Numpy combine channels

[Wanderer]

I have an array generated by audiolab of left and right stereo channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine the left and right channels to get an array [2,3,5]. Is there a numpy command to do that?

Thanks

 

[Nick Cash]

I have an array generated by audiolab of left and right stereo

channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine
the left and right channels to get an array [2,3,5]. Is there a numpy
command to do that?

You may be over-thinking this, and numpy might not be necessary.

A simple solution would be just a quick list comprehension:

stereo_array = [[1, 1], [1, 2], [2, 3]]
mono_array = [l+r for (l, r) in stereo_array]

Thanks,
Nick Cash

 

[Wanderer]

I have an array generated by audiolab of left and right stereo channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine the left and right channels to get an array [2,3,5]. Is there a numpy command to do that?



Thanks

I figured it out. numpy.sum(array, axis=1).

 

[MRAB]

I have an array generated by audiolab of left and right stereo
channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine
the left and right channels to get an array [2,3,5]. Is there a numpy
command to do that?

import numpy
numpy.array([[1,1],[1,2],[2,3]], dtype="i")

array([[1, 1],
[1, 2],
[2, 3]])

a[:, 0] array([1, 1, 2])
a[:, 1] array([1, 2, 3])
a[:, 0] + a[:, 1]

array([2, 3, 5])

But should they be added together to make mono?

Suppose, for example, that both channels have a maximum value. Their
sum would be _twice_ the maximum.

Therefore, I think that it should probably be the average.

(a[:, 0] + a[:, 1]) / 2

array([1, 1, 2])

 

[Wanderer]

I have an array generated by audiolab of left and right stereo

channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine

the left and right channels to get an array [2,3,5]. Is there a numpy

command to do that?

import numpy
numpy.array([[1,1],[1,2],[2,3]], dtype="i")

array([[1, 1],

[1, 2],

[2, 3]])

a[:, 0]

array([1, 1, 2])

a[:, 1]

array([1, 2, 3])

a[:, 0] + a[:, 1]

array([2, 3, 5])



But should they be added together to make mono?



Suppose, for example, that both channels have a maximum value. Their

sum would be _twice_ the maximum.



Therefore, I think that it should probably be the average.

(a[:, 0] + a[:, 1]) / 2

array([1, 1, 2])

I'm decoding morse code. So it's CV dots and dashes.

 

[Wanderer]

I have an array generated by audiolab of left and right stereo

channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine

the left and right channels to get an array [2,3,5]. Is there a numpy

command to do that?

import numpy
numpy.array([[1,1],[1,2],[2,3]], dtype="i")

array([[1, 1],

[1, 2],

[2, 3]])

a[:, 0]

array([1, 1, 2])

a[:, 1]

array([1, 2, 3])

a[:, 0] + a[:, 1]

array([2, 3, 5])



But should they be added together to make mono?



Suppose, for example, that both channels have a maximum value. Their

sum would be _twice_ the maximum.



Therefore, I think that it should probably be the average.

(a[:, 0] + a[:, 1]) / 2

array([1, 1, 2])

I'm decoding morse code. So it's CV dots and dashes.

 

[Wanderer]

On 10/09/2012 20:39, Wanderer wrote:

I have an array generated by audiolab of left and right stereo

channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine

the left and right channels to get an array [2,3,5]. Is there a numpy

command to do that?

import numpy

numpy.array([[1,1],[1,2],[2,3]], dtype="i")

array([[1, 1],
[1, 2],
[2, 3]])

a[:, 0]

array([1, 1, 2])

a[:, 1]

array([1, 2, 3])

a[:, 0] + a[:, 1]

array([2, 3, 5])
But should they be added together to make mono?
Suppose, for example, that both channels have a maximum value. Their
sum would be _twice_ the maximum.
Therefore, I think that it should probably be the average.

(a[:, 0] + a[:, 1]) / 2

array([1, 1, 2])

I'm decoding morse code. So it's CV dots and dashes.

In case anyone is interested, here is the full code.

