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pythonsignal-processingfftwavwindowing

Perform FFT for every second on wav file with Python

I have code which performs FFT on a 5 second wav file. I'm not good at Python so I wrote very basic code which splits the wav file and calculates FFT for every second. Is there any more convenient way to do this?

I'm also not sure if they show every frequency and its related amplitude because of the range part. I'm splitting a signal into 5 pieces but I might also split frequencies into 5 pieces also.

The variable names which end with numbers are my additions, normally I had only one for each of them to calculate whole FFT on wav. Any suggestion will be great. (I erased first second because of the bias, you should check the plot)

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import print_function
import scipy.io.wavfile as wavfile
import scipy
import scipy.fftpack
import numpy as np
from matplotlib import pyplot as plt

fs_rate, signal = wavfile.read("db1.wav")
#print ("Frequency sampling", fs_rate)
l_audio = len(signal.shape)
#print ("Channels", l_audio)
if l_audio == 2:
    signal = signal.sum(axis=1) / 2

signal2 = signal + 480000000    
N = signal2.shape[0]
#print ("Complete Samplings N", N)
secs = N / float(fs_rate)
#print ("secs", secs)
Ts = 1.0/fs_rate # sampling interval in time
#print ("Timestep between samples Ts", Ts)
t = scipy.arange(0, secs, Ts) # time vector as scipy arange field / numpy.ndarray

#FFT1 = abs(scipy.fft(signal2[0:44100]))
FFT2 = abs(scipy.fft(signal2[44100:88200]))
FFT3 = abs(scipy.fft(signal2[88200:132300]))
FFT4 = abs(scipy.fft(signal2[132300:176400]))
FFT5 = abs(scipy.fft(signal2[176400:220500]))

#FFT_side1 = FFT1[range(N//20)] # one side FFT range
FFT_side2 = FFT2[range(N//20)] # one side FFT range
FFT_side3 = FFT3[range(N//20)] # one side FFT range
FFT_side4 = FFT4[range(N//20)] # one side FFT range
FFT_side5 = FFT5[range(N//20)] # one side FFT range

#freqs1 = scipy.fftpack.fftfreq(signal2[0:44100].size, t[1]-t[0])
freqs2 = scipy.fftpack.fftfreq(signal2[44100:88200].size, t[1]-t[0])
freqs3 = scipy.fftpack.fftfreq(signal2[88200:132300].size, t[1]-t[0])
freqs4 = scipy.fftpack.fftfreq(signal2[132300:176400].size, t[1]-t[0])
freqs5 = scipy.fftpack.fftfreq(signal2[176400:220500].size, t[1]-t[0])

#fft_freqs = np.array(freqs)

#freqs_side1 = freqs1[range(N//20)] # one side frequency range
freqs_side2 = freqs2[range(N//20)] # one side frequency range
freqs_side3 = freqs3[range(N//20)] # one side frequency range
freqs_side4 = freqs4[range(N//20)] # one side frequency range
freqs_side5 = freqs5[range(N//20)] # one side frequency range


#fft_freqs_side = np.array(freqs_side)

#abs(FFT_side1)
abs(FFT_side2)
abs(FFT_side3)
abs(FFT_side4)
abs(FFT_side5)

for a in range(60):
    #FFT_side1[a] = 0
    FFT_side2[a] = 0
    FFT_side3[a] = 0
    FFT_side4[a] = 0
    FFT_side5[a] = 0

plt.subplot(611)
p1 = plt.plot(t, signal2, "r") # plotting the signal
plt.xlabel('Time')
plt.ylabel('Amplitude')

# plt.subplot(612)
# p3 = plt.plot(freqs_side1, FFT_side1, "b") # plotting the positive fft spectrum
# plt.xlabel('Frequency (Hz)')
# plt.ylabel('Amplitude')

plt.subplot(613)
p3 = plt.plot(freqs_side2, FFT_side2, "g") # plotting the positive fft spectrum
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')

plt.subplot(614)
p3 = plt.plot(freqs_side3, FFT_side3, "g") # plotting the positive fft spectrum
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')

plt.subplot(615)
p3 = plt.plot(freqs_side4, FFT_side4, "g") # plotting the positive fft spectrum
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')

plt.subplot(616)
p3 = plt.plot(freqs_side5, FFT_side5, "g") # plotting the positive fft spectrum
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')

plt.show()

Plots

Solution

  • What you are looking can be accomplished in a single lane, by using the standard command for the STFT