def loop(W, Xpos, Xneg):
n = W.size
X = np.arange(n)
#############
Xpos, Xneg = se.get_frontiers(W)
Xpos = hp.refine_frontier_iter(Xpos, W)
Xneg = hp.refine_frontier_iter(Xneg, W)
#############
pcaverage = average_pc_waveform(Xpos, Xneg, W)
# Ap = hp.approximate_pseudocycles_average(pcaverage)
# Wp, dWp = hp.parametric_W_wtow(Xpos, Ap, n)
pcx = interpolate.interp1d(np.linspace(0, 1, pcaverage.size), pcaverage,
"cubic")
Wp = np.zeros(n)
for i in range(Xpos.size - 1):
x0 = Xpos[i]
x1 = Xpos[i + 1]
Wp[x0:x1] = pcx(np.linspace(0, 1, x1 - x0))
dWp = np.zeros(n)
dWp[:-1] = Wp[1:] - Wp[:-1]
Xf = np.unique(np.hstack([Xpos, Xneg]))
Ix = hp.split_raw_frontier(Xf, W, 10)
A = hp.constrained_least_squares_arbitrary_intervals_wtow(
Wp, dWp, W, Xf[Ix].tolist(), 2)
We = hp.coefs_to_array_arbitrary_intervals_wtow(A, Wp, Xf[Ix].tolist(), n)
residue = W - We
env = hp.coefs_to_array_arbitrary_intervals(A, X, Xf[Ix].tolist(), n)
# Ws = hp.parametric_W(hp.linearize_pc(Xpos), Ap, n, True)
Xposl = hp.linearize_pc(Xpos)
Ws = np.zeros(n)
for i in range(1, Xposl.size - 1):
# print(i)
x0 = Xposl[i]
x1 = Xposl[i + 1]
Ws[x0:x1] = pcx(np.linspace(0, 1, x1 - x0))
return We, Ws * env, residue
pos_avgpc = np.average(np.array(pos_norm_pcs), 0)
return pos_avgpc, pos_orig_pcs, pos_norm_pcs
'''==============='''
''' Read wav file '''
'''==============='''
name = "alto"
W, fps = se.read_wav(f"Samples/{name}.wav")
W = W - np.average(W)
amp = np.max(np.abs(W))
n = W.size
X = np.arange(n)
Xpos_orig, Xneg_orig = se.get_frontiers(W)
Xpos = hp.refine_frontier_iter(Xpos_orig, W)
Xneg = hp.refine_frontier_iter(Xneg_orig, W)
siz = min(Xpos.size, Xneg.size)
Average_ref_X = (Xpos[:siz] + Xneg[:siz]) / 2
Average_ref_X_smooth = np.round(savgol_filter(Average_ref_X, 51,
3)).astype(np.int)
Average_ref_X_smooth = np.unique(np.abs(Average_ref_X_smooth))
Average_ref_X_smooth_linear = hp.linearize_pc_approx(Average_ref_X_smooth)
pos_avgpc, pos_orig_pcs, pos_norm_pcs = average_pc_waveform(
Average_ref_X_smooth, W)
pcx = interpolate.interp1d(np.linspace(0, 1, pos_avgpc.size), pos_avgpc,
import numpy as np
import signal_envelope as se
import hp
from statistics import mode
'''==============='''
''' Read wav file '''
'''==============='''
name = "piano33"
W, fps = se.read_wav(f"Samples/{name}.wav")
W = W - np.average(W)
amp = np.max(np.abs(W))
n = W.size
X = np.arange(n)
Xpos, Xneg = se.get_frontiers(W)
Xpos = hp.refine_frontier_iter(Xpos, W)
Xneg = hp.refine_frontier_iter(Xneg, W)
T = []
for i in range(1, Xpos.size):
T.append(Xpos[i] - Xpos[i - 1])
for i in range(1, Xneg.size):
T.append(Xneg[i] - Xneg[i - 1])
T = np.array(T, dtype=np.int)
maxT = np.max(T)
modeT = mode(T)
Xf = np.sort(np.hstack([Xpos, Xneg]))
Ix = hp.split_raw_frontier(Xf, W, 2)
import sys
# sys.path.append("signal_envelope/")
import numpy as np
import signal_envelope as se
from scipy import interpolate
