Python – Plotting fft from a wav file using python

audiofftpythonsignal processingsignals

I am trying to plot the frequency spectrum of a wav file, but it seems like frequency spectrum always matches the time domain signal, with the following code.

import matplotlib.pyplot as plt
import numpy as np


def plot(data):
    plt.plot(data, color='steelblue')
    plt.figure()
    plt.show()

rate, wav_data = wavfile.read("audio_self/on/on.wav")
plot(wav_data)
plot(np.abs(np.fft.fft(wav_data)))

Am I doing something wrong?

Best Answer

If you want two separate a stereo track to left and right channels and then take a separate graph of each, it would be a lot more accurate of a reading unless you put the track in mono like Frank Zalkow says. This is how to separate the stereo track into left and right channels: 

"""
Plot
"""
#Plots a stereo .wav file
#Decibels on the y-axis
#Frequency Hz on the x-axis

import matplotlib.pyplot as plt
import numpy as np

from pylab import*
from scipy.io import wavfile


def plot(file_name):

    sampFreq, snd = wavfile.read(file_name)

    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 = ceil((n+1)/2.0)
    p = p[0:nUniquePts]
    p = abs(p)

    mUniquePts = ceil((m+1)/2.0)
    p2 = p2[0:mUniquePts]
    p2 = abs(p2)

'''
Left Channel
'''
   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('LeftChannel_Frequency (kHz)')
     plt.ylabel('LeftChannel_Power (dB)')
     plt.show()

'''
Right Channel
'''
    p2 = p2 / float(m) # scale by the number of points so that
             # the magnitude does not depend on the length 
             # of the signal or on its sampling frequency  
    p2 = p2**2  # square it to get the power 




# multiply by two (see technical document for details)
# odd nfft excludes Nyquist point
    if m % 2 > 0: # we've got odd number of points fft
         p2[1:len(p2)] = p2[1:len(p2)] * 2
    else:
         p2[1:len(p2) -1] = p2[1:len(p2) - 1] * 2 # we've got even number of points fft

    freqArray2 = arange(0, mUniquePts, 1.0) * (sampFreq / m);
    plt.plot(freqArray2/1000, 10*log10(p2), color='k')
    plt.xlabel('RightChannel_Frequency (kHz)')
    plt.ylabel('RightChannel_Power (dB)')
    plt.show()

I hope this helps.

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