# morsecode.py
import numpy as np
from scikits.audiolab import wavread
from scipy.signal import decimate
from pylab import plot
from pylab import show
import os

def movingaverage(interval, window_size):
window = np.ones(int(window_size)) / float(window_size)
return np.convolve(interval, window, 'same')

def wav2morse(resultDir, filename):
""" Convert a wave file to morse code
resultDir: directory for wave file and results
filename: wave file name

"""
data, _fs, _enc = wavread(resultDir + '\\' + filename)
data = np.sum(data, axis=1)
data = np.fabs(data)
data = movingaverage(data, 100)
data = decimate(data, 2)
highcount = 0
lowcount = 0
fileBase, _fileExt = os.path.splitext(filename)
f = open(resultDir + '\\' + fileBase + '.txt', 'w')
for d in data:
if d > 0.3:
if lowcount > 3000:
f.write(' ')
lowcount = 0
highcount += 1
else:
if highcount > 3000:
f.write('-')
elif highcount > 1000:
f.write('.')
highcount = 0
lowcount += 1
f.close()

 

[Wanderer]

On 10/09/2012 20:39, Wanderer wrote:

I have an array generated by audiolab of left and right stereo

channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine

the left and right channels to get an array [2,3,5]. Is there a numpy

command to do that?

import numpy

numpy.array([[1,1],[1,2],[2,3]], dtype="i")

array([[1, 1],
[1, 2],
[2, 3]])

a[:, 0]

array([1, 1, 2])

a[:, 1]

array([1, 2, 3])

a[:, 0] + a[:, 1]

array([2, 3, 5])
But should they be added together to make mono?
Suppose, for example, that both channels have a maximum value. Their
sum would be _twice_ the maximum.
Therefore, I think that it should probably be the average.

(a[:, 0] + a[:, 1]) / 2

array([1, 1, 2])

I'm decoding morse code. So it's CV dots and dashes.

In case anyone is interested, here is the full code.

# morsecode.py
import numpy as np
from scikits.audiolab import wavread
from scipy.signal import decimate
from pylab import plot
from pylab import show
import os

def movingaverage(interval, window_size):
window = np.ones(int(window_size)) / float(window_size)
return np.convolve(interval, window, 'same')

def wav2morse(resultDir, filename):
""" Convert a wave file to morse code
resultDir: directory for wave file and results
filename: wave file name

"""
data, _fs, _enc = wavread(resultDir + '\\' + filename)
data = np.sum(data, axis=1)
data = np.fabs(data)
data = movingaverage(data, 100)
data = decimate(data, 2)
highcount = 0
lowcount = 0
fileBase, _fileExt = os.path.splitext(filename)
f = open(resultDir + '\\' + fileBase + '.txt', 'w')
for d in data:
if d > 0.3:
if lowcount > 3000:
f.write(' ')
lowcount = 0
highcount += 1
else:
if highcount > 3000:
f.write('-')
elif highcount > 1000:
f.write('.')
highcount = 0
lowcount += 1
f.close()

 

[Aahz]

I have an array generated by audiolab of left and right stereo
channels. It looks like [[1,1],[1,2],[2,3]]. I would like to combine
the left and right channels to get an array [2,3,5]. Is there a numpy
command to do that?

import numpy
numpy.array([[1,1],[1,2],[2,3]], dtype="i")

array([[1, 1],
[1, 2],
[2, 3]])

a[:, 0] array([1, 1, 2])
a[:, 1] array([1, 2, 3])
a[:, 0] + a[:, 1]

array([2, 3, 5])

But should they be added together to make mono?

Suppose, for example, that both channels have a maximum value. Their
sum would be _twice_ the maximum.

Therefore, I think that it should probably be the average.

(a[:, 0] + a[:, 1]) / 2

array([1, 1, 2])

I'd actually think it should be the max. Consider a stereo where one
side is playing a booming bass while the other side is playing a rest
note -- should the mono combination be half as loud as as the bass?

 

[Dave Angel]

<snip>

But should they be added together to make mono?

Suppose, for example, that both channels have a maximum value. Their
sum would be _twice_ the maximum.

Therefore, I think that it should probably be the average.

(a[:, 0] + a[:, 1]) / 2

array([1, 1, 2])

I'd actually think it should be the max. Consider a stereo where one
side is playing a booming bass while the other side is playing a rest
note -- should the mono combination be half as loud as as the bass?

max would sound awful.

The right answer is to add them with weighting, then scale the whole
waveform according to a new calculation of clipping. Just like a mixer,
you have level controls on each input, then an overall gain.

So if the inputs were x and y, the output would be gain *( x_scale * x
+ y_scale * y), but it'd normally be done in two passes, so as to
minimize the places that are clipped, while maximizing the average.
it's also possible to have gain vary across the time axis, like an agc.
But you wouldn't want that as a default, as it'd destroy the dynamics of
a musical piece.

One more consideration. If these are unsigned values (eg. 0 to 255),
then you should adjust both signals by 128 before storing them as signed
values, do your arithmetic, and then adjust again by adding 128. You
could do the algebraic equivalent, but the programming would be much
simpler on signed values.