import plotly.graph_objects as go
name = "alto"
# name="bend"
# name="spoken_voice"
W, _ = se.read_wav(f"test_samples/{name}.wav")
amp = np.max(np.abs(W))
W = 4 * W / amp
X = np.arange(W.size)
Xpos, Xneg = se.get_frontiers(W, 0)
E = se.get_frontiers(W, 1)
f = interpolate.interp1d(E, np.abs(W[E]), kind="linear", fill_value="extrapolate")
E = f(X)
for i in range(E.size):
if E[i] < 0.1:
E[i] = 0.1
C = W / E
'''============================================================================'''
''' PLOT '''
'''============================================================================'''
def plot_envelope(self,
TRAI,
COLOR,
features_path,
valid=False,
method='hl',
xlog=False):
fig = plt.figure(figsize=[6, 3.9])
fig.text(0.95,
0.17,
self.status,
fontdict={
'family': 'Arial',
'fontweight': 'bold',
'fontsize': 12
},
horizontalalignment="right")
ax = plt.subplot()
for idx, [trai, color] in enumerate(zip(TRAI, COLOR)):
XX, YY = [], []
for k in tqdm(trai):
tmp = self.data_tra[k - 1]
time, sig = self.cal_wave(tmp, valid=valid)
if time[-1] < 50:
continue
if method == 'se':
sig = (sig / max(sig))
X_pos_frontier, X_neg_frontier = se.get_frontiers(sig, 0)
XX.extend(np.linspace(0, time[-1],
len(X_pos_frontier) - 2))
YY.extend(sig[X_pos_frontier[2:]]**2)
if not xlog:
ax.semilogy(np.linspace(0, time[-1],
len(X_pos_frontier) - 2),
sig[X_pos_frontier[2:]]**2,
'.',
Marker='.',
color=color)
else:
ax.loglog(np.linspace(0, time[-1],
len(X_pos_frontier) - 2),
sig[X_pos_frontier[2:]]**2,
'.',
Marker='.',
color=color)
else:
sig = sig**2 / max(sig**2)
high_idx, low_idx = hl_envelopes_idx(sig, dmin=60, dmax=60)
XX.extend(time[low_idx])
YY.extend(sig[low_idx])
if not xlog:
ax.semilogy(time[low_idx],
sig[low_idx],
'.',
Marker='.',
color=color)
else:
ax.loglog(time[low_idx],
sig[low_idx],
'.',
Marker='.',
color=color)
with open(
'%s_Decay Function_Pop %d.txt' %
(features_path[:-4], idx + 1), 'w') as f:
f.write('Time (μs), Normalized A$^2$\n')
for j in range(len(XX)):
f.write('{}, {}\n'.format(XX[j], YY[j]))
plot_norm(ax, 'Time (μs)', 'Normalized A$^2$', legend=False)
# name = "soprano"
'''==============='''
''' Read wav file '''
'''==============='''
W, fps = se.read_wav(f"test_samples/{name}.wav")
W = W - np.average(W)
amplitude = np.max(np.abs(W))
W = W / amplitude
n = W.size
print(f"n={n}")
X = np.arange(n)
'''==============='''
''' Present work '''
'''==============='''
start = timer()
Ex = se.get_frontiers(W, 1)
f = interp1d(Ex,
np.abs(W[Ex]),
kind="linear",
fill_value="extrapolate",
assume_sorted=False)
envY = f(X)
lms = np.average((np.abs(W) - envY * 0.5)**2)
print(f"Present work: lms ={lms}, time={timer() - start}")
'''==============='''
''' Smoothing '''
'''==============='''
start = timer()
envY_smooth = savgol_filter(np.abs(W), 3000 + 1, 3)
lms_smooth = np.average((np.abs(W) - envY_smooth * 0.5)**2)
print(f"Smoothing: lms ={lms_smooth}, time={timer() - start}")
