def test(filename=None):
import random, os
import matplotlib.pyplot as plt
from sys import argv
#signal, params = read_signal(sound,WINSIZE)
scenario = None
if filename != None:
scene = os.path.basename(filename)[0]
else:
filename = random.choice([
x for x in os.listdir("tmp/") if os.path.splitext(x)[1] == ".flac"
])
scene = filename[0]
filename = "tmp/" + filename
print(filename)
truths = vad.load_truths()
signal, rate = speech.read_soundfile(filename)
seconds = float(len(signal)) / rate
winsize = librosa.time_to_samples(float(WINMS) / 1000, rate)[0]
window = sp.hanning(winsize)
ltsd = LTSD(winsize, window, 5)
res, threshold, nstart, nend = ltsd.compute(signal)
segments = ltsd.segments(res, threshold)
#print(float(len(signal))/rate, librosa.core.frames_to_time(len(res), 8000, winsize/2))
segments = librosa.core.frames_to_time(segments, rate, winsize / 2)
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.plot((signal/np.max(signal))*np.mean(res)+np.mean(res))
ax.plot(np.linspace(0, seconds, len(res)), res)
ax.plot([0, seconds], [threshold, threshold])
vad.plot_segments(truths[scene]['combined'], segments, ax)
n1 = float(nstart) / rate
n2 = float(nend) / rate
ax.vlines([n1, n2], -20, 20)
plt.show()
def test(filename=None):
import random, os
import matplotlib.pyplot as plt
from sys import argv
#signal, params = read_signal(sound,WINSIZE)
scenario=None
if filename != None:
scene = os.path.basename(filename)[0]
else:
filename = random.choice([x for x in os.listdir("tmp/") if os.path.splitext(x)[1] == ".flac"])
scene = filename[0]
filename = "tmp/"+filename
print(filename)
truths = vad.load_truths()
signal,rate = speech.read_soundfile(filename)
seconds = float(len(signal))/rate
winsize = librosa.time_to_samples(float(WINMS)/1000, rate)[0]
window = sp.hanning(winsize)
ltsd = LTSD(winsize,window,5)
res, threshold,nstart,nend = ltsd.compute(signal)
segments = ltsd.segments(res, threshold)
#print(float(len(signal))/rate, librosa.core.frames_to_time(len(res), 8000, winsize/2))
segments = librosa.core.frames_to_time(segments, rate, winsize/2)
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.plot((signal/np.max(signal))*np.mean(res)+np.mean(res))
ax.plot(np.linspace(0,seconds, len(res)), res)
ax.plot([0, seconds], [threshold, threshold])
vad.plot_segments(truths[scene]['combined'], segments, ax)
n1 = float(nstart)/rate
n2 = float(nend)/rate
ax.vlines([n1,n2], -20,20)
plt.show()
def pipeline(path, frame_ms=64, hop_ms=64):
sig, rate = speech.read_soundfile(path)
fsize = librosa.time_to_samples(float(frame_ms)/1000, rate)[0]
hop = librosa.time_to_samples(float(hop_ms)/1000, rate)[0]
frames = librosa.util.frame(sig, fsize, hop)
rms = np.apply_along_axis(speech.rms, 0, frames)
H, p = spectral_entropy(frames, rate, fsize)
return sig, rate, frames, fsize, rms, H, p
def vad(soundfile, noisefile=None):
signal, rate = speech.read_soundfile(soundfile)
if noisefile != None:
noise, nrate = speech.read_soundfile(noisefile)
print("found noisefile: " + noisefile)
else:
noise = None
seconds = float(len(signal)) / rate
winsize = librosa.time_to_samples(float(WINMS) / 1000, rate)[0]
window = sp.hanning(winsize)
ltsd = LTSD(winsize, window, 5, init_noise=noise)
res, threshold, nstart, nend = ltsd.compute(signal)
segments, = ltsd.segments(res, threshold)
#print(float(len(signal))/rate, librosa.core.frames_to_time(len(res), 8000, winsize/2))
segments = librosa.core.samples_to_time(segments, rate).tolist()
indexes = []
for s in segments:
indexes += s
indexes.append(seconds)
return indexes
def vad(soundfile, noisefile=None):
signal,rate = speech.read_soundfile(soundfile)
if noisefile != None:
noise,nrate = speech.read_soundfile(noisefile)
print("found noisefile: "+noisefile)
else:
noise = None
seconds = float(len(signal))/rate
winsize = librosa.time_to_samples(float(WINMS)/1000, rate)[0]
window = sp.hanning(winsize)
ltsd = LTSD(winsize,window,5, init_noise=noise)
res, threshold,nstart,nend = ltsd.compute(signal)
segments, = ltsd.segments(res, threshold)
#print(float(len(signal))/rate, librosa.core.frames_to_time(len(res), 8000, winsize/2))
segments = librosa.core.samples_to_time(segments, rate).tolist()
indexes = []
for s in segments:
indexes += s
indexes.append(seconds)
return indexes
def pipeline(path,
frame_ms=30,
hop_ms=15,
filt=True,
noisy=True,
shift=True,
snr=60):
