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MNSS_function_bk.py
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MNSS_function_bk.py
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__author__ = 'mac'
import librosa
import librosa.decompose
from matplotlib.colors import LogNorm
import scipy.io.wavfile
from pylab import *
from numpy import *
import numpy as np
import scipy.io.wavfile
import scipy.linalg
from scikits.samplerate import resample
import pickle
import math
from random import *
# from xp00_s import NN_judge
from NN_wrapper import NN_judge2
from random import random
'''
TF domain Music Noise Segmentation
'''
# Convert Sampling Rate
def wav_convert(data, SR, tar_freq):
# If the input signal is stereo, make it mono.
if ndim(data) == 2:
# Mix stereo signal into a mono signal
buff01 = 0.49 * (data[:, 0] + data[:, 1])
wave_ts = array(buff01)
else:
wave_ts = array(data[:])
wave_ts = array(wave_ts)
# Set a sampling rate
up_SR = 44100
# Compute a ratio to feed into resample function
ratio = float(float(tar_freq)/float(up_SR))
# Resample the file.
wave_ts = resample(wave_ts, ratio , 'linear')
# Transpose the data list.
wave_ts = transpose(wave_ts)
# Return wave_ts signal
return wave_ts
fileN = []
fileN_n = []
fileN_m = []
wav_folder = 'music_det'
sls = '/'
## Name of the instance
inst_name = 'mix_test_p10'
inst_name_n = 'mix_test_pminf'
inst_name_m = 'mix_test_pinf'
FLS_txt = 'file_list.txt'
total_path = wav_folder + sls + inst_name + sls + FLS_txt
total_path_n = wav_folder + sls + inst_name_n + sls + FLS_txt
total_path_m = wav_folder + sls + inst_name_m + sls + FLS_txt
## A loop for file list generation.
open_ct = 0
with open(total_path) as fp:
for line in fp:
open_ct += 1
fileN.append(line[:-1])
with open(total_path_n) as fp:
for line in fp:
open_ct += 1
fileN_n.append(line[:-1])
with open(total_path_m) as fp:
for line in fp:
open_ct += 1
fileN_m.append(line[:-1])
def MNSS_function(file_names):
##
SN = 7
fn_for = fileN[SN]
fn_for_n = fileN_n[SN]
fn_for_m = fileN_m[SN]
file_total = file_names[0]
file_total_n = file_names[1]
file_total_m = file_names[2]
# # Set files for input data.
# file_total = wav_folder + sls + inst_name + sls + fn_for
# file_total_n = wav_folder + sls + inst_name_n + sls + fn_for_n
# file_total_m = wav_folder + sls + inst_name_m + sls + fn_for_m
print "wav file path: ", file_total
print "wav file path_n: ", file_total_n
print "wav file path_m: ", file_total_m
# Use scipy.io library to read wave signal.
SR, data = scipy.io.wavfile.read(file_total)
SR, data_n = scipy.io.wavfile.read(file_total_n)
SR, data_m = scipy.io.wavfile.read(file_total_m)
tar_freq = 44100
data_ts = wav_convert(data, SR, tar_freq )
data_ts_n = wav_convert(data_n, SR, tar_freq )
data_ts_m = wav_convert(data_m, SR, tar_freq )
## The log2 of RQA plot image size
RP = 6 ## 32 by 32
img_W = 4
## Feature image size for ANALYZE
# Unit window size
img_sz_org = [(2**RP), img_W*(2**RP)]
# Total window size
img_sz = [4*(2**RP), img_W*(2**RP)]
## FFT specification
NFFT = 2*img_sz[0]
HOP_LENGTH = NFFT/4
## Set size of dimension
depth = 8
