Design FIR filter to reject unwanted frequencies

Let’s see how to design a simple digital FIR filter to reject unwanted frequencies in an incoming signal. As a per-requisite, I urge you to read through this post: Introduction to digital filter design

Background on transfer function

The transfer function of a system provides the underlying support for ascertaining vital system response characteristics without solving the difference equations of the system. As mentioned earlier, the transfer function of a generic filter in Z-domain is given by ratio of polynomials in z

The values of z when H(z) =0 are called zeros of H(z). The values of z when H(z) = ∞ are called poles of H(z).

It is often easy to solve for zeros {z_i} and poles {p_j}, when the polynomials in the numerator and denominator are expressed as resolvable factors.

The zeros {z_i} are obtained by finding the roots of the equation

The poles {p_j} are obtained by finding the roots of the equation

Pole-zero plots are suited for visualizing the relationship between the Z-domain and the frequency response characteristics. As mentioned before, the frequency response of the system H(e ^jω) can be computed by evaluating the transfer function H(z) at specific values of z = e ^jω. Because the frequency response is periodic with period 2π, it is sufficient to evaluate the frequency response for the range -π <= ω < π (that is one loop around the unit circle on the z-plane starting from z=-1 and ending at the same point).

FIR filter design

FIR filters contain only zeros and no poles in the pole-zero plot (in fact all the poles sit at the origin for a causal FIR). For an FIR filter, the location of zeros of H(z) on the unit-circle nullifies specific frequencies. So, to design an FIR filter to nullify specific frequency ω, we just have to place the zeros at corresponding locations on the unit circle (z=e^jω) where the gain of the filter needs to be 0. Let’s illustrate this using an example.

For this illustration, we would use this audio file as an input to the filtering system. As a first step in the filter design process, we should understand the nature of the input signal. Discrete-time Fourier transform (DTFT) is a tool for analyzing the frequency domain characteristics of the given signal.

The following function is used to compute the DTFT of the sequence read from the audio file.

import numpy as np
import matplotlib.pyplot as plt

from math import ceil,log,pi,cos
from scipy.fftpack import fft,fftfreq,fftshift

def compute_DTFT(x,M=0):
    """
    Compute DTFT of the given sequence x
    M is the desired length for computing DTFT (optional).
    Returns the DTFT X[k] and corresponding frequencies w (omega) arranged as -pi to pi
    """
    N = max(M,len(x))
    N = 2**(ceil(log(N)/log(2)))
    
    X = fftshift(fft(x,N))
    w = 2*pi*fftshift(fftfreq(N))    
    return (X,w)

Let’s read the audio file, load the samples as a signal sequence , and plot the sequence in time-domain/frequency domain (using DTFT).

from scipy.io.wavfile import read

samplerate, x = read('speechWithNoise.wav')
duration = len(x)/samplerate
time = np.arange(0,duration,1/samplerate)

fig1, (ax1,ax2) = plt.subplots(nrows=2,ncols=1)
ax1.plot(time,x)
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Amplitude')
ax1.set_title('speechWithNoise.wav - x[n]')

(X,w)= compute_DTFT(x)
ax2.plot(w,abs(X))
ax2.set_xlabel('Normalized frequency (radians/sample)')
ax2.set_ylabel('|X[k]|')
ax2.set_title('Magnitude vs. Frequency')

The magnitude vs. frequency plot simply shows huge spikes at θ=±1.32344 radians. The location of the spikes are captured by using the numpy.argmax↗ function.

maxIndex = np.argmax(X)
theta = w[maxIndex]
print(theta)

Since a sinusoid can be mathematically represented as

The two spikes at θ=±1.32344 radians in the frequency domain, will manifest as a sinusoidal signal in the time domain.

Zooming in the area between θ= ±0.4 radians, the frequency domain plot reveals a hidden signal.

Now, our goal is to design an FIR filter that should reject the sinusoid at θ=±1.32344 radians, so that only the hidden signal gets filtered in.

Since the sinusoid that we want to reject is occurring at some θ radians in the frequency domain, for the FIR filter design, we place two zeros at

Therefore, the transfer function of the filter system is given by

This is a second order FIR filter. The coefficients of the filter are

For the given problem, to should reject the sinusoid at θ=±1.32344 radians, we set θ=1.32344 in the filter coefficients above.

Filter in action

Filter the input audio signal through the designed filter and plot the filtered output in time-domain and frequency domain. The lfilter function from scipy.signal package↗ is utilized for the filtering operation.

from scipy.signal import lfilter
y_signal = lfilter(b, a, x)
fig3, (ax3,ax4) = plt.subplots(nrows=1,ncols=2)
ax3.plot(time,y_signal,'g')
ax3.set(title='Noise removed speech - y[n]',xlabel='Time (s)',ylabel='Amplitude')

(Y,w)= compute_DTFT(y_signal)
ax4.plot(w,abs(Y)/max(abs(Y)),'r')
ax4.set(title='Frequency content of Y',xlabel='Normalized frequency (radians/sample)',ylabel='Magnitude - |Y[k]|')

The filter has effectively removed the sinusoidal noise, as evident from both time-domain and frequency domain plots.

