linearwinvolume 1.1.0

A Python implementation of pycaw that doesn't function on a decibel scale

linearWinVolume

A Python implementation of pycaw that doesn't function on a decibel scale

In order to linearly interface with Windows' volume control in a manner that matches the UI's output, linearWinVolume computes a logarithmic regression from user collected sample data points. From there it optionally applies a linear correction value such that when setting and getting the volume state it is accurate to rougly ~1 unit of Windows volume at all times.

Installation

Windows:

pip install linearwinvolume

Setup (Need to setup every sound device individually):

# With desired sound device as Output Device under windows
import linearwinvolume
linearwinvolume.setup()
# From here, follow the CLI prompts to callibrate your sound device's dB levels

In order to complete setup:

1. Select a sample size of data points (Windows volume units)
2. Input Windows volume values until Windows volume is 0
3. (OPTIONAL) Compute a linear correction
   • This massively improves accuracy on some sound devices
   • The setup function will count down from 100 to 0 setting your volume accordingly
   • Enter any letter once the guessed value matches the true Windows value

Usage examples

This python module offers 4 functions. The first, linearwinvolume.setup(), is used to callibrate the sound device. The rest are:

# Set volume to 55%
linearwinvolume.set_volume(55)

# Get current volume, returns integer from 0 - 100
linearwinvolume.get_volume()

# Change volume, to increase volume, use a positive integer, to decrease use a negative value
linearwinvolume.change_volume()

Explanation

In order to derive an equation that accurately represents all volume values form 0 to 100, a logarithmic regression is preformed on the collected sample values.

Initially, the program computes the logarithmic regression which takes the form of y = A ln(x) + B

Oftentimes, this is enough to maintain ~1 unit of Windows volume unit error.

In order to improve accuracy on some devices, an additional linear term is added such that the new function takes the form of y = A ln(x) + C x + (B + D)

C is defined as 100 - intersect divided by the max volume in Db - initial logarithmic regression for x =100

D is defined as the difference between the max volume and y = A ln(x) + C x + B solved for x = 100

Using Mathematica, the resultant equation, y = A ln(x) + C x + (B + D), is solved for x, to reveal a new Equation, in order to use the get_volume() function. As a result of it using the Lambert W function W(z), this package requires scipy.

The configuration file where values are saved is stored inside the pip directory and is global for a python installation. It takes the form of:

[Headset (Headphone adapter)]
natural logarithm coeff = 10.690485218963337
constant offset = -54.99262202770247
correction coeff = 0.05761118223586133
min_vol = -50.0
max_vol = 0.0
samples = 25

Release History

• 1.1.0
  • Added linear correction algorithm that dramatically improves accuracy on some devices
• 1.0.0
  • Initial Release

Credits

Adrian Ornelas – [e-mail]

Distributed under the MIT license. See LICENSE for more information.

https://github.com/That-CC