EC8562 DSP Viva Questions

ST.XAVIER’S CATHOLIC COLLEGE OF ENGINEERING
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
EC8562 DIGITAL SIGNAL PROCESSING LABORATORY
VIVA QUESTIONS WITH ANSWERS
1. Define Signal.
A signal is defined as any physical quantity that varies with time, space or any
other independent variable or variables
2. Define system.
A system is a physical device (i.e., hardware) or algorithm (i.e., software) that
performs an operation on the system.
3. What is analog signal?
The analog signal is a continuous function of an independent variable such as time,
space etc. the analog signal is defines for every instant of the independent variable and so
the magnitude of analog signal is continuous in the specified range. Here both the
magnitude ofthe signal and the independent variable are continuous.
4. What is discrete signal?
The discrete signal is a function of a discrete independent variable which is an
integer. The independent variable is divided into uniform intervals and each interval is
represented by an integer. The discrete signal is defined for every integer value of the
independent variable. The magnitude of discrete signal can take any discrete value in the
specified range. Here both the value of the signal and the independent variable are
discrete.
5. What is digital signal?
The digital signal is same as discrete signal except that the magnitude of the signal
is quantized. The magnitude of the signal can take one of the values in a set of
quantizedvalues. Here quantization is necessary to represent the signal in binary codes.
6. How will you classify the discrete time signals?
The discrete time signals are classified depending on their characteristics. Some
ways of classifying discrete time signals are (a) Energy signals and Power signals, (b)
Periodic and Aperiodic signals, (c) Symmetric and Anti symmetric signals.
7. What is DSP?

Digital Signal Processing refers to processing of signals by digital systems. DSP is
a technique that converts signals from real world sources (usually in analog form) into
digital data that can then be analyzed. Analysis is performed in digital form because once
a signal has been reduced to numbers; its components can be isolated, analyzed and
rearranged more easily than in analog form. Eventually, when the DSP has finished its
work, the digital datacan be turned back into an analog signal, with improved quality.
8. What are the advantages of DSP?
The advantages of DSP are (i) programs can be modified easily for better
performance; (ii) Better accuracy can be achieved by using adaptive algorithms, (iii)
digital signals can be easily stored and transported, (iv)digital systems are cheaper than
analog equivalent.
9. What are the applications of DSP?
Speech coding & decoding Audio mixing & editing, speech encryption &
decryption Image compression & decompression, Speech recognition, Image compression
and processing.
10.What is DT system?
A DT system is a device or algorithm that operates on a DT signal according to
some well-defined rule, to produce another DT signal. In general a DT system can be
thought as a set of operations performed on the input signal x[n] to produce the output
signal y[n].
11.How Discrete Time signal is obtained?
DT signal is obtained by sampling CT signal at regular intervals of time. In
practicalapplication sampling is implemented using S/H circuit.
12.What is impulse response and what is its significance?
The response or output of an LTI system for unit impulse input δ(n) is called
impulse response. It is denoted by h(n). The response y(n) of an LTI system for any

arbitrary inputx(n) is given by convolution of impulse response and input i.e., y(n) = x(n)*
h(n)
13.What is meant by discrete convolution?
The convolution of two discrete time signals is called discrete convolution. The
discrete convolution of two discrete time signals x1(n) and x2(n) is defined as
14.Why linear convolution is important in DSP?
The response or output of LTI discrete time system for any input x(n) is given
bylinear convolution of the input x(n) and the impulse response h(n) of the system. This
means that if the impulse response of a system is known, then the response of the system
for any input can be determined by convolution operation.
15.What is Circular Convolution?
The convolution of two periodic sequences with periodicity N is called circular
convolution.If x1(n) and x2(n) are two periodic sequences with N samples in a period, then
the circular convolution of x1(n) and x2(n) is defined as,
16.What is the difference between circular convolution and periodic convolution
In periodic convolution input signals are originally periodic with common value of
period. In circular convolution, if input signals are not periodic then they are assumed to
be periodic with period = N where N = max (L, M) where L is the length of first signal and
M is length of second signal.
17.Why circular convolution is important in DSP?
The Discrete Fourier Transform (DFT) is used for the analysis and design of
discrete time systems using digital computers. The DFT supports only circular

