Create NK_band_pass.py

Code split up for NK for final submission

NK_band_pass.py

@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 31 11:47:16 2019
+
+@author: Narayanan Krishna
+"""
+from __future__ import print_function, division, unicode_literals
+import wave
+import numpy as np
+from pylab import*
+import matplotlib.pyplot as plt
+from scipy.io import wavfile
+import pyaudio
+
+def fft_dis(fname):
+    sampFreq, snd = wavfile.read(fname)
+
+    snd = snd / (2.**15) #convert sound array to float pt. values
+
+    s1 = snd[:,0] #left channel
+
+    s2 = snd[:,1] #right channel
+
+    n = len(s1)
+    p = fft(s1) # take the fourier transform of left channel
+
+    m = len(s2) 
+    p2 = fft(s2) # take the fourier transform of right channel
+
+    nUniquePts = int(ceil((n+1)/2.0))
+    p = p[0:nUniquePts]
+    p = abs(p)
+
+    mUniquePts = int(ceil((m+1)/2.0))
+    p2 = p2[0:mUniquePts]
+    p2 = abs(p2)
+    p = p / float(n) # scale by the number of points so that
+             # the magnitude does not depend on the length 
+             # of the signal or on its sampling frequency  
+    p = p**2  # square it to get the power 
+# multiply by two (see technical document for details)
+# odd nfft excludes Nyquist point
+    if n % 2 > 0: # we've got odd number of points fft
+        p[1:len(p)] = p[1:len(p)] * 2
+    else:
+        p[1:len(p) -1] = p[1:len(p) - 1] * 2 # we've got even number of points fft
+
+    freqArray = arange(0, nUniquePts, 1.0) * (sampFreq / n);
+    plt.plot(freqArray/1000, 10*log10(p), color='k')
+    plt.xlabel('Channel_Frequency (kHz)')
+    plt.ylabel('Channel_Power (dB)')
+    plt.show()
+
+
+
+
+
+fn = 'C:\\Users\\Narayanan Krishna\\Music\\A.R. Rahman - Roobaroo - Rang de Basanti.wav'
+
+wfin = wave.open('C:\\Users\\Narayanan Krishna\\Music\\A.R. Rahman - Roobaroo - Rang de Basanti.wav', 'r')
+par = list(wfin.getparams()) # Get the parameters from the input.
+# This file is stereo, 2 bytes/sample, 44.1 kHz.
+par[3] = 0 # The number of samples will be set by writeframes.
+
+# Open the output file
+wfout = wave.open('filtered-talk.wav', 'w')
+wfout.setparams(tuple(par)) # Use the same parameters as the input file.
+
+lowpass = 21 # Remove lower frequencies.
+highpass = 9000 # Remove higher frequencies.
+
+sz = wfin.getframerate() # Read and process 1 second at a time.
+c = int(wfin.getnframes()/sz) # whole file
+for num in range(c):
+    print('Processing {}/{} s'.format(num+1, c))
+    da = np.fromstring(wfin.readframes(sz), dtype=np.int16)
+    left, right = da[0::2], da[1::2] # left and right channel
+    lf, rf = np.fft.rfft(left), np.fft.rfft(right)
+    lf[:lowpass], rf[:lowpass] = 0, 0 # low pass filter
+    lf[55:66], rf[55:66] = 0, 0 # line noise
+    lf[highpass:], rf[highpass:] = 0,0 # high pass filter
+    nl, nr = np.fft.irfft(lf), np.fft.irfft(rf)
+    ns = np.column_stack((nl,nr)).ravel().astype(np.int16)
+    wfout.writeframes(ns.tostring())
+# Close the files.
+wfin.close()
+wfout.close()
+
+n = 0
+fo = 'filtered-talk.wav'
+for n in range (0,2): 
+    if n==0:
+        fft_dis(fn)
+    elif n==1:
+        fft_dis(fo)