is an algorithm developed for generating an equalizer to correct the in-room frequency response (FR) of a loudspeaker to a specified target curve.
Most room correction algorithms create parametric filters to improve the in-room FR of a loudspeaker.
This algorithm creates a config for a detailed graphical equalizer that can be imported into software equalizers like EQApo or Wavelet.
This is especially useful when you want to do room correction for a bluetooth speaker (Wavelet)
You can customize the target curve and certain parameters of equalizer you want to create.
To use the algorithm, you need to export FR measurements as *.txt files from REW.
The simplest way is to either supply a list of paths to req files or a directory with the files.
eq_str = get_graph_eq_str(file_paths=["path/to/file1", "path/to/file2", "path/to/file3"])
# Or if all measurements are inside a directory:
eq_str = get_graph_eq_str(measurements_dir="path/to/measurement/dir")
In this case, the target curve is linear, and the equalizer is created with 128 log-spaced points in the 20-20000 Hz range.
More examples on the usage, are in the examples.py
The algorithm adjusts the target curve regarding the bass response of the measurements,
in order not to apply too much boost, as this leads to a lower overall volume and introduces distortion.
In the next step, an equalization curve is created to minimize the deviation from the target.
The smoothness of the eq-curve is increasing from low to high frequencies. Different weighting functions can be used, to change the smoothing behaviour, more on that later.
You also get an estimation of the in-room FR after equalization.
It is possible to customize the properties of the created eq definition. Just pass your own EQConfig to get_graph_eq_str or create_eq
EQ-Config
eq_from=20: Lower bound for eq frequencies
eq_to=20000: Upper bound for eq frequencies
eq_res=128: The number of eq points.
If supplied, eq_res log-spaced points will be computed between eq_from and eq_to.
eq_points: Can be supplied instead of eq_from, eq_to and eq_res. In this case,
the eq points are supplied instead of being computed
set_max_zero=True: Determines, if the max boost value of the created eq
get anchored at 0 db, in order not to introduce distortion
max_boost=10: the maximum db boost that will be applied.
weighting_fun: function that applies weighting based on smooting factor and frequency
To compute an equalizer, 512 boost levels for log spaced frequencies are evaluated.
For each of the frequencies, the process is as follows:
We take a strongly smoothed version of each measurement, and compare the level of our target curve to the current spl.
We look for a dB adjustment at this frequency to minimize the error between target-spl and equalized-spl. This is a classical example of a convex optimization problem. You can find more on this in the Algorithm PDF.
In multiple iterations, the boost gets adjusted, by repeating the above process with decreasingly smoothed measurements.
In each iteration, we take the difference, between the current level + dB adjustment and the target level.
This difference gets weighted and added to the current dB adjustment.
We repeat this process until we used the raw (no smoothing) measurements.
Higher frequencies and number of the current iteration decrease the factor to which the boost gets changed.
This way, we create an eq that gets more smooth, the higher the frequency gets. The weighting process can be changed by supplying a custom weighting function to the constructor of the EQConfig.