# The MIT License (MIT)
#
# Copyright (c) 2015 braindead
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
#
# This code was extracted from the logmmse package (https://pypi.org/project/logmmse/) and I
# simply modified the interface to meet my needs.
import numpy as np
import math
from scipy.special import expn
from collections import namedtuple
NoiseProfile = namedtuple("NoiseProfile", "sampling_rate window_size len1 len2 win n_fft noise_mu2")
def profile_noise(noise, sampling_rate, window_size=0):
"""
Creates a profile of the noise in a given waveform.
:param noise: a waveform containing noise ONLY, as a numpy array of floats or ints.
:param sampling_rate: the sampling rate of the audio
:param window_size: the size of the window the logmmse algorithm operates on. A default value
will be picked if left as 0.
:return: a NoiseProfile object
"""
noise, dtype = to_float(noise)
noise += np.finfo(np.float64).eps
if window_size == 0:
window_size = int(math.floor(0.02 * sampling_rate))
if window_size % 2 == 1:
window_size = window_size + 1
perc = 50
len1 = int(math.floor(window_size * perc / 100))
len2 = int(window_size - len1)
win = np.hanning(window_size)
win = win * len2 / np.sum(win)
n_fft = 2 * window_size
noise_mean = np.zeros(n_fft)
n_frames = len(noise) // window_size
for j in range(0, window_size * n_frames, window_size):
noise_mean += np.absolute(np.fft.fft(win * noise[j:j + window_size], n_fft, axis=0))
noise_mu2 = (noise_mean / n_frames) ** 2
return NoiseProfile(sampling_rate, window_size, len1, len2, win, n_fft, noise_mu2)
def denoise(wav, noise_profile: NoiseProfile, eta=0.15):
"""
Cleans the noise from a speech waveform given a noise profile. The waveform must have the
same sampling rate as the one used to create the noise profile.
:param wav: a speech waveform as a numpy array of floats or ints.
:param noise_profile: a NoiseProfile object that was created from a similar (or a segment of
the same) waveform.
:param eta: voice threshold for noise update. While the voice activation detection value is
below this threshold, the noise profile will be continuously updated throughout the audio.
Set to 0 to disable updating the noise profile.
:return: the clean wav as a numpy array of floats or ints of the same length.
"""
wav, dtype = to_float(wav)
wav += np.finfo(np.float64).eps
p = noise_profile
nframes = int(math.floor(len(wav) / p.len2) - math.floor(p.window_size / p.len2))
x_final = np.zeros(nframes * p.len2)
aa = 0.98
mu = 0.98
ksi_min = 10 ** (-25 / 10)
x_old = np.zeros(p.len1)
xk_prev = np.zeros(p.len1)
noise_mu2 = p.noise_mu2
for k in range(0, nframes * p.len2, p.len2):
insign = p.win * wav[k:k + p.window_size]
spec = np.fft.fft(insign, p.n_fft, axis=0)
sig = np.absolute(spec)
sig2 = sig ** 2
gammak = np.minimum(sig2 / noise_mu2, 40)
if xk_prev.all() == 0:
ksi = aa + (1 - aa) * np.maximum(gammak - 1, 0)
else:
ksi = aa * xk_prev / noise_mu2 + (1 - aa) * np.maximum(gammak - 1, 0)
ksi = np.maximum(ksi_min, ksi)
log_sigma_k = gammak * ksi/(1 + ksi) - np.log(1 + ksi)
vad_decision = np.sum(log_sigma_k) / p.window_size
if vad_decision < eta:
noise_mu2 = mu * noise_mu2 + (1 - mu) * sig2
a = ksi / (1 + ksi)
vk = a * gammak
ei_vk = 0.5 * expn(1, np.maximum(vk, 1e-8))
hw = a * np.exp(ei_vk)
sig = sig * hw
xk_prev = sig ** 2
xi_w = np.fft.ifft(hw * spec, p.n_fft, axis=0)
xi_w = np.real(xi_w)
x_final[k:k + p.len2] = x_old + xi_w[0:p.len1]
x_old = xi_w[p.len1:p.window_size]
output = from_float(x_final, dtype)
output = np.pad(output, (0, len(wav) - len(output)), mode="constant")
