Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using
Scilab
Iman Mukherjee
Department of Electrical Engineering, IIT Bombay
December 1, 2010 Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Outline
1
Basic signal processing tools Discrete Fourier Transform
Fast Fourier Transform
Convolution
Plotting
Group Delay
Aliasing
2
Filter Design NonRecipe Based
Recipe Based
An Example Application
Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Basic signal processing toolsDiscrete Fourier Transform DFT
X
(! ) = 1
X
n = 1 x
[n ]e j !n The Scilab command
99K[xf ] = dft(x,
ag); x is the time domain representation
xf is the frequency domain representation
ag = 1 or 1
Notice Cosine is Even Symmetric, hence this 64point DFT
is real with peaks at 4 and 60 (644) Faster way t Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Basic signal processing toolsDiscrete Fourier Transform DFT
X
(! ) = 1
X
n = 1 x
[n ]e j !n The Scilab command
99K[xf ] = dft(x,
ag); x is the time domain representation
xf is the frequency domain representation
ag = 1 or 1
Notice Cosine is Even Symmetric, hence this 64point DFT
is real with peaks at 4 and 60 (644) Faster way t Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Basic signal processing toolsDiscrete Fourier Transform DFT
X
(! ) = 1
X
n = 1 x
[n ]e j !n The Scilab command
99K[xf ] = dft(x,
ag); x is the time domain representation
xf is the frequency domain representation
ag = 1 or 1
Notice Cosine is Even Symmetric, hence this 64point DFT
is real with peaks at 4 and 60 (644) Faster way t Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Basic signal processing toolsDiscrete Fourier Transform DFT
X
(! ) = 1
X
n = 1 x
[n ]e j !n The Scilab command
99K[xf ] = dft(x,
ag); x is the time domain representation
xf is the frequency domain representation
ag = 1 or 1
Notice Cosine is Even Symmetric, hence this 64point DFT
is real with peaks at 4 and 60 (644) Faster way t Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Basic signal processing toolsDiscrete Fourier Transform DFT
X
(! ) = 1
X
n = 1 x
[n ]e j !n The Scilab command
99K[xf ] = dft(x,
ag); x is the time domain representation
xf is the frequency domain representation
ag = 1 or 1
Notice Cosine is Even Symmetric, hence this 64point DFT
is real with peaks at 4 and 60 (644) Faster way t Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Basic signal processing toolsDiscrete Fourier Transform DFT
X
(! ) = 1
X
n = 1 x
[n ]e j !n The Scilab command
99K[xf ] = dft(x,
ag); x is the time domain representation
xf is the frequency domain representation
ag = 1 or 1
Notice Cosine is Even Symmetric, hence this 64point DFT
is real with peaks at 4 and 60 (644) Faster way t Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Basic signal processing toolsDiscrete Fourier Transform DFT
X
(! ) = 1
X
n = 1 x
[n ]e j !n The Scilab command
99K[xf ] = dft(x,
ag); x is the time domain representation
xf is the frequency domain representation
ag = 1 or 1
Notice Cosine is Even Symmetric, hence this 64point DFT
is real with peaks at 4 and 60 (644) Faster way t Iman Mukherjee Digital Signal Processing and Filter Design using Scilab
Digital Signal Processing and Filter Design using Scilab
Basic signal processing toolsFast Fourier Transform FFT
x=t(a ,1) or x=t(a)
y=t2(x,n,m) twodimension
x=t(a,1,dim,incr) multidimensional