System identification/ Filter estimation to mimic frequency equalizer of audio with Scipy
Thanks for all your comments. I finally found the answer this article and implemented it in python:https://www.dsprelated.com/freebooks/filters/Time_Domain_Filter_Estimation.html It works pretty well, if you first find the transfer function h(t) with raw and equalized white noise and then apply the function as a filter to any audio data: 

import numpy as np
import scipy

def learn_equalizer(signal_x, signal_y, detail_level):
    # Create Toeplitz Matrix
    padding = np.zeros(detail_level, signal_x.dtype)
    first_col = np.r_[signal_x, padding]
    first_row = np.r_[signal_x[0], padding]
    X = scipy.linalg.toeplitz(first_col, first_row)

    # Implement the formula for finding the optimal solution numerically
    X = X[0:len(X)-detail_level,]
    h = np.dot(X.T,X)
    h = scipy.linalg.inv(h)
    h = np.dot(h,X.T)
    equalizer = np.dot(h,signal_y)

    return equalizer


Finally looking at the curves let's consider two plots: One with the raw and equalized signal, to show their differences. The other plot shows the equalized signal and the "mimicked" equalizer. Here you see how good the learned filter is, when applied to real sounds after being trained on white noise.

Signal comparison: