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BPF_analysis1.py
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#coding:utf-8
# BPF bank analysis Spectrogram
#
# feature
# BPF's target response is 2nd harmonic level less than -70dB
# Mel-frequency division
# Half-wave rectification until a few KHz signal or DC with ripple signal
# Down sampling to decrease temporal resolution
# N-th root compression
# normalized Gray scale image output
import sys
import argparse
from scipy import signal
from scipy.io.wavfile import read as wavread
from matplotlib import pyplot as plt
from mel import *
from BPF4 import *
from Compressor1 import *
# Check version
# Python 3.6.4 on win32 (Windows 10)
# numpy 1.18.4
# matplotlib 3.3.1
# scipy 1.4.1
class Class_Analysis1(object):
def __init__(self, num_band=1024, fmin=40, fmax=8000, sr=44100, Q=40.0, \
moving_average_factor=50, down_sample_factor=10, \
power_index=1/3.5):
# instance
# (1) mel frequency list
self.num_band=num_band
self.fmin=fmin
self.fmax=fmax
self.mel=Class_mel(self.num_band, self.fmin, self.fmax)
# (2) BPF bank
self.sr= sr
self.Q= Q
self.maf= int(moving_average_factor)
self.dsf= int(down_sample_factor)
self.BPF_list=[]
for flist0 in self.mel.flist:
bpf=Class_BPFtwice(fc=flist0, Q=self.Q, sampling_rate=self.sr, moving_average_factor=self.maf, down_sample_factor=self.dsf)
self.BPF_list.append(bpf)
# (3) compress via power function
self.power_index= power_index
self.comp1= Class_Compressor1(power_index= self.power_index)
def compute(self, yg):
# yg should be mono
self.dwn_len= int(len(yg)/self.dsf)
self.out1= np.empty( ( self.num_band, self.dwn_len), dtype=np.float32 )
for i, bpf in enumerate( self.BPF_list ):
print ('\r fc', bpf.fc, end='')
self.out1[i]=self.comp1(bpf.filtering2( yg, self.dwn_len))
print ('self.out1.shape', self.out1.shape)
print ('max', np.amax(self.out1), ' min', np.amin(self.out1))
return self.out1
def trans_gray(self, indata0 ):
# in_data0 dimension should be 2 zi-gen
# convert to single Gray scale
f= np.clip( indata0, 0.0, None) # clip to >= 0
# Normalize to [0, 255]
f= f / np.amax(f) # normalize as max is 1.0
fig_unit = np.uint8(np.around( f * 255))
return fig_unit
def conv_gray2RGBgray(self, in_fig ):
# convert single Gray scale to RGB gray
rgb_fig= np.zeros( (in_fig.shape[0],in_fig.shape[1], 3) )
for i in range(3):
rgb_fig[:,:,i] = 255 - in_fig
return rgb_fig
def conv_int255(self, in_fig):
# matplotllib imshow x format was changed from version 2.x to version 3.x
if 1: # matplotlib > 3.x
return np.array(np.abs(in_fig - 255), np.int)
else: # matplotlib = 2.x
return in_fig
def plot_image(self, yg=None):
#
fig_image= self.conv_gray2RGBgray( self.trans_gray(self.out1))
#
if yg is not None:
fig, [ax0, ax] = plt.subplots(2, 1)
ax0.plot(yg)
ax0.set_xlim(0, len(yg))
else:
fig, ax = plt.subplots()
ax.set_title('BPF bank analysis Spectrogram')
ax.set_xlabel('time step [sec]')
ax.set_ylabel('frequecny [Hz]')
# draw time value
xlen=fig_image.shape[1]
slen=xlen / ( self.sr/ self.dsf)
char_slen=str( int(slen*1000) / 1000) # ms
char_slen2=str( int((slen/2)*1000) / 1000) # ms
ax.set_xticks([0,int(xlen/2)-1, xlen-1])
ax.set_xticklabels(['0', char_slen2, char_slen])
# draw frequecny value
ylen=fig_image.shape[0]
flens=[self.fmin, 100, 200, 300, 500,700, 1000,1500, 2000, 2500, 3000, 3500, 4000, 5000,6000, self.fmax]
#flens=[self.fmin, 300, 1000, 3000, self.fmax]
# flens=[self.fmin, 300, 600, 1000, 1400, 2000, 3000, self.fmax] # forMix_400Hz1KHz-10dB_44100Hz_400msec_TwoTube_mono.wav
yflens,char_flens= self.mel.get_postion( flens)
ax.set_yticks( yflens )
ax.set_yticklabels( char_flens)
ax.imshow( self.conv_int255(fig_image), aspect='auto', origin='lower')
plt.tight_layout()
plt.show()
def load_wav( path0):
# return
# yg: wav data (mono)
# sr: sampling rate
try:
sr, y = wavread(path0)
except:
print ('error: wavread ', path0)
sys.exit()
else:
yg= y / (2 ** 15)
if yg.ndim == 2: # if stereo
yg= np.average(yg, axis=1)
print ('file ', path0)
print ('sampling rate ', sr)
print ('length ', len(yg))
return yg,sr
if __name__ == '__main__':
#
parser = argparse.ArgumentParser(description='BPF bank analysis Spectrogram')
parser.add_argument('--wav_file', '-w', default='wav/1KHz-10dB_44100Hz_400ms-TwoTube_stereo.wav', help='wav file name(16bit)')
args = parser.parse_args()
path0= args.wav_file
# overwrite wav file name
#path0='wav/Mix_400Hz1KHz-10dB_44100Hz_400msec_MONO.wav'
#path0='wav/400Hz-10dB_44100Hz_400msec.wav'
#path0='wav/1KHz-10dB_44100Hz_400msec.wav'
#path0='wav/3KHz-10dB_44100Hz_400msec.wav'
#path0='wav/5KHz-10dB_44100Hz_400msec.wav'
#path0='wav/2KHz-80dB_44100Hz_400msec.wav'
#path0='wav/1KHz-10dB_44100Hz_400ms-TwoTube_stereo.wav'
#path0='wav/Mix_400Hz1KHz-10dB_44100Hz_400msec_TwoTube_mono.wav'
yg,sr=load_wav( path0)
# instance
Ana1= Class_Analysis1(num_band=1024, fmin=40, fmax=8000, sr=sr)
# process
yo= Ana1.compute(yg)
# draw image
Ana1.plot_image()
#Ana1.plot_image(yg)