class ActivityDetection:
def __init__(self):
self.initted = False
#self.nr = NoiseReduction()
self.ltsd = LTSD_VAD()
def init_noise(self, fs, signal):
self.initted = True
#self.nr.init_noise(fs, signal)
self.ltsd.init_params_by_noise(fs, signal)
#nred = self.nr.filter(fs, signal)
#self.ltsd.init_params_by_noise(fs, nred)
def filter(self, fs, signal):
if not self.initted:
raise "NoiseFilter Not Initialized"
# nred = self.nr.filter(fs, signal)
# removed = remove_silence(fs, nred)
# self.ltsd.plot_ltsd(fs, nred)
orig_len = len(signal)
filtered, intervals = self.ltsd.filter(signal)
#print 'signal lengths', len(filtered), orig_len
if len(filtered) > orig_len / 3:
return filtered
return np.array([])
def remove_silence(self,fs, signal, frame_duration = 0.02, frame_shift = 0.01, perc = 0.15):
orig_dtype = type(signal[0])
siglen = len(signal)
retsig = np.zeros(siglen, dtype = np.int64)
frame_length = int(frame_duration * fs)
frame_shift_length = int(frame_shift * fs)
new_siglen = 0
i = 0
average_energy = np.sum(signal ** 2) / float(siglen)
#print "Avg Energy of signal: ", average_energy
while i < siglen:
subsig = signal[i:i + frame_length]
ave_energy = np.sum(subsig ** 2) / float(len(subsig))
if ave_energy < average_energy * perc:
i += frame_length
else:
sigaddlen = min(frame_shift_length, len(subsig))
retsig[new_siglen:new_siglen + sigaddlen] = subsig[:sigaddlen]
new_siglen += sigaddlen
i += frame_shift_length
retsig = retsig[:new_siglen]
return retsig.astype(orig_dtype)
class VAD(object):
def __init__(self):
self.initted = False
self.nr = NoiseReduction()
self.ltsd = LTSD_VAD()
def init_noise(self, fs, signal):
self.initted = True
self.nr.init_noise(fs, signal)
self.ltsd.init_params_by_noise(fs, signal)
def filter(self, fs, signal):
if not self.initted:
raise "NoiseFilter Not Initialized"
filtered, intervals = self.ltsd.filter(signal)
return filtered, intervals
class VAD(object):
def __init__(self):
self.initted = False
self.nr = NoiseReduction()
self.ltsd = LTSD_VAD()
def init_noise(self, fs, signal):
self.initted = True
self.nr.init_noise(fs, signal)
self.ltsd.init_params_by_noise(fs, signal)
#nred = self.nr.filter(fs, signal)
#self.ltsd.init_params_by_noise(fs, nred)
def filter(self, fs, signal):
if not self.initted:
raise "NoiseFilter Not Initialized"
# nred = self.nr.filter(fs, signal)
# removed = remove_silence(fs, nred)
# self.ltsd.plot_ltsd(fs, nred)
filtered, intervals = self.ltsd.filter(signal)
return filtered, intervals
class VAD(object):
def __init__(self):
self.initted = False
self.nr = NoiseReduction()
self.ltsd = LTSD_VAD()
def init_noise(self, fs, signal):
self.initted = True
self.nr.init_noise(fs, signal)
self.ltsd.init_params_by_noise(fs, signal)
#nred = self.nr.filter(fs, signal)
#self.ltsd.init_params_by_noise(fs, nred)
def filter(self, fs, signal):
if not self.initted:
raise "NoiseFilter Not Initialized"
# nred = self.nr.filter(fs, signal)
# removed = remove_silence(fs, nred)
# self.ltsd.plot_ltsd(fs, nred)
filtered, intervals = self.ltsd.filter(signal)
return filtered, intervals
def __init__(self):
self.initted = False
self.nr = NoiseReduction()
self.ltsd = LTSD_VAD()
def __init__(self):
self.initted = False
self.nr = NoiseReduction()
self.ltsd = LTSD_VAD()