convolution. Hence when DFT techniques are employed, the results of linear convolution
are obtained only via circular convolution.
18.How to perform linear convolution using circular convolution?
If two signals x (n) and y (n) are of length n1 and n2, then the linear convoluted
output z (n) is of length n1+n2-1. Each of the input signals is padded with zeros to make
it of length n1+n2-1. Then circular convolution is done on zero padded sequences to get
the linearconvolution of original input sequences x (n) and y (n).
19.What is correlation?
Correlation gives a measure of similarity between two data sequences. In this process, two
signals are compared and the degree to which the two signals are similar is computed.
20. What are the applications of Correlation?
Typical applications of correlation include speech processing, image processing and radar
systems. In a radar system, the transmitted signal is correlated with the echo signal to
locate the position of the target. Similarly, in speech processing systems, different
waveforms are compared for voice recognition.
21.What are the applications of FFT?
(i) Linear filtering i.e. to find output of digital filter for any given input sequence (ii)
Spectral Analysis i.e. to find magnitude spectrum and phase spectrum (iii) Circular
Correlation i.e., to find degree of similarity between two signals.
22.What is auto-correlation?
It is a measure of similarity of similarity of a signal/waveform with itself.
23. Differentiate between DTFT and DFT. Why it is advantageous to use DFT in
computersrather than DTFT?
In DTFT, frequency appears to be continuous. But, in DFT, frequency is discrete.
This property is useful for computation in computers.
24.What is Periodic Convolution?
Periodic convolution is convolution of two periodic signals of the same period.

When two periodic signals are periodic with common period, periodic convolution is
similar to circular convolution.
25.What is the need of FFT?
It may be noted that the number of complex multiply and add operations required by
the simple forms both the DFT and IDFT is of order N
2
. This is because there are N
datapoints to calculate, each of which requires N complex arithmetic operations. In
computer science jargon, we say they have algorithmic complexity O(N
2
). This is not
good news. If we can't do any better than this then the DFT will not be very useful for the
majority of practical DSP applications. Fortunately, there are a number of different 'Fast
Fourier Transform' (FFT) algorithms that enable us to do very much better than this.
26.What is FFT? What is difference between DIT and DIF FFT?
A fast Fourier transform (FFT) is an efficient algorithm to compute the
discrete Fourier transform (DFT) and it’s inverse. The most significant difference between
simple DIF and DIT algorithms is that DIF starts with normal order input and generates bit
reversed order output. In contrast, DIT starts with bit reversed order input and
generatesnormal order output. So if both forward and inverse transforms are required and
bit reversed addressing isn't available, then the choice would seem clear. Use DIF for the
forward transform and DIT for the inverse transform. (For the inverse transform you will
need to conjugate the twiddle factors.) Unfortunately, the issue isn't quite so simple. If
your performing FFT's on pure real data it may be simpler to use a modified DIT for the
forward transform and a modified DIF for the inverse transform
27.FFT is faster than DFT. Justify.
FFT produces fast results because calculations are reduced by decomposition
technique. In FFT, N point DFT is decomposed into two N/2 point DFT’s, N/2 point DFT
is decomposed into N/$ point DFT’s and so on. Decomposition reduces calculations. FFT
algorithms are implemented using parallel processing techniques. Because calculations are
done in parallel, FFT produces fast computations.

28. Which algorithm is more powerful: DIT-FFT or DIF-FFT?
Computationally, both the algorithms are exactly same
29.What is the drawback in Fourier Transform and how it is overcome?
The draw back in Fourier Transform is that it is a continuous function of ω and so
it cannot be processed by digital system. This drawback is overcome by using Discrete
Fourier Transform. The DFT converts the continuous function of ω to a discrete function
of ω
30.Mention the importance of DFT
DFT is used for spectral analysis of signals using digital computer and to
perform filtering operations on signals using digital computer.
31. What is the application of Convolution?
Application of Convolution is to find output of Digital Filter for any given input signal.
Output of Digital filter y[n] is linear Convolution of input signal x[n] and impulse
response of the filter h[n].
32. What is convolution property of DFT?
Convolution in time domain corresponds to multiplication in frequency domain. If
DFT{x[n]} = X[k] and DFT {h[n]} = H[k] Then DFT{x[n] * h[n]} = X[k] H[k]
33.List the differences between linear convolution and circular convolution.
Linear convolution Circular convolution

1. The length of the input
sequencescan be different
2. Zero padding is not required
3. The input sequences need not be
periodic
4. The output sequence is non-periodic
5. The length of output sequence will
be greater than the length of input
sequence
1. The length of the input sequences
should be same
2. If the length of the sequences are
different, then zero padding is
required
3. Atleast one of the input sequence
should be periodic or should be
periodically extended
4. The output sequence is periodic.
The periodicity is same as that of
input sequences
5. The length of the input and output
sequences are same

34.What is Zero padding? Why it is needed?
Appending zeros to a sequence in order to increase the size or length of the
sequence is called zero padding. In circular convolution, when the two input sequences
are of different size, then they are converted to equal size by zero padding.
35.What are the two methods of sectioned convolution?
The two methods of sectioned convolution are overlap add method and overlap
savemethod.
36.Compare the overlap add and overlap save method of sectioned convolution.
Overlap add method Overlap save method
1. Linear convolution of each section
of longersequencewith
smallersequence is performed
2. Zero padding is not required
3. Overlapping of samples of input
sections are not required
4. The overlapped samples in the
outputof sectioned convolutions are
added to get the overall output.
1. Circular convolution of each
section of longer sequence with
smallersequence is performed.
2. Zero-padding is required to convert
the input sequences to the size of
output sequence.
3. The N2-1 sample of an input section
of longer sequence is overlapped
withnext input section.
4. Depending on the method of
overlapping the input samples, either
the last N2-1 samples or the first
N2-1samples of the output sequence
of each sectioned
convolution are discarded.
37.What is a digital filter?

