NK_band_pass.py

# -*- 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)