重新采样一个 numpy 数组

本文介绍了重新采样一个 numpy 数组的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

重采样数组很容易，比如

 a = numpy.array([1,2,3,4,5,6,7,8,9,10])

具有整数重采样因子.例如，因子为 2 :

b = a[::2] # [1 3 5 7 9]

但是使用非整数重采样因子，它不会那么容易工作:

c = a[::1.5] # [1 2 3 4 5 6 7 8 9 10] =>不是需要的...

应该是(使用线性插值):

[1 2.5 4 5.5 7 8.5 10]

或(通过取数组中最近的邻居)

[1 3 4 6 7 9 10]

如何使用非整数重采样因子对 numpy 数组进行重采样?

应用示例:音频信号重采样/重音

解决方案

As scipy.signal.resample 可以是 (一维线性插值"):

It's easy to resample an array like

 a = numpy.array([1,2,3,4,5,6,7,8,9,10])

with an integer resampling factor. For instance, with a factor 2 :

b = a[::2]    # [1 3 5 7 9]

But with a non-integer resampling factor, it doesn't work so easily :

c = a[::1.5]    # [1 2 3 4 5 6 7 8 9 10]  => not what is needed...

It should be (with linear interpolation):

[1 2.5 4 5.5 7 8.5 10]

or (by taking the nearest neighbour in the array)

[1 3 4 6 7 9 10]

How to resample a numpy array with a non-integer resampling factor?

Example of application: audio signal resampling / repitching

解决方案

As scipy.signal.resample can be very slow, I searched for other algorithms adapted for audio.

It seems that Erik de Castro Lopo's SRC (a.k.a. Secret Rabbit Code a.k.a. libsamplerate) is one of the best resampling algorithms available.

It is used by scikit's scikit.samplerate, but this library seems to be complicated to install (I gave up on Windows).
Fortunately, there is an easy-to-use and easy-to-install Python wrapper for libsamplerate, made by Tino Wagner: https://pypi.org/project/samplerate/. Installation with pip install samplerate. Usage:
```
import samplerate
from scipy.io import wavfile
sr, x = wavfile.read('input.wav')  # 48 khz file
y = samplerate.resample(x, 44100 * 1.0 / 48000, 'sinc_best')
```

Addendum: comparison of spectrograms of a resampled frequency sweep (20hz to 20khz):

1) Original

2) Resampled with libsamplerate / samplerate module

3) Resampled with numpy.interp ("One-dimensional linear interpolation"):

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