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ltsd.py
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ltsd.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#https://gist.github.com/shunsukeaihara/4603147#file-ltsd_vad-py
import wave
import numpy as np
import scipy as sp
import speech_processing as speech
import vad_eval as vad
import librosa
try:
try:
import scikits.audiolab as al
except ImportError:
import audiolab as al
except ImportError:
al = None
print("Warning: scikits.audiolab not found! Using scipy.io.wavfile")
from scipy.io import wavfile
#WINSIZE=8192
WINMS = 50
def read_signal(filename, winsize):
wf=wave.open(filename,'rb')
n=wf.getnframes()
str=wf.readframes(n)
#channels, samplewidth?, framerate, nframes, comptype, compname
#why?
params = ((wf.getnchannels(), wf.getsampwidth(),
wf.getframerate(), wf.getnframes(),
wf.getcomptype(), wf.getcompname()))
siglen=((int )(len(str)/2/winsize) + 1) * winsize
signal=sp.zeros(siglen, sp.int16)
signal[0:len(str)/2] = sp.fromstring(str,sp.int16)
return [signal, params]
def get_frame(signal, winsize, no):
shift=winsize/2
start=no*shift
end = start+winsize
return signal[start:end]
class LTSD():
def __init__(self,winsize,window,order, init_noise=None):
self.winsize = winsize
self.window = window
self.order = order
self.amplitude = {}
self.E0 = 0
self.E1 = 0
self.init_noise = init_noise
def get_amplitude(self,signal,l):
if self.amplitude.has_key(l):
return self.amplitude[l]
else:
amp = sp.absolute(sp.fft(get_frame(signal, self.winsize,l) * self.window))
self.amplitude[l] = amp
return amp
def compute_noise_avg_spectrum(self,nsignal):
windownum = len(nsignal)/(self.winsize/2) - 1
avgamp = np.zeros(self.winsize)
for l in xrange(windownum):
avgamp += sp.absolute(sp.fft(get_frame(nsignal, self.winsize,l) * self.window))
return avgamp/float(windownum)
def compute(self,signal):
self.windownum = len(signal)/(self.winsize/2) - 1
ltsds = np.zeros(self.windownum)
#Calculate the average noise spectrum amplitude based on 20 frames in the head parts of input signal.
#print("first frames", self.winsize,self.winsize*10,self.winsize*20)
noise_start = self.winsize
noise_end = self.winsize*20
if self.init_noise is None:
noise_magnitudes=np.zeros(9)
noise = signal
print("not using auxilliary noise signal")
else:
print("processing auxilliary noise signal")
noise = self.init_noise*(speech.rms(signal)/speech.rms(self.init_noise))
noise_magnitudes=np.zeros(len(noise[noise_start:noise_end])/self.winsize/2)
for i in range(0,len(noise_magnitudes)):
noise_magnitudes[i] = np.sum((get_frame(noise[noise_start:noise_end], self.winsize, i+1)*self.window)**2)
self.avgnoise = self.compute_noise_avg_spectrum(noise[noise_start:noise_end])**2
self.E0 = min(noise_magnitudes)
self.E1 = max(noise_magnitudes)
#print(self.E0, self.E1)
for l in xrange(self.windownum):
ltsds[l] = self.ltsd(signal, l, 5)
return ltsds, np.percentile(ltsds[2:20],75), noise_start, noise_end
def gamma(self, e):
if e <= self.E0:
return gamma0
elif self.E0 < e < self.E1:
return None #TODO
else:
return gamma0
def ltse(self,signal,l,order):
maxmag = np.zeros(self.winsize)
for idx in range(l-order,l+order+1):
amp = self.get_amplitude(signal,idx)
maxmag = np.maximum(maxmag,amp)
return maxmag
def ltsd(self,signal,l,order):
if l < order or l+order >= self.windownum:
return 0
return 10.0*np.log10(np.sum(self.ltse(signal,l,order)**2/self.avgnoise)/float(len(self.avgnoise)))
def update_avgnoise(ltsds, l, k, alpha=0.25):
self.avgnoise = self.compute_noise_avg_spectrum(ltsds[i-k:i])**2
def segments(self, frames, ltsds, t, min_len=30):
ranges = []
segment=[]
n_noise = 0 #n_of_noise_neighbors
for i in range(0, len(ltsds)):
if ltsds[i]>t:
if len(segment) == 0 or len(segment) == 1:
segment.append(i)
elif len(segment) == 2:
segment[1] = i
else:
if len(segment) == 2:
ranges.append(segment)
segment = []
self.update_avgnoise(ltsds,i,k)
t = self.gamma()
return ranges
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 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
if __name__ == "__main__":
import random, os, sys
import matplotlib.pyplot as plt
from sys import argv
if len(sys.argv) >= 3:
for f in os.listdir(argv[1]):
if os.path.splitext(f)[1] == ".flac":
signame = os.path.basename(os.path.splitext(f)[0])
print(signame)
ids = signame.split("_")
noisefile = "noise8k/"+ids[1]+".flac"
print(noisefile)
#if not os.path.exists(noisefile):
if True:
noisefile = None
indexes = vad(argv[1]+f, noisefile)
res_name = argv[2]+"/ltsd_"+os.path.basename(os.path.splitext(f)[0])+".txt"
f = open(res_name, 'w')
f.write("\n".join([str(x) for x in indexes]))
f.close()
else:
print("Usage: "+sys.argv[0]+" [inputdir] [resultdir]")