Digital Signal Procesesing

1. Prepared by
Ms.J.Shiny Christobel
Assistant Professor
Department of ECE
Sri Ramakrishna Institute of Technology, Coimbatore
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2.  Introduction to DSP
 Block Diagram
 Filters
 Applications
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3. SIGNAL
 A physical quantity varies
with time, space or any
other independent variable
 1-D (Eg: Speech , ECG or
EEG signal)
 2-D (Eg: Image)
 3-D (Eg:Videos)
 Multi dimensional
signal(Eg:Google map)
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4. Analog Signal
 Signal that are not digitalized
 Denoted by x(t)
 Analog signal = continuous-time + continuous
amplitude
 Eg: Audio, Inputs through microphone
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5. Discrete Signal
 Signal that are digitalized
 Denoted by x(n)
 Discrete signal = Discrete-time + Discrete
amplitude
 Digital signal -quantization of the discrete signal
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6.  Energy signal (E=finite and P=infinity)
 Power signal (E=0 and P=finite)
 Causal signal (depends on past and present values)
 Non-causal signal (depends on future values)
 Deterministic signal (Eg: CT or DT signal)
 Random signal (Eg: Noise)
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7.  Analysis , interpretation and manipulation of
signals
 Gets an input signal, process it and produces an
output signal.
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8.  When a signal is transmitted from one point to
another there is every possibility of contamination
/deformation of the signal by external noise. So to
retrieve the original signal at the receiver suitable
filters are to be used.
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10.  A system is any process that produces an output
signal in response to an input signal
 Continuous systems-input and output are
continuous signals, such as in analog electronics
 Discrete systems-input and output are
discrete signals Eg: computer programs that
manipulate the values stored in arrays.
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11.  Static or Dynamic system
 Linear and Non-linear system (superposition principle)
 Time Varient and time In-varient system
 Causal and non-causal systems
 Stable and unstable system
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14. USES OF FILTERS (Eg:Poor audio, Blurred Image)
 Signal restoration
 Signal separation
TYPES OF FILTERS
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15.  Analog filters take the analog signal as input
and process the signal and finally gives the
analog output.
 An analog filter is constructed using resistors,
capacitors, active components etc…
 An analog filter is denoted by a differential
equation. (Laplace tranform)
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 A digital filter processes and generates digital
data. (Z-Transform)
 A digital filter constitutes elements like adder,
multiplier and delay units.
 Digital filters are vastly superior in the level of
performance in comparison to analog filters.

17.  A digital filter is denoted by a difference equation
 The performance is not influenced by component
ageing, temperature and power variations.
 It is highly immune to noise and relatively stable.
 Transportation and reconfiguration is very easy which
is not true in the case of analog filters.
 Multiple filtering is possible only in digital filters.
 Computational problems are minimum.
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18.  Quantization error occurs due to finite word length in
the representation of signals and parameters.
 Digital filters also suffer from Bandwidth problems.
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19.  FIR Filters (Finite impulse response filters)
 IIR Filters (Infinite Impulse response filters)
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20.  The digital filter whose impulse response is of finite
duration is known as Finite impulse response filter
 The response depends only on the present and past
input samples
 Also called non recursive filters
 It has a finite number of non-zero terms
 FIR filters can be designed with exact linear phase.
 FIR filters are stable
 Round off noise can be eliminated in FIR filters
 DEMERT :The delay of linear phase FIR filters can
create problems in some DSP applications. 20

21.  The digital filter whose impulse response is of
infinite duration is known as Infinite impulse
response filter.
 The response of an IIR filter is a function of
current and past input signal samples and past
output signal samples.
 It is also called recursive filter.
 It has lesser number of side lobes in the stop-
band.
 The implementation of an IIR filter involves
fewer parameters, less memory requirements and
lower computational complexity
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22.  IIR filters do not have linear phase
 not very stable
 Realization of IIR filters is not very easy as
compared to FIR filters
 As it is a recursive filter the number of
coefficients is very large and the memory
requirements are also high
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24.  In all musical recordings, the sound from instruments is
recorded in studio and then special audio effects are
added by manipulating the recorded musical sounds.
The audio effects are artificially generated using
various DSP techniques.
 the sound recorded in a studio is different and it doesn’t
sound natural.
 So, echoes are simply generated by delay units.
 The direct sound and a single echo appear in K
sampling period latter can be generated by the FIR
filter with the system function
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26.  To generate multiple echoes separated K
sampling periods we can use an FIR filter with
transfer function
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27.  The other special sound effects are flanging and
chorus. The flanging effect is created by feeding the
same musical note to two tape recorders and then
combining their delayed outputs.
 This effect can be simulated using the FIR filter by
periodically varying the delay K(n) between 0 and K.
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28.  The chorus effect is achieved when several
musicians are playing the same musical note at the
same time with small changes in the amplitudes and
small timing differences between their sounds.
 A chorus generator can be realized by parallelly
connecting few number of flanging effect filters
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29.  IMAGE PROCESSING
Cancer Detection
Fog Removal
 SPEECH PROCESSING
Speech to Text conversion
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