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How to extract Audio Wave from a mixture of Signal using Scipy – Python?

  • Last Updated : 24 Feb, 2021

Prerequisites: Scipy

Spectral Analysis refers to analyzing of the frequency spectrum/response of the waves. This article as the title suggests deals with extracting audio wave from a mixture of signals and what exactly goes into the process can be explained as:

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Consider we have 3 mixed Audio Signals having frequency of 50Hz,1023Hz & 1735Hz respectively. Apart from these signals we will be also implementing noise to the signal beforehand. The spectral analysis will be done via using a filter so that we can separate out the signals. On requirement, we can tweak our signals according to the frequency of the signal we want to extract.



Approach

  • Import modules
  • Specify conditions such as number of samples, sampling frequency, inner sample time & creating our mixed audio wave
  • Add noise to the audio signal
  • Estimate of Filter Window & Computing Cutoff Frequency
  • Create a filter to filter out noise
  • Plot the Noisy Signal, Frequency Response of Filter, Extracted Audio Wave, Frequency Spectrum of Mixed Audio Signal, Frequency Spectrum of our extracted Audio Signal
  • Display plot

Program:

Python3




# Original, high sample rate signal
# Let us imagine this is like our analog signal
from scipy import signal
from scipy.fft import fft
import numpy as np
import matplotlib.pyplot as plt
  
# Number of samples
N_sample = 512
  
# Sampling frequency
fs = 10000
  
# inter sample time = 0.001s = 1kHz sampling
dt = 1/fs
  
# time vector
t = np.arange(0, N_sample)*dt
  
# Create signal vector that is the sum of 50 Hz, 1023 Hz, and 1735 Hz
Signal = np.sin(2*np.pi*50*t) + np.sin(2*np.pi*1023*t)+np.sin(2*np.pi*1735*t)
  
# Add random noise to the signal
Signal = Signal+np.random.normal(0, .1, Signal.shape)
  
# Part A: Estimation of Length and Window
# Select design  Specification
# Lower stopband frequency in Hz
fstop_L = 500
  
# Lower passband frequency in HZ
fpass_L = 800
  
# Upper stopband frequency in Hz
fstop_U = 1500
  
# Upper passband frequency in HZ
fpass_U = 1200
  
# Calculations
# Normalized lower transition band w.r.t. fs
del_f1 = abs(fpass_L-fstop_L)/fs
  
# Normalized upper transition band w.r.t. fs
del_f2 = abs(fpass_U-fstop_U)/fs
  
# Filter length using selected window based
# on Normalized lower transition band
N1 = 3.3/del_f1
  
# Filter length using selected window based
# on Normalized upper transition band
N2 = 3.3/del_f2
print('Filter length based on lower transition band:', N1)
print('Filter length based on upper transition band:', N2)
  
# Select length as the maximum of the N1 and N2
# and if it is even, make it next higher integer
N = int(np.ceil(max(N1, N2)))
if(N % 2 == 0):
    N = N+1
print('Selected filter length :', N)
  
# Calculate lower and uper cut-off frequencies
# Lower cut-off frequency in Hz
fL = (fstop_L+fpass_L)/2
  
# Upper cut-off frequency in Hz
fU = (fstop_U+fpass_U)/2
  
# Normalized Lower cut-off frequency in (w/pi) rad
wL = 2*fL/fs
  
# Normalized upper cut-off frequency in (w/pi) rad
wU = 2*fU/fs
  
# Cutoff frequency array
cutoff = [wL, wU]
  
# Since the given specification of Stopband attenuation = 50 dB
# and Passband ripple = 0.05 dB, atleast satisfy with
# Hamming window, we have to choose it.
  
# Determine Filter coefficients
# Call filter design function using Hamming window
b_ham = signal.firwin(N, cutoff, window="hamming", pass_zero="bandpass")
  
# Determine Frequency response of the filters
# Calculate response h at specified frequency
# points w for Hamming window
w, h_ham = signal.freqz(b_ham, a=1)
  
# Calculate Magnitude in dB
# Calculate magnitude in decibels
h_dB_ham = 20*np.log10(abs(h_ham))
  
a = [1]
  
# Filter the noisy signal by designed filter
# using signal.filtfilt
filtOut = signal.filtfilt(b_ham, a, Signal)
  
  
# Plot filter magnitude and phase responses using
# subplot. Digital frequency w converted in analog
# frequency
fig = plt.figure(figsize=(12, 18))
  
# Original signal
sub1 = plt.subplot(5, 1, 1)
sub1.plot(t[0:200], Signal[0:200])
sub1.set_ylabel('Amplitude')
sub1.set_xlabel('Time')
sub1.set_title('Noisy signal', fontsize=20)
  
# Magnitude response Plot
sub2 = plt.subplot(5, 1, 2)
sub2.plot(w*fs/(2*np.pi), h_dB_ham, 'r', label='Bandpass filter',
          linewidth='2'# Plot for magnitude response window
  
sub2.set_ylabel('Magnitude (db)')
sub2.set_xlabel('Frequency in Hz')
sub2.set_title('Frequency response of Bandpass Filter', fontsize=20)
sub2.axis = ([0,  fs/2-1105])
sub2.grid()
  
sub3 = plt.subplot(5, 1, 3)
sub3.plot(t[0:200], filtOut[0:200], 'g', label='Filtered signal',
          linewidth='2'# Plot for magnitude response window
sub3.set_ylabel('Magnitude ')
sub3.set_xlabel('Time')
sub3.set_title('Filtered output of Band pass Filter', fontsize=20)
  
  
# Show spectrum of noisy input signal
Sigf = fft(Signal)  # Compute FFT of noisy signal
sub4 = plt.subplot(5, 1, 4)
xf = np.linspace(0.0, 1.0/(2.0*dt), (N_sample-1)//2)
sub4.plot(xf, 2.0/N_sample * np.abs(Sigf[0:(N_sample-1)//2]))
sub4.set_ylabel('Magnitude')
sub4.set_xlabel('Frequency in Hz')
sub4.set_title('Frequency Spectrum of Original Signal', fontsize=20)
sub4.grid()
  
# Show spectrum of filtered output signal
Outf = fft(filtOut)  # Compute FFT of filtered signal
sub5 = plt.subplot(5, 1, 5)
xf = np.linspace(0.0, 1.0/(2.0*dt), (N_sample-1)//2)
sub5.plot(xf, 2.0/N_sample * np.abs(Outf[0:(N_sample-1)//2]))
sub5.set_ylabel('Magnitude')
sub5.set_xlabel('Frequency in Hz')
sub5.set_title('Frequency Spectrum of Filtered Signal', fontsize=20)
sub5.grid()
  
fig.tight_layout()
plt.show()

Output:




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