#sig, rate = librosa.load(path)
#sig2, rate2 = ad.read_file(path)
sig, rate = speech.read_soundfile(path)
#sig = signal.wiener(sig)
fsize = librosa.time_to_samples(float(frame_ms) / 1000, rate)[0]
hop = librosa.time_to_samples(float(hop_ms) / 1000, rate)[0]
if filt:
sig = bp_filter(sig, lowcut=120, highcut=1000)
if noisy:
sig = speech.add_noise(sig, "noise8k/white.flac", snr)
frames = librosa.util.frame(sig, fsize, hop)
w = signal.hann(fsize)
#frames_W = np.zeros_like(frames)
#print(frames.shape)
#frames = frames.T
#print(w.shape)
frames_w = np.apply_along_axis(lambda x, w: x * w, 0, frames, w)
frames = frames_w
frames = np.apply_along_axis(lambda x, w: x / (w + 1e-15), 0, frames, w)
# frames_W[i] = signal.convolve(frames[i],w, mode='same')
#frames = frames_W.T
#w = signal.correlate(w,w,mode='full')
#w = w[w.size/2:]
#print(frames.shape)
#frames = sigutil.enframe(sig, fsize, hop, signal.hann)
#print("normalized autocorrelation")
naccs = np.apply_along_axis(nacc, 0, frames)
#print("trimming")
naccs = np.apply_along_axis(trim_frame, 0, naccs)
lags = np.zeros(len(naccs.T))
acf_n = np.zeros(len(naccs.T))
for i in range(len(naccs.T)):
frame = naccs.T[i]
relmax = signal.argrelmax(frame)[0]
if len(relmax) > 0:
argmax2 = relmax[0] + np.argmax(frame[relmax[0]:])
else:
argmax2 = np.argmax(frame)
#print(relmax)
"""
if len(relmax)>=2:
#print(relmax[0], relmax[1], relmax[1]-relmax[0])
lags[i] = relmax[1]-relmax[0]
elif len(relmax) == 1:
lags[i] = relmax[0]
"""
lags[i] = argmax2
acf_n[i] = len(relmax)
#print(lags[i], len(relmax))
naccs.T[i] = np.roll(frame, -1 * argmax2)
#minacs = np.zeros_like(naccs)
#for i in range(len(naccs.T)):
# minacs[:,i] = min_ac(naccs.T, i)
meanacs = np.zeros_like(naccs)
for i in range(len(naccs.T)):
meanacs[:, i] = mean_ac(naccs.T, i)
#print(naccs.shape)
#print(meanacs.shape)
#print("lags")
#print("variances")
#acvars = np.apply_along_axis(acvar, 0, naccs2)
acvars = np.apply_along_axis(acvar, 0, meanacs)
#print("ltacs")
ltacs = np.zeros_like(acvars)
for i in range(len(acvars)):
ltacs[i] = ltac(acvars, i)
print("done: " + path)
return sig, rate, frames, fsize, meanacs, acvars, ltacs, (lags, acf_n)
def pipeline(path, frame_ms=30, hop_ms=15, filt=True, noisy=True, shift=True, snr=30):
#sig, rate = librosa.load(path)
#sig2, rate2 = ad.read_file(path)
sig, rate = speech.read_soundfile(path)
sig = signal.wiener(sig)
fsize = librosa.time_to_samples(float(frame_ms)/1000, rate)[0]
hop = librosa.time_to_samples(float(hop_ms)/1000, rate)[0]
if filt:
sig = bp_filter(sig)
if noisy:
sig = speech.add_noise(sig, "noise8k/white.flac", snr)
frames = librosa.util.frame(sig, fsize, hop)
w = signal.hann(fsize)
#frames_W = np.zeros_like(frames)
#print(frames.shape)
#frames = frames.T
#print(w.shape)
frames_w = np.apply_along_axis(lambda x,w: x*w, 0, frames, w)
frames = frames_w
frames = np.apply_along_axis(lambda x,w: x/(w+1e-15), 0, frames, w)
# frames_W[i] = signal.convolve(frames[i],w, mode='same')
#frames = frames_W.T
#w = signal.correlate(w,w,mode='full')
#w = w[w.size/2:]
#print(frames.shape)
#frames = sigutil.enframe(sig, fsize, hop, signal.hann)
#print("normalized autocorrelation")
naccs = np.apply_along_axis(nacc, 0, frames)
#print("trimming")
naccs = np.apply_along_axis(trim_frame, 0, naccs)
lags = np.zeros(len(naccs.T))
acf_n = np.zeros(len(naccs.T))
for i in range(len(naccs.T)):
frame = naccs.T[i]
relmax = signal.argrelmax(frame)[0]
if len(relmax)>0:
argmax2 = relmax[0] + np.argmax(frame[relmax[0]:])
else:
argmax2 = np.argmax(frame)
#print(relmax)
"""
if len(relmax)>=2:
#print(relmax[0], relmax[1], relmax[1]-relmax[0])
lags[i] = relmax[1]-relmax[0]
elif len(relmax) == 1:
lags[i] = relmax[0]
"""
lags[i] = argmax2
acf_n[i] = len(relmax)
#print(lags[i], len(relmax))
naccs.T[i] = np.roll(frame, -1*argmax2)
#minacs = np.zeros_like(naccs)
#for i in range(len(naccs.T)):
# minacs[:,i] = min_ac(naccs.T, i)
meanacs = np.zeros_like(naccs)
for i in range(len(naccs.T)):
meanacs[:,i] = mean_ac(naccs.T, i)
#print(naccs.shape)
#print(meanacs.shape)
#print("lags")
#print("variances")
#acvars = np.apply_along_axis(acvar, 0, naccs2)
acvars = np.apply_along_axis(acvar, 0, meanacs)
#print("ltacs")
ltacs = np.zeros_like(acvars)
for i in range(len(acvars)):
ltacs[i] = ltac(acvars, i)
print("done: "+path)
return sig, rate, frames, fsize, meanacs, acvars, ltacs, (lags, acf_n)