波形文件的頻譜圖

Question

我正試圖在python中獲取wav文件的頻譜圖。但它給人的錯誤：

'module' object has no attribute 'spectrogram'.

這裏是代碼：

import scipy.io.wavfile 
from scipy.io.wavfile import read 
from scipy import signal 

sr_value, x_value = scipy.io.wavfile.read("test.wav") 

f, t, Sxx= signal.spectrogram(x_value,sr_value) 

有也沒有辦法獲得wav文件的頻譜？

Answer 1

使用scipy.fftpack我們可以繪製fft內容作爲光譜圖。

**這是基於我的舊帖子**

下面的示例代碼。

"""Plots 
Time in MS Vs Amplitude in DB of a input wav signal 
""" 

import numpy 
import matplotlib.pyplot as plt 
import pylab 
from scipy.io import wavfile 
from scipy.fftpack import fft 


myAudio = "audio.wav" 

#Read file and get sampling freq [ usually 44100 Hz ] and sound object 
samplingFreq, mySound = wavfile.read(myAudio) 

#Check if wave file is 16bit or 32 bit. 24bit is not supported 
mySoundDataType = mySound.dtype 

#We can convert our sound array to floating point values ranging from -1 to 1 as follows 

mySound = mySound/(2.**15) 

#Check sample points and sound channel for duel channel(5060, 2) or (5060,) for mono channel 

mySoundShape = mySound.shape 
samplePoints = float(mySound.shape[0]) 

#Get duration of sound file 
signalDuration = mySound.shape[0]/samplingFreq 

#If two channels, then select only one channel 
mySoundOneChannel = mySound[:,0] 

#Plotting the tone 

# We can represent sound by plotting the pressure values against time axis. 
#Create an array of sample point in one dimension 
timeArray = numpy.arange(0, samplePoints, 1) 

# 
timeArray = timeArray/samplingFreq 

#Scale to milliSeconds 
timeArray = timeArray * 1000 

#Plot the tone 
plt.plot(timeArray, mySoundOneChannel, color='G') 
plt.xlabel('Time (ms)') 
plt.ylabel('Amplitude') 
plt.show() 


#Plot frequency content 
#We can get frquency from amplitude and time using FFT , Fast Fourier Transform algorithm 

#Get length of mySound object array 
mySoundLength = len(mySound) 

#Take the Fourier transformation on given sample point 
#fftArray = fft(mySound) 
fftArray = fft(mySoundOneChannel) 

numUniquePoints = numpy.ceil((mySoundLength + 1)/2.0) 
fftArray = fftArray[0:numUniquePoints] 

#FFT contains both magnitude and phase and given in complex numbers in real + imaginary parts (a + ib) format. 
#By taking absolute value , we get only real part 

fftArray = abs(fftArray) 

#Scale the fft array by length of sample points so that magnitude does not depend on 
#the length of the signal or on its sampling frequency 

fftArray = fftArray/float(mySoundLength) 

#FFT has both positive and negative information. Square to get positive only 
fftArray = fftArray **2 

#Multiply by two (research why?) 
#Odd NFFT excludes Nyquist point 
if mySoundLength % 2 > 0: #we've got odd number of points in fft 
    fftArray[1:len(fftArray)] = fftArray[1:len(fftArray)] * 2 

else: #We've got even number of points in fft 
    fftArray[1:len(fftArray) -1] = fftArray[1:len(fftArray) -1] * 2 

freqArray = numpy.arange(0, numUniquePoints, 1.0) * (samplingFreq/mySoundLength); 

#Plot the frequency 
plt.plot(freqArray/1000, 10 * numpy.log10 (fftArray), color='B') 
plt.xlabel('Frequency (Khz)') 
plt.ylabel('Power (dB)') 
plt.show() 

#Get List of element in frequency array 
#print freqArray.dtype.type 
freqArrayLength = len(freqArray) 
print "freqArrayLength =", freqArrayLength 
numpy.savetxt("freqData.txt", freqArray, fmt='%6.2f') 

#Print FFtarray information 
print "fftArray length =", len(fftArray) 
numpy.savetxt("fftData.txt", fftArray) 

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