Normalize values between -1 and 1 inclusive

Question

I am trying to generate a .wav file in python using Numpy. I have voltages ranging between 0-5V and I need to normalize them between -1 and 1 to use them in a .wav file.

I have seen this website which uses numpy to generate a wav file but the algorithm used to normalize is no long available.

Can anyone explain how I would go about generating these values in Python on my Raspberry Pi.

This [Answer](http://stackoverflow.com/questions/10357992/how-to-generate-audio-from-a-numpy-array) may help. — Anzel, Oct 21 '15 at 10:04
Is [this](https://github.com/mgeier/python-audio/blob/master/audio-files/utility.py#L7) the thing you were saying is no longer available? — David Z, Oct 21 '15 at 10:05

score 3 · Answer 1 · answered Oct 21 '15 at 11:21

isn't this just a simple calculation? Divide by half the maximum value and minus 1:

In [12]: data=np.linspace(0,5,21)

In [13]: data
Out[13]: 
array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ,  1.25,  1.5 ,  1.75,  2.  ,
        2.25,  2.5 ,  2.75,  3.  ,  3.25,  3.5 ,  3.75,  4.  ,  4.25,
        4.5 ,  4.75,  5.  ])

In [14]: data/2.5-1.
Out[14]: 
array([-1. , -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1,  0. ,
        0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])

this is assuming that the minimum value is zero – U3.1415926 Dec 08 '22 at 20:59 — U3.1415926, Dec 08 '22 at 20:59

score 1 · Answer 2 · answered Dec 08 '22 at 21:05

The following function should do what you want, irrespective of the range of the input data, i.e., it works also if you have negative values.

import numpy as np
def my_norm(a):
    ratio = 2/(np.max(a)-np.min(a)) 
    #as you want your data to be between -1 and 1, everything should be scaled to 2, 
    #if your desired min and max are other values, replace 2 with your_max - your_min
    shift = (np.max(a)+np.min(a))/2 
    #now you need to shift the center to the middle, this is not the average of the values.
    return (a - shift)*ratio

my_norm(data)

score -1 · Answer 3 · answered Mar 25 '21 at 01:01

You can use the fit_transform method in sklearn.preprocessing.StandardScaler. This method will remove the mean from your data and scale your array to unit variance (-1,1)

from sklearn.preprocessing import StandardScaler
data = np.asarray([[0, 0, 0],
     [1, 1, 1],
     [2,1, 3]])
data = StandardScaler().fit_transform(data)

And if you print out data, you will now have:

[[-1.22474487 -1.41421356 -1.06904497]
[ 0.          0.70710678 -0.26726124]
[ 1.22474487  0.70710678  1.33630621]]

Normalize values between -1 and 1 inclusive

3 Answers3