digitized noisy signal
x(n)
the digital lowpass
filter produces a clean digital signal
y(n)
.
The cleaned signal
y(n)
can be applied to another DSP algorithm
for a different application or convert it to the analog signal via DAC
and the reconstruction filter.
Typical applications of noise filtering include acquisition of clean
digital audio and biomedical signals and enhancement of speech
recording, among others.
The digitized noisy signal and clean digital signal, respectively, are
plotted in Figure 4.
Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2
nd
Edition, 2013, Elsevier.

Digital Filtering (3)
16
Figure 4:
(Top) Digitized noisy signal. (Bottom) Clean digital signal using the
digital lowpass filter.
Clean signal
Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2
nd
Edition, 2013, Elsevier.

Signal Frequency (Spectrum) Analysis
17
As shown in Figure 5, certain DSP applications often require that
time domain information and the frequency content of the signal be
analyzed.
Figure 5:
Signal spectral analysis.
Figure
6
(next
slide)
shows
a
digitized
audio
signal
and
its
calculated signal spectrum (frequency content), that is, the signal
amplitude versus its corresponding frequency, obtained from a DSP
algorithm, called the fast Fourier transform (FFT).
Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2
nd
Edition, 2013, Elsevier.

Signal Frequency (Spectrum) Analysis
(2)
18
Figure 6:
Audio signals and their spectrums.
Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2
nd
Edition, 2013, Elsevier.

Signal Frequency (Spectrum) Analysis
(3)
19
The plot in Figure 6(a) is a time domain display of the recorded
audio signal with a frequency of 1,000 Hz sampled at 16,000
samples per second, while the frequency content display of plot (b)
displays the calculated signal spectrum versus frequency, in which
the peak amplitude is clearly located at 1,000 Hz.

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