stride = img_sz_org[0]/4
x_stride = img_sz_org[0]/4
D_org = np.abs(librosa.stft(data_ts, n_fft = NFFT, hop_length = HOP_LENGTH))
# Show original spectrogram
figure()
img = librosa.display.specshow(D_org, sr=SR, cmap = 'jet', y_axis='linear', x_axis='time', norm=LogNorm(vmin=0.01, vmax=10))
subplot(depth ,1 ,1)
seg_sig = data_ts
D = np.abs(librosa.stft(seg_sig, n_fft = NFFT, hop_length = HOP_LENGTH))
D_n = np.abs(librosa.stft(data_ts_n, n_fft = NFFT, hop_length = HOP_LENGTH))
D_m = np.abs(librosa.stft(data_ts_m, n_fft = NFFT, hop_length = HOP_LENGTH))
time_length = int( (shape(D)[1] - img_sz_org[1]) /float(x_stride))
X_test = np.zeros((time_length*(depth+1), 1, img_sz_org[0], img_sz_org[1]), dtype="float32")
y_test = np.zeros((time_length*(depth+1), 1, 1), dtype="float32")
for r in range(0, time_length):
x_offset = r * x_stride
# print 'x_offset', x_offset
for k in range(0, depth+1):
offset = stride * k
x_start = x_offset + 0
x_end = x_offset + img_sz_org[1]
y_start = (offset + 0)
y_end = (offset + img_sz_org[0])# /float(SR)
Ds = D[ y_start : y_end, x_start : x_end ]
X_test[r*(depth+1)+k,0,:,:] = abs(Ds)
subplot(depth+1, 1, (depth+1)-k)
img = librosa.display.specshow(Ds, sr=SR, y_axis='linear', cmap = 'jet', x_axis='time', norm=LogNorm(vmin=0.01, vmax=10))
## Dicern whether the frames are noise or music
##################################
pr_val = NN_judge2(X_test, y_test)
##################################
pr_val_raw = (pr_val + 1e-10)
pr_val = log10(pr_val + 1e-10)
pr_val_box_music = pr_val[:,0].reshape((depth+1), time_length)
pr_val_box_noise = pr_val[:,1].reshape((depth+1), time_length)
pr_val_box_music_raw = pr_val_raw[:,0].reshape((depth+1), time_length)
pr_val_box_noise_raw = pr_val_raw[:,1].reshape((depth+1), time_length)
D_mask_m = 0*D[0:(img_sz_org[0]*3), 0:stride*time_length ]
D_mask_n = 0*D[0:(img_sz_org[0]*3), 0:stride*time_length ]
D_mask_mb = 0*D[0:(img_sz_org[0]*3), 0:stride*time_length ]
D_mask_nb = 0*D[0:(img_sz_org[0]*3), 0:stride*time_length ]
for r in range(0, time_length):
x_offset = r * x_stride
for k in range(0, depth+1):
offset = stride * k
x_start = x_offset + 0
x_end = x_offset + img_sz_org[1]
y_start = (offset + 0)
y_end = (offset + img_sz_org[0])# /float(SR)
D_mask_m[ y_start : y_end, x_start : x_end ] = \
D_mask_m[ y_start : y_end, x_start : x_end ] + pr_val_box_music[k, r]
D_mask_n[ y_start : y_end, x_start : x_end ] = \
D_mask_n[ y_start : y_end, x_start : x_end ] + pr_val_box_noise[k, r]
D_mask_mb[ y_start : y_end, x_start : x_end ] = \
D_mask_m[ y_start : y_end, x_start : x_end ] + pr_val_box_music_raw[k, r]
D_mask_nb[ y_start : y_end, x_start : x_end ] = \
D_mask_n[ y_start : y_end, x_start : x_end ] + pr_val_box_noise_raw[k, r]
for r in range(0, time_length):
x_offset = r * x_stride
for k in range(0, depth+4):
offset = stride * k
x_start = x_offset + 0
x_end = x_offset + img_sz_org[0]/4
y_start = (offset + 0)
y_end = (offset + img_sz_org[0]/4)# /float(SR)
k_com_L = 4/float(amin([k+1, 4]))
k_com_H = 4/float(amin([(depth+4-k), 4]))
D_mask_m[ y_start : y_end, x_start : x_end ] = \