return output
## Alternative VAD algorithm to webrctvad. It has the advantage of not requiring to install that
## darn package and it also works for any sampling rate. Maybe I'll eventually use it instead of
## webrctvad
# def vad(wav, sampling_rate, eta=0.15, window_size=0):
# """
# TODO: fix doc
# Creates a profile of the noise in a given waveform.
#
# :param wav: a waveform containing noise ONLY, as a numpy array of floats or ints.
# :param sampling_rate: the sampling rate of the audio
# :param window_size: the size of the window the logmmse algorithm operates on. A default value
# will be picked if left as 0.
# :param eta: voice threshold for noise update. While the voice activation detection value is
# below this threshold, the noise profile will be continuously updated throughout the audio.
# Set to 0 to disable updating the noise profile.
# """
# wav, dtype = to_float(wav)
# wav += np.finfo(np.float64).eps
#
# if window_size == 0:
# window_size = int(math.floor(0.02 * sampling_rate))
#
# if window_size % 2 == 1:
# window_size = window_size + 1
#
# perc = 50
# len1 = int(math.floor(window_size * perc / 100))
# len2 = int(window_size - len1)
#
# win = np.hanning(window_size)
# win = win * len2 / np.sum(win)
# n_fft = 2 * window_size
#
# wav_mean = np.zeros(n_fft)
# n_frames = len(wav) // window_size
# for j in range(0, window_size * n_frames, window_size):
# wav_mean += np.absolute(np.fft.fft(win * wav[j:j + window_size], n_fft, axis=0))
# noise_mu2 = (wav_mean / n_frames) ** 2
#
# wav, dtype = to_float(wav)
# wav += np.finfo(np.float64).eps
#
# nframes = int(math.floor(len(wav) / len2) - math.floor(window_size / len2))
# vad = np.zeros(nframes * len2, dtype=np.bool)
#
# aa = 0.98
# mu = 0.98
# ksi_min = 10 ** (-25 / 10)
#
# xk_prev = np.zeros(len1)
# noise_mu2 = noise_mu2
# for k in range(0, nframes * len2, len2):
# insign = win * wav[k:k + window_size]
#
# spec = np.fft.fft(insign, n_fft, axis=0)
# sig = np.absolute(spec)
# sig2 = sig ** 2
#
# gammak = np.minimum(sig2 / noise_mu2, 40)
#
# if xk_prev.all() == 0:
# ksi = aa + (1 - aa) * np.maximum(gammak - 1, 0)
# else:
# ksi = aa * xk_prev / noise_mu2 + (1 - aa) * np.maximum(gammak - 1, 0)
# ksi = np.maximum(ksi_min, ksi)
#
# log_sigma_k = gammak * ksi / (1 + ksi) - np.log(1 + ksi)
# vad_decision = np.sum(log_sigma_k) / window_size
# if vad_decision < eta:
# noise_mu2 = mu * noise_mu2 + (1 - mu) * sig2
# print(vad_decision)
#
# a = ksi / (1 + ksi)
# vk = a * gammak
# ei_vk = 0.5 * expn(1, np.maximum(vk, 1e-8))
# hw = a * np.exp(ei_vk)
# sig = sig * hw
# xk_prev = sig ** 2
#
# vad[k:k + len2] = vad_decision >= eta
#
# vad = np.pad(vad, (0, len(wav) - len(vad)), mode="constant")
# return vad
def to_float(_input):
if _input.dtype == np.float64:
return _input, _input.dtype
elif _input.dtype == np.float32:
return _input.astype(np.float64), _input.dtype
elif _input.dtype == np.uint8:
return (_input - 128) / 128., _input.dtype
elif _input.dtype == np.int16:
return _input / 32768., _input.dtype
elif _input.dtype == np.int32:
return _input / 2147483648., _input.dtype
raise ValueError('Unsupported wave file format')
def from_float(_input, dtype):
if dtype == np.float64:
return _input, np.float64
elif dtype == np.float32:
return _input.astype(np.float32)
elif dtype == np.uint8:
return ((_input * 128) + 128).astype(np.uint8)
elif dtype == np.int16:
return (_input * 32768).astype(np.int16)
elif dtype == np.int32:
print(_input)
return (_input * 2147483648).astype(np.int32)
raise ValueError('Unsupported wave file format')