Save the filtered output signal as .wav file for audio playback

from scipy.io.wavfile import write
output_data = np.asarray(y_signal, dtype=np.int16)#convert y to int16 format
write("noise_removed_output.wav", samplerate, output_data)

Characteristics of the designed filter

Let’s compute the double sided frequency response of the designed FIR filter. The frequency response of the designed FIR digital filter is computed using freqz function from the scipy.signal package↗.

from scipy.signal import freqz
b = [1,-2*cos(theta),1] #filter coefficients
a = [1]
w, h = freqz(b,a,whole=True)#frequency response h[e^(jw)]
#whole = True returns output for whole range 0 to 2*pi
#To plot double sided response, use fftshift
w = w - 2*np.pi*(w>=np.pi) #convert to range -pi to pi
w = fftshift(w)
h = fftshift(h)

Plot the magnitude response, phase response, pole-zero plot and the impulse response of the designed filter.

#Plot Magnitude-frequency response
fig2, (ax) = plt.subplots(nrows=2,ncols=2)
ax[0,0].plot(w,abs(h),'b')
ax[0,0].set(title='Magnitude response',xlabel='Frequency [radians/sample]',ylabel='Magnitude [dB]')
ax[0,0].grid();ax[0,0].axis('tight');

#Plot phase response
angles = np.unwrap(np.angle(h))
ax[0,1].plot(w,angles,'r')
ax[0,1].set(title='Phase response',xlabel='Frequency [radians/sample]',ylabel='Angles [radians]')
ax[0,1].grid();ax[0,1].axis('tight');

#Transfer function to pole-zero representation
from scipy.signal import tf2zpk
z, p, k = tf2zpk(b, a)

#Plot pole-zeros on a z-plane
from  matplotlib import patches
patch = patches.Circle((0,0), radius=1, fill=False,
                    color='black', ls='dashed')
ax[1,0].add_patch(patch)
ax[1,0].plot(np.real(z), np.imag(z), 'xb',label='Zeros')
ax[1,0].plot(np.real(p), np.imag(p), 'or',label='Poles')
ax[1,0].legend(loc=2)
ax[1,0].set(title='Pole-Zero Plot',ylabel='Real',xlabel='Imaginary')
ax[1,0].grid()

#Impulse response
#create an impulse signal
imp = np.zeros(20)
imp[0] = 1

from scipy.signal import lfilter
y_imp = lfilter(b, a, imp) #drive the impulse through the filter
ax[1,1].stem(y_imp,linefmt='-.')
ax[1,1].set(title='Impulse response',xlabel='Sample index [n]',ylabel='Amplitude')
ax[1,1].axis('tight')

Questions

Use the comment form below to answer the following questions

1) Is the filter that we designed a lowpass, highpass, bandpass or a bandstop filter ?
2) To reject a single sinusoidal signal, why two zeros are needed in the above filter design ?
3) What do you understand from the phase response plotted above ?

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Similar topics

Essentials of Signal Processing ● Generating standard test signals  □ Sinusoidal signals  □ Square wave  □ Rectangular pulse  □ Gaussian pulse  □ Chirp signal ● Interpreting FFT results - complex DFT, frequency bins and FFTShift  □ Real and complex DFT  □ Fast Fourier Transform (FFT)  □ Interpreting the FFT results  □ FFTShift  □ IFFTShift ● Obtaining magnitude and phase information from FFT  □ Discrete-time domain representation  □ Representing the signal in frequency domain using FFT  □ Reconstructing the time domain signal from the frequency domain samples ● Power spectral density ● Power and energy of a signal  □ Energy of a signal  □ Power of a signal  □ Classification of signals  □ Computation of power of a signal - simulation and verification ● Polynomials, convolution and Toeplitz matrices  □ Polynomial functions  □ Representing single variable polynomial functions  □ Multiplication of polynomials and linear convolution  □ Toeplitz matrix and convolution ● Methods to compute convolution  □ Method 1: Brute-force method  □ Method 2: Using Toeplitz matrix  □ Method 3: Using FFT to compute convolution  □ Miscellaneous methods ● Analytic signal and its applications  □ Analytic signal and Fourier transform  □ Extracting instantaneous amplitude, phase, frequency  □ Phase demodulation using Hilbert transform ● Choosing a filter : FIR or IIR : understanding the design perspective  □ Design specification  □ General considerations in design

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