python从音频文件中提取音频频谱 [英] Audio spectrum extraction from audio file by python

问题描述

很抱歉，如果我提交了副本，但是我想知道python中是否有任何lib使您能够从音频文件中提取声谱.我希望能够获取音频文件并编写算法，该算法将返回一组数据{TimeStampInFile;频率幅度}.

Sorry if I submit a duplicate, but I wonder if there is any lib in python which makes you able to extract sound spectrum from audio files. I want to be able to take an audio file and write an algoritm which will return a set of data {TimeStampInFile; Frequency-Amplitude}.

我听说这通常被称为节拍检测，但是据我所知，节拍检测不是一种精确的方法，它仅对可视化有用，而我想对提取的数据进行处理，然后将其转换回音频文件.我不需要实时进行此操作.

I heard that this is usually called Beat Detection, but as far as I see beat detection is not a precise method, it is good only for visualisation, while I want to manipulate on the extracted data and then convert it back to an audio file. I don't need to do this real-time.

我将不胜感激.

推荐答案

您可以使用scipy计算并可视化频谱和频谱图，在本测试中，我使用了以下音频文件:

You can compute and visualize the spectrum and the spectrogram this using scipy, for this test i used this audio file: vignesh.wav

from scipy.io import wavfile # scipy library to read wav files
import numpy as np

AudioName = "vignesh.wav" # Audio File
fs, Audiodata = wavfile.read(AudioName)

# Plot the audio signal in time
import matplotlib.pyplot as plt
plt.plot(Audiodata)
plt.title('Audio signal in time',size=16)

# spectrum
from scipy.fftpack import fft # fourier transform
n = len(Audiodata) 
AudioFreq = fft(Audiodata)
AudioFreq = AudioFreq[0:int(np.ceil((n+1)/2.0))] #Half of the spectrum
MagFreq = np.abs(AudioFreq) # Magnitude
MagFreq = MagFreq / float(n)
# power spectrum
MagFreq = MagFreq**2
if n % 2 > 0: # ffte odd 
    MagFreq[1:len(MagFreq)] = MagFreq[1:len(MagFreq)] * 2
else:# fft even
    MagFreq[1:len(MagFreq) -1] = MagFreq[1:len(MagFreq) - 1] * 2 

plt.figure()
freqAxis = np.arange(0,int(np.ceil((n+1)/2.0)), 1.0) * (fs / n);
plt.plot(freqAxis/1000.0, 10*np.log10(MagFreq)) #Power spectrum
plt.xlabel('Frequency (kHz)'); plt.ylabel('Power spectrum (dB)');


#Spectrogram
from scipy import signal
N = 512 #Number of point in the fft
f, t, Sxx = signal.spectrogram(Audiodata, fs,window = signal.blackman(N),nfft=N)
plt.figure()
plt.pcolormesh(t, f,10*np.log10(Sxx)) # dB spectrogram
#plt.pcolormesh(t, f,Sxx) # Lineal spectrogram
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [seg]')
plt.title('Spectrogram with scipy.signal',size=16);

plt.show()

我测试了所有代码，并且可以正常工作，您需要numpy，matplotlib和scipy.

i tested all the code and it works, you need, numpy, matplotlib and scipy.

欢呼

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