python - 使用 scipy.signal.resample 对信号进行重采样

Question

我尝试使用以下代码将生成的信号从 256 样本重新采样为 20 样本:

import scipy.signal 
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 256, endpoint=False)
y = np.cos(-x**2/6.0)
yre = signal.resample(y,20)
xre = np.linspace(0, 10, len(yre), endpoint=False)
plt.plot(x,y,'b', xre,yre,'or-')
plt.show()

返回此图(显然是正确的):

但是，可以注意到，第一个样本的近似值很差。我相信 resample 计算属于等距样本组的样本的平均值，在这种情况下，似乎第一个样本子组在开始时用零填充以估计第一个输出样本。

因此，我认为可以通过告诉 resample 函数我不想用零填充第一个子组来成功估计第一个样本。

有人可以帮助我实现对这个信号的正确重采样吗？

提前致谢。

Answer 1

我遇到了类似的问题。在网上找到的解决方案似乎也比 scipy.signal.resample ( https://github.com/nwhitehead/swmixer/blob/master/swmixer.py ) 更快。它基于 np.interp 函数。还添加了 scipy.signal.resample_poly 用于比较(在这种情况下不是最好的)。

import scipy.signal 
import matplotlib.pyplot as plt
import numpy as np

# DISCLAIMER: This function is copied from https://github.com/nwhitehead/swmixer/blob/master/swmixer.py, 
#             which was released under LGPL. 
def resample_by_interpolation(signal, input_fs, output_fs):

    scale = output_fs / input_fs
    # calculate new length of sample
    n = round(len(signal) * scale)

    # use linear interpolation
    # endpoint keyword means than linspace doesn't go all the way to 1.0
    # If it did, there are some off-by-one errors
    # e.g. scale=2.0, [1,2,3] should go to [1,1.5,2,2.5,3,3]
    # but with endpoint=True, we get [1,1.4,1.8,2.2,2.6,3]
    # Both are OK, but since resampling will often involve
    # exact ratios (i.e. for 44100 to 22050 or vice versa)
    # using endpoint=False gets less noise in the resampled sound
    resampled_signal = np.interp(
        np.linspace(0.0, 1.0, n, endpoint=False),  # where to interpret
        np.linspace(0.0, 1.0, len(signal), endpoint=False),  # known positions
        signal,  # known data points
    )
    return resampled_signal

x = np.linspace(0, 10, 256, endpoint=False)
y = np.cos(-x**2/6.0)
yre = scipy.signal.resample(y,20)
xre = np.linspace(0, 10, len(yre), endpoint=False)

yre_polyphase = scipy.signal.resample_poly(y, 20, 256)
yre_interpolation = resample_by_interpolation(y, 256, 20)

plt.figure(figsize=(10, 6))
plt.plot(x,y,'b', xre,yre,'or-')
plt.plot(xre, yre_polyphase, 'og-')
plt.plot(xre, yre_interpolation, 'ok-')
plt.legend(['original signal', 'scipy.signal.resample', 'scipy.signal.resample_poly', 'interpolation method'], loc='lower left')
plt.show()

关心!然而，这种方法似乎执行了一些不需要的低通滤波。

x = np.linspace(0, 10, 16, endpoint=False)
y = np.random.RandomState(seed=1).rand(len(x))
yre = scipy.signal.resample(y, 18)
xre = np.linspace(0, 10, len(yre), endpoint=False)

yre_polyphase = scipy.signal.resample_poly(y, 18, 16)
yre_interpolation = resample_by_interpolation(y, 16, 18)

plt.figure(figsize=(10, 6))
plt.plot(x,y,'b', xre,yre,'or-')
plt.plot(xre, yre_polyphase, 'og-')
plt.plot(xre, yre_interpolation, 'ok-')
plt.legend(['original signal', 'scipy.signal.resample', 'scipy.signal.resample_poly', 'interpolation method'], loc='lower left')
plt.show()

不过，这是我得到的最好的结果，但我希望有人能提供更好的结果。