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43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
from scipy.io import wavfile # scipy library to read wav files
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import numpy as np
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AudioName = "target.wav" # Audio File
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fs, Audiodata = wavfile.read(AudioName)
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# Plot the audio signal in time
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import matplotlib.pyplot as plt
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plt.plot(Audiodata)
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plt.title('Audio signal in time',size=16)
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# spectrum
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from scipy.fftpack import fft # fourier transform
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n = len(Audiodata)
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AudioFreq = fft(Audiodata)
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AudioFreq = AudioFreq[0:int(np.ceil((n+1)/2.0))] #Half of the spectrum
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MagFreq = np.abs(AudioFreq) # Magnitude
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MagFreq = MagFreq / float(n)
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# power spectrum
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MagFreq = MagFreq**2
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if n % 2 > 0: # ffte odd
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MagFreq[1:len(MagFreq)] = MagFreq[1:len(MagFreq)] * 2
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else:# fft even
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MagFreq[1:len(MagFreq) -1] = MagFreq[1:len(MagFreq) - 1] * 2
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plt.figure()
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freqAxis = np.arange(0,int(np.ceil((n+1)/2.0)), 1.0) * (fs / n);
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plt.plot(freqAxis/1000.0, 10*np.log10(MagFreq)) #Power spectrum
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plt.xlabel('Frequency (kHz)'); plt.ylabel('Power spectrum (dB)');
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#Spectrogram
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from scipy import signal
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N = 512 #Number of point in the fft
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f, t, Sxx = signal.spectrogram(Audiodata, fs,window = signal.blackman(N),nfft=N)
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plt.figure()
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plt.pcolormesh(t, f,10*np.log10(Sxx)) # dB spectrogram
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#plt.pcolormesh(t, f,Sxx) # Lineal spectrogram
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plt.ylabel('Frequency [Hz]')
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plt.xlabel('Time [seg]')
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plt.title('Spectrogram with scipy.signal',size=16);
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plt.show() |