根据公式解析离散傅立叶
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2022-06-13 21:14:13
import numpy as npimport matplotlib.pyplot as pltdef signal_xHz(A, fi, time_s, sample): print(fi * time_s * 2 * np.pi) print(sample* time_s) return A * np.sin(np.linspace(0, fi * time_s * 2 * np.pi , num=sample* time_s))sig = signal_xHz(2...
import numpy as np
import matplotlib.pyplot as plt
def signal_xHz(A, fi, time_s, sample):
print(fi * time_s * 2 * np.pi)
print(sample* time_s)
return A * np.sin(np.linspace(0, fi * time_s * 2 * np.pi , num=sample* time_s))
sig = signal_xHz(20000,1000,5,8000)
31415.926535897932
40000
plt.plot(sig[0:10])
[<matplotlib.lines.Line2D at 0x11bbba3d0>]
sig[0:10]
array([ 0.00000000e+00, 1.41424133e+04, 2.00000000e+04, 1.41413025e+04,
-1.57083560e+00, -1.41435240e+04, -1.99999999e+04, -1.41401917e+04,
3.14167118e+00, 1.41446346e+04])
type(sig)
sig.astype('int16').tofile('sin.pcm')
import os
os.system('sox -t raw -c 1 -e signed-integer -b 16 -r 8000 sin.pcm sin.wav')
os.system('play sin.wav')
0
print (sig.astype('int16')[0:100])
[ 0 14142 19999 14141 -1 -14143 -19999 -14140 3 14144
19999 14139 -4 -14145 -19999 -14137 6 14146 19999 14136
-7 -14147 -19999 -14135 9 14149 19999 14134 -10 -14150
-19999 -14133 12 14151 19999 14132 -14 -14152 -19999 -14131
15 14153 19999 14130 -17 -14154 -19999 -14129 18 14155
19999 14127 -20 -14156 -19999 -14126 21 14157 19999 14125
-23 -14159 -19999 -14124 25 14160 19999 14123 -26 -14161
-19999 -14122 28 14162 19999 14121 -29 -14163 -19999 -14120
31 14164 19999 14119 -32 -14165 -19999 -14117 34 14166
19999 14116 -36 -14167 -19999 -14115 37 14169 19999 14114]
ak=0
N=8000
k=1000
for i in np.arange(0,8000-1):
ak+=sig[i]*np.cos(2*np.pi*k*i/N)
print(ak/N)
784.6553321430675
ak=0
N=8000
k=1000
for i in np.arange(0,8000-1):
ak+=sig[i]*np.exp(2*np.pi*k*i*(-1.j)/N)
print(ak/N)
(784.6553321424174-9957.788439085387j)
y=sig[0:8000]
YY = np.fft.fft(y) # 未归一化
Y = np.fft.fft(y)/len(y) # fft computing and normalization 归一化
Y1 = Y[range(int(len(y)/2))]
print(Y[999:1001])
[ 18.92979144 -242.80867967j 783.61624233-9958.8275289j ]
plt.plot(Y1)
/Users/chenpeiwen/opt/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part
return array(a, dtype, copy=False, order=order)
[<matplotlib.lines.Line2D at 0x11bd3d050>]
import librosa
import librosa.display
y, sr = librosa.load('sin.wav')
S = np.abs(librosa.stft(y))
plt.figure()
librosa.display.specshow(S**2, sr=sr, y_axis='log') # 从波形获取功率谱图
plt.colorbar()
plt.title('Power spectrogram')
Text(0.5, 1.0, 'Power spectrogram')
mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)
print(mfccs.shape) # (40, 65)
(40, 216)
本文地址:https://blog.csdn.net/daad777/article/details/107122588
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