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main_NMF.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 28 11:12:48 2018
@author: eesungkim
"""
import librosa
import numpy as np
import scipy.io.wavfile as wav
import matplotlib.pyplot as plt
import librosa.display
import tensorflow as tf
import os
import librosa
import argparse
from utils.utils import *
def makedirs(path):
if not os.path.exists(path):
print(" [*] Make directories : {}".format(path))
os.makedirs(path)
def NMF(args):
# PATH_MATLAB='"C:/Program Files/MATLAB/R2014a/bin/matlab.exe"'
PATH_ROOT = os.getcwd()
# PATH_MATLAB1 = os.path.join(PATH_ROOT , 'PESQ_MATLAB/execute_pesq.m')
os.chdir(PATH_ROOT)
path_clean_train = os.path.join(PATH_ROOT , args.input_clean_train)
path_clean_test = os.path.join(PATH_ROOT , args.input_clean_test)
path_noisy_test = os.path.join(PATH_ROOT , args.input_noisy_test)
path_noise_1 = os.path.join(PATH_ROOT , args.input_noise_1)
path_noise_2 = os.path.join(PATH_ROOT , args.input_noise_2)
path_noise_3 = os.path.join(PATH_ROOT , args.input_noise_3)
output_path_estimated_noisy_test = os.path.join(PATH_ROOT , args.output_file)
makedirs(os.path.dirname(output_path_estimated_noisy_test))
(sr, clean_train) = wav.read(path_clean_train)
(sr, clean_test) = wav.read(path_clean_test)
(sr, noisy_test) = wav.read(path_noisy_test)
(sr, noise_1) = wav.read(path_noise_1)
(sr, noise_2) = wav.read(path_noise_2)
(sr, noise_3) = wav.read(path_noise_3)
clean_train = clean_train.astype('float')
noisy_test = noisy_test.astype('float')
noise_1 = noise_1.astype('float')
noise_2 = noise_2.astype('float')
noise_3 = noise_3.astype('float')
# NMF training stage
#####################################################################################
#clean_train
stft_clean_train = librosa.stft(clean_train, n_fft=args.num_FFT, hop_length=args.hop_size, window=args.window)
stft_clean_train = stft_clean_train[:,:10000]
magnitude_clean_train, phase_clean_train = divide_magphase(stft_clean_train, power=1)
#obtain the basis matrix of clean_speech
W_clean_train, H_clean_train = NMF_MuR(magnitude_clean_train,args.r,args.max_iter,args.display_step,const_W=False,init_W=0)
# noise
##########################################################
# 1) make 40 bases with total 9000 frames ; 3000 frames for each noise
# # three noises
stft_noise_1 = librosa.stft(noise_1, n_fft=args.num_FFT, hop_length=args.hop_size, window=args.window)
stft_noise_2 = librosa.stft(noise_2, n_fft=args.num_FFT, hop_length=args.hop_size, window=args.window)
stft_noise_3 = librosa.stft(noise_3, n_fft=args.num_FFT, hop_length=args.hop_size, window=args.window)
magnitude_noise_1, _ = divide_magphase(stft_noise_1, power=1)
magnitude_noise_2, _ = divide_magphase(stft_noise_2, power=1)
magnitude_noise_3, _ = divide_magphase(stft_noise_3, power=1)
magnitude_noise_1, magnitude_noise_2, magnitude_noise_3= magnitude_noise_1[:,:3000],magnitude_noise_2[:,:3000],magnitude_noise_3[:,:3000]
nmf_magnitude_noise = np.concatenate((magnitude_noise_1, magnitude_noise_2, magnitude_noise_3),axis=1)
#obtain the basis matrix of noise
W_noise, H_noise = NMF_MuR(nmf_magnitude_noise,args.r,args.max_iter,args.display_step,const_W=False,init_W=0)
# 2) concat 13, 13, 14 bases for each noise
# stft_noise_1 = librosa.stft(noise_1, n_fft=args.num_FFT, hop_length=args.hop_size, window=args.window)
# stft_noise_2 = librosa.stft(noise_2, n_fft=args.num_FFT, hop_length=args.hop_size, window=args.window)
# stft_noise_3 = librosa.stft(noise_3, n_fft=args.num_FFT, hop_length=args.hop_size, window=args.window)
# stft_noise_1 = stft_noise_1[:,:9000]
# stft_noise_2 = stft_noise_2[:,:9000]
# stft_noise_3 = stft_noise_3[:,:9000]
# magnitude_noise_1, _ = divide_magphase(stft_noise_1, power=1)
# magnitude_noise_2, _ = divide_magphase(stft_noise_2, power=1)
# magnitude_noise_3, _ = divide_magphase(stft_noise_3, power=1)
#
# #obtain the basis matrix of noise
# W_noise_1, _ = NMF_MuR(magnitude_noise_1,13,args.max_iter,args.display_step,const_W=False,init_W=0)
# W_noise_2, _ = NMF_MuR(magnitude_noise_2, 13, args.max_iter, args.display_step, const_W=False, init_W=0)
# W_noise_3, _ = NMF_MuR(magnitude_noise_3, 14, args.max_iter, args.display_step, const_W=False, init_W=0)
# ####################################################
# W_noise = np.concatenate((W_noise_1, W_noise_2, W_noise_3),axis=1)
#noisy