D_mask_m[ y_start : y_end, x_start : x_end ]*float(float(k_com_L)*float(k_com_H))
D_mask_n[ y_start : y_end, x_start : x_end ] = \
D_mask_n[ y_start : y_end, x_start : x_end ]*float(float(k_com_L)*float(k_com_H))
D_show = D[0:(img_sz_org[0]*3), 0:stride*time_length ]
D_show_n = D_n[0:(img_sz_org[0]*3), 0:stride*time_length ]
D_show_m = D_m[0:(img_sz_org[0]*3), 0:stride*time_length ]
D_show = flipud(D_show)
D_show_n = flipud(D_show_n)
D_show_m = flipud(D_show_m)
D_mask_dn = flipud(D_mask_n)
D_mask_dm = flipud(D_mask_m)
D_music_m = (D_mask_dm - D_mask_dn)
D_soft_m = D_music_m
D_music_m[D_music_m>0]=1
D_music_m[D_music_m<=0]=0
D_noise_n = (D_mask_dn - D_mask_dm)
D_soft_n = D_noise_n
D_noise_n[D_noise_n>0]=1
D_noise_n[D_noise_n<=0]=0
D_noise_n = (D_mask_dn - D_mask_dm)
total_plot = 7
with open( 'pickle_folder/' + 'MNN_visual_data_'+ str(SN) + '.pickle', 'w') as f:
pickle.dump([D_show, D_show_n, D_show_m, D_mask_dn, D_mask_dm, D_music_m, D_noise_n], f)
subplot(total_plot, 1, 1)
img = librosa.display.specshow(flipud(D_show_n), cmap = 'jet', y_axis='linear', norm=LogNorm(vmin=0.01, vmax=10))
subplot(total_plot, 1, 2)
img = librosa.display.specshow(flipud(D_show_m), cmap = 'jet', y_axis='linear', norm=LogNorm(vmin=0.01, vmax=10))
subplot(total_plot, 1, 3)
img = librosa.display.specshow(flipud(D_show), cmap = 'jet', y_axis='linear', norm=LogNorm(vmin=0.01, vmax=10))
subplot(total_plot, 1, 4)
imshow(D_mask_dn, interpolation = 'none', cmap = 'jet' )
subplot(total_plot, 1, 5)
imshow(D_mask_dm, interpolation = 'none', cmap = 'jet' )
subplot(total_plot, 1, 6)
imshow(log10(np.array(D_show)*np.array(D_noise_n)), interpolation = 'none', cmap = 'jet' )
subplot(total_plot, 1, 7)
imshow(log10(np.array(D_show)*np.array(D_music_m)), interpolation = 'none', cmap = 'jet' )
show()
# with open(pickle_path + open_data + '.pickle') as f:
# sum_mat_X_data, sum_mat_y_data, sum_mat_k_data, sum_mat_D_data, jump_num, FN, nb_classes = pickle.load(f)
seg_sig = data_ts
D_out = librosa.stft(seg_sig, n_fft = NFFT, hop_length = HOP_LENGTH)
shape_Dout = shape(D_out)
D_wr = 0 * D_music_m
shape_D = shape(D_music_m)
D_wr = zeros((shape_D[0],shape_D[1]), dtype=complex)
D_wr[0:shape_D[0], 0:shape_D[1] ] = D_out[ 0:shape_D[0], 0:shape_D[1] ]
D_n_out = np.array(D_wr)*np.array(D_noise_n)
D_m_out = np.array(D_wr)*np.array(D_music_m)
D_yn = zeros((shape_Dout[0],shape_D[1]), dtype=complex)
D_ym = zeros((shape_Dout[0],shape_D[1]), dtype=complex)
D_yn[0:shape_D[0], 0:shape_D[1] ] = D_n_out
D_ym[0:shape_D[0], 0:shape_D[1] ] = D_m_out
y_noise = librosa.istft(D_n_out)
y_music = librosa.istft(D_m_out)
file_total_wr = wav_folder
print 'SR', SR
print 'tar_freq', tar_freq
SR_w = 33000
scipy.io.wavfile.write(file_total_wr + sls + 'y_music.wav', SR_w, y_music)
scipy.io.wavfile.write(file_total_wr + sls + 'y_noise.wav', SR_w, y_noise)
show()
print 'max and min of pvb music', amax(pr_val_box_music), amin(pr_val_box_music)
print 'max and min of pvb noise', amax(pr_val_box_noise), amin(pr_val_box_noise)
print 'max and min of pr_val', amax(pr_val), amin(pr_val)
print 'shape of pr_val_box_music ', shape(pr_val_box_music)