def noise_reduction(signal,params,winsize,window,ss,ntime):
out=sp.zeros(len(signal),sp.float32)
n_pow = compute_avgpowerspectrum(signal[0:winsize*int(params[2] /float(winsize)/(1000.0/ntime))],winsize,window)#maybe 300ms
nf = len(signal)/(winsize/2) - 1
for no in xrange(nf):
s = get_frame(signal, winsize, no)
add_signal(out, ss.compute_by_noise_pow(s,n_pow), winsize, no)
return out
def subtruction(ssignal,ksignal,window,winsize,method):
nf = len(ssignal)/(winsize/2) - 1
out=sp.zeros(len(ssignal),sp.float32)
for no in xrange(nf):
s = get_frame(ssignal, winsize, no)
k = get_frame(ksignal, winsize, no)
add_signal(out, method.compute(s,k), winsize, no)
return out
def noise_reduction(signal, params, winsize, window, ss, ntime):
out = sp.zeros(len(signal), sp.float32)
# maybe 300ms
n_pow = compute_avgpowerspectrum(signal[0:winsize*int(params[2]/float(winsize)/(1000.0/ntime))], winsize, window)
nf = int(len(signal)/(winsize/2)) - 1
for no in xrange(nf):
s = get_frame(signal, winsize, no)
add_signal(out, ss.compute_by_noise_pow(s, n_pow), winsize, no)
return out
def vad(vas,signal,winsize,window):
out=sp.zeros(len(signal),sp.float32)
for va in vas:
for i in range(va[0],va[1]):
add_signal(out,get_frame(signal, winsize, i)*window,winsize,i)
for va in vas:
out[(va[0])*winsize/2:(va[0]+4)*winsize/2] = fin(winsize*2,out[(va[0])*winsize/2:(va[0]+4)*winsize/2])
out[(va[1]-4)*winsize/2:(va[1])*winsize/2] = fout(winsize*2,out[(va[1]-4)*winsize/2:(va[1])*winsize/2])
return out
def noise_reduction(signal,params,winsize,window,ss,ntime):
out=sp.zeros(len(signal),sp.float32)
ms = MinimumStatistics(winsize,window,params[2])
NP_lambda = compute_avgpowerspectrum(signal[0:winsize*int(params[2] /float(winsize)/(1000.0/ntime))],winsize,window)#maybe 300ms
ms.init_noise_profile(NP_lambda)
nf = int(len(signal)/(winsize/2)) - 1
for no in xrange(nf):
frame = get_frame(signal, winsize, no)
n_pow = ms.compute(frame,no)
if int(params[2] /float(winsize)/3.0)*2>no:
n_pow = NP_lambda
res = ss.compute_by_noise_pow(frame,n_pow)
add_signal(out, res, winsize, no)
#ms.show_debug_result()
return out
def vad(vas, signal, winsize, window):
out = np.zeros(len(signal), np.float32)
for va in vas:
for i in range(va[0], va[1]+2):
add_signal(out, get_frame(signal, winsize, i)*window, winsize, i)
return out