stft_noisy_test = librosa.stft(noisy_test, n_fft=args.num_FFT, hop_length=args.hop_size, window=args.window)
magnitude_noisy_test, phase_noisy_test = divide_magphase(stft_noisy_test, power=1)
#####################################################################################
W_noisy = np.concatenate([W_clean_train,W_noise], axis=1)
_,H_reconstructed_noisy = NMF_MuR(magnitude_noisy_test,2*args.r,args.max_iter,args.display_step,const_W=True, init_W=W_noisy)
H_reconstructed_clean = H_reconstructed_noisy[:args.r,:]
H_reconstructed_noise = H_reconstructed_noisy[args.r:,:]
magnitude_reconstructed_clean=np.matmul(W_clean_train,H_reconstructed_clean)
magnitude_reconstructed_noise = np.matmul(W_noise,H_reconstructed_noise)
#Gain function similar to wiener filter to enhance the speech signal
wiener_gain = np.power(magnitude_reconstructed_clean,args.p) / (np.power(magnitude_reconstructed_clean,args.p) + np.power(magnitude_reconstructed_noise, args.p))
magnitude_estimated_clean = wiener_gain * magnitude_noisy_test
#Reconstruct
stft_reconstructed_clean = merge_magphase(magnitude_estimated_clean, phase_noisy_test)
signal_reconstructed_clean =librosa.istft(stft_reconstructed_clean, hop_length=args.hop_size, window=args.window)
signal_reconstructed_clean = signal_reconstructed_clean.astype('int16')
wav.write(output_path_estimated_noisy_test,sr,signal_reconstructed_clean)
# # =============================================================================
# # PESQ
# # =============================================================================
# from pymatbridge import Matlab
# mlab = Matlab()
# mlab = Matlab(executable=PATH_MATLAB)
# mlab.start()
# #PATH_MATLAB1 = os.path.join(PATH_ROOT , "PESQ_MATLAB","execute_pesq.m")
# result_PESQ = mlab.run_func(PATH_MATLAB1, {'arg1': sr})
# noisy_original_PESQ = result_PESQ['result'][0][0]
# enhanced_PESQ = result_PESQ['result'][1][0]
# mlab.stop()
# snr=args.input_noisy_test
# name=snr[53:-9]
# print("[%s]\n Original: %.2f\n NMF\t: %.2f"%(name,noisy_original_PESQ,enhanced_PESQ))
#
# # print('Noisy STOI: %.6f' % calc_stoi(clean_test / norm(clean_test), noisy_test / norm(noisy_test), sr))
# # print('NMF STOI: %.6f' % calc_stoi(clean_test / norm(clean_test), signal_reconstructed_clean / norm(signal_reconstructed_clean), sr))
def parse_args():
parser = argparse.ArgumentParser(description='NMF Speech Enhancement')
parser.add_argument('--datasets_dir', type=str, default='datasets/')
parser.add_argument('--input_clean_train', type=str, default='datasets/timit_clean_selected_train_total.wav')
parser.add_argument('--input_clean_test', type=str, default='datasets/timit_clean_selected/timit_clean_selected_test.wav')
parser.add_argument('--input_noisy_test', type=str, default='datasets/timit_noisy_selected/test_match/timit_noisy_babble_snr10_test.wav')
parser.add_argument('--input_noise_1', type=str, default='datasets/noise/NOISEX/babble.wav')
parser.add_argument('--input_noise_2', type=str, default='datasets/noise/NOISEX/factory1.wav')
parser.add_argument('--input_noise_3', type=str, default='datasets/noise/NOISEX/machinegun.wav')
parser.add_argument('--output_file', type=str, default='datasets/output/estimated_clean_NMF.wav')
parser.add_argument('--frame_length', type=int, default='512', help='')
parser.add_argument('--num_FFT', type=int, default='512', help='')
parser.add_argument('--hop_size', type=int, default='128', help='')
parser.add_argument('--window', type=str, default='hamming',help='boxcar, triang, blackman, hamming, hann, bartlett, flattop, parzen, bohman, blackmanharris, nuttall, barthann, kaiser')
parser.add_argument('--r', type=int, default='40', help='number of basis in NMF')
parser.add_argument('--max_iter', type=int, default='50', help='number of maximum of NMF iteration')
parser.add_argument('--display_step', type=int, default='10', help='display step in NMF interation')
parser.add_argument('--p', type=int, default='2', help='parameter in wiener filter for gain')
return check_args(parser.parse_args())
def check_args(args):
if not os.path.exists(args.datasets_dir):
os.makedirs(args.datasets_dir)
assert args.num_FFT >= 1, 'number of FFT size must be larger than or equal to one'
assert args.hop_size < args.num_FFT, 'hop size must be smaller than number of FFT size'
return args
if __name__ == '__main__':
args = parse_args()
NMF(args)