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utils/logmmse.py

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+# 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')