Digital filters are a very important part of DSP. Filters have two uses: signal
separation and signal restoration. These problems can be attacked with either analog
or digital filters. Digital filters are used for two general purposes: separation of signals
that have been combined, and restoration of signals that have been distorted in some
way.
38.What is Finite Impulse Response?
When length of h[n] is finite it is called finite impulse response.
39. What is frequency response?
Frequency response means magnitude response and phase response.
40.What is Infinite Impulse Response (IIR) filter?
If the impulse response of the system is of infinite duration, the system is
said to be IIR filter system.
41.What are the advantages of IIR filters (compared to FIR filters)?
IIR filters can achieve a given filtering characteristic using less memory and
calculations than a similar FIR filter.
42.What are the disadvantages of IIR filters (compared to FIR filters)?
They are more susceptible to problems of finite-length arithmetic, such as noise
generated by calculations, and limit cycles. They are harder to implement using fixed-
point arithmetic. They don't offer the computational advantages of FIR filters for
multirate (decimation and interpolation) application
43.What is a linear phase filter?
"Linear Phase" refers to the condition where the phase response of the filter is a
linear (straight- line) function of frequency.
44.What is the advantage of Linear Phase?
This results in the delay through the filter being the same at all
frequencies. Therefore, the filter does not cause "phase distortion" or "delay
distortion".

45.List the different types of structures for realization of IIR filters
The different types of structures for realizing IIR systems are i) Direct form – I,
ii)Direct form- II iii) Cascade form, iv) Parallel form
46. What are the factors that influence the choice of structure for realization of an
LTI system?
The factors that influence the choice of realization structure are computational
complexity, memory requirements, finite word length effects, parallel processing and
pipelining of computations
47.List the different types of structures for realizing FIR systems?
The different types of structures for realizing FIR systems are i) Direct form
realization, ii) Cascade realization, iii)Linear phase realization.
48.What is Recursive system?
If the output y(n) at time of a system depends on past output values then the
systemis called Recursive system. The recursive systems are functions of past outputs,
present and past inputs.
49.What is Non-recursive system?
If the output y(n) at time of a system does not depends on past output values
then the system is called Recursive system. The recursive systems are functions of
present and past inputs.
50.What is an FFT "radix"?
The "radix" is the size of FFT decomposition. In the example above, the radix was
2. For single-radix FFT's, the transform size must be a power of the radix. In the
exampleabove, the size was 32, which is 2 to the 5th power.
51. What is an "in place" FFT?
An "in place" FFT is simply an FFT that is calculated entirely inside its
original sample memory. In other words, calculating an "in place" FFT does not require
additional buffer memory (as some FFT's do.)
52. What is "bit reversal"?
"Bit reversal" is just what it sounds like: reversing the bits in a binary word
from leftto write. Therefore the MSB's become LSB's and the LSB's become MSB's.

53.What are Advantages of FIR Filters-?The advantages of FIR filters are
• They can easily be designed to be "linear phase"
• They are suited to multi-rate applications.
• They have desirable numeric properties.
• They can be implemented using fractional arithmetic.
• They are simple to implement.
54.What are the disadvantages of FIR Filters (compared to IIR filters)?
Compared to IIR filters, FIR filters sometimes have the disadvantage that they
require more memory and/or calculation to achieve a given filter response
characteristic.
55.Explain the concept of Linear Phase and its importance.
If the Phase Response is Linear the output of the Filter during pass-band is
delayed input. II. If the phase Response is non Linear the output of the filter during
pass-band is distorted one. The linear Phase characteristic is important when the phase
distortion is not tolerable. FIR Filter can be designed with linear phase characteristic.
In application like data transmission, speech processing etc. phase distortion cannot be
tolerated and here linear phase characteristic of FIR filter is useful.
56. What is the role of window in the design of FIR filter? Name the few types of
windows.
FIR filter is designed by truncating infinite samples of hd[n] by using window
function. Examples of window function include, Hamming window, Bartlet Window,
Hanning window, Blackman window etc.
57.What are the desirable characteristics of window Function?
The Fourier Transform of the window function W(ejw) should
have a small width of the linear Phase characteristic is important when the phase distortion
is not tolerable. FIR Filter can be designed with linear phase characteristic. In application
like data transmission, speech processing etc. phase distortion cannot be tolerated and here

linearphase characteristic of FIR filter is useful

EC8562 DSP Viva Questions

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