重新采样一个 numpy 数组 [英] Resample a numpy array

问题描述

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')  
  

Interesting reading / comparison of many resampling solutions: http://signalsprocessed.blogspot.com/2016/08/audio-resampling-in-python.html

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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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