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music_mixing_style_transfer / mixing_style_transfer /mixing_manipulator /normalization_imager.py
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"""
Implementation of the normalization process of stereo-imaging and panning effects
"""
import numpy as np
import sys
import os
currentdir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(currentdir)
from common_audioeffects import AugmentationChain, Haas
'''
### normalization algorithm for stereo imaging and panning effects ###
process :
1. inputs 2-channeled audio
2. apply Haas effects if the input audio is almost mono
3. normalize mid-side channels according to target precomputed feature value
4. normalize left-right channels 50-50
5. normalize mid-side channels again
'''
def normalize_imager(data, \
target_side_mid_bal=0.9, \
mono_threshold=0.95, \
sr=44100, \
eps=1e-04, \
verbose=False):
# to mid-side channels
mid, side = lr_to_ms(data[:,0], data[:,1])
if verbose:
print_balance(data[:,0], data[:,1])
print_balance(mid, side)
print()
# apply mid-side weights according to energy
mid_e, side_e = np.sum(mid**2), np.sum(side**2)
total_e = mid_e + side_e
# apply haas effect to almost-mono signal
if mid_e/total_e > mono_threshold:
aug_chain = AugmentationChain(fxs=[(Haas(sample_rate=sr), 1, True)])
data = aug_chain([data])[0]
mid, side = lr_to_ms(data[:,0], data[:,1])
if verbose:
print_balance(data[:,0], data[:,1])
print_balance(mid, side)
print()
# normalize mid-side channels (stereo imaging)
new_mid, new_side = process_balance(mid, side, tgt_e1_bal=target_side_mid_bal, eps=eps)
left, right = ms_to_lr(new_mid, new_side)
imaged = np.stack([left, right], 1)
if verbose:
print_balance(new_mid, new_side)
print_balance(left, right)
print()
# normalize panning to have the balance of left-right channels 50-50
left, right = process_balance(left, right, tgt_e1_bal=0.5, eps=eps)
mid, side = lr_to_ms(left, right)
if verbose:
print_balance(mid, side)
print_balance(left, right)
print()
# normalize again mid-side channels (stereo imaging)
new_mid, new_side = process_balance(mid, side, tgt_e1_bal=target_side_mid_bal, eps=eps)
left, right = ms_to_lr(new_mid, new_side)
imaged = np.stack([left, right], 1)
if verbose:
print_balance(new_mid, new_side)
print_balance(left, right)
print()
return imaged
# balance out 2 input data's energy according to given balance
# tgt_e1_bal range = [0.0, 1.0]
# tgt_e2_bal = 1.0 - tgt_e1_bal_range
def process_balance(data_1, data_2, tgt_e1_bal=0.5, eps=1e-04):
e_1, e_2 = np.sum(data_1**2), np.sum(data_2**2)
total_e = e_1 + e_2
tgt_1_gain = np.sqrt(tgt_e1_bal * total_e / (e_1 + eps))
new_data_1 = data_1 * tgt_1_gain
new_e_1 = e_1 * (tgt_1_gain ** 2)
left_e_1 = total_e - new_e_1
tgt_2_gain = np.sqrt(left_e_1 / (e_2 + 1e-3))
new_data_2 = data_2 * tgt_2_gain
return new_data_1, new_data_2
# left-right channeled signal to mid-side signal
def lr_to_ms(left, right):
mid = left + right
side = left - right
return mid, side
# mid-side channeled signal to left-right signal
def ms_to_lr(mid, side):
left = (mid + side) / 2
right = (mid - side) / 2
return left, right
# print energy balance of 2 inputs
def print_balance(data_1, data_2):
e_1, e_2 = np.sum(data_1**2), np.sum(data_2**2)
total_e = e_1 + e_2
print(total_e, e_1/total_e, e_2/total_e)