def segmental_log_spectrum_distance(s, shat, winsize, winfunc):
size = min(len(s), len(shat))
nf = size / (winsize / 2) - 1
ret = []
for no in xrange(nf):
s_i = get_frame(s, winsize, no)
shat_i = get_frame(shat, winsize, no)
ret.append(log_spectrum_distance(s_i, shat_i, winfunc))
return ret
def segmental_log_spectrum_distance(s, shat, winsize, winfunc):
size = min(len(s), len(shat))
nf = size / (winsize / 2) - 1
ret = []
for no in xrange(nf):
s_i = get_frame(s, winsize, no)
shat_i = get_frame(shat, winsize, no)
ret.append(log_spectrum_distance(s_i, shat_i, winfunc))
return ret
def padded_append(self, last_token, token, text_buffer):
if last_token and last_token.span:
spaces = token.span.start - last_token.span.stop
elif last_token:
spaces = 1
else:
spaces = 0
for i in xrange(spaces):
text_buffer.append(' ')
text_buffer.append(token.norm)
def padded_append(self, last_token, token, text_buffer):
if last_token and last_token.span:
spaces = token.span.start - last_token.span.stop
elif last_token:
spaces = 1
else:
spaces = 0
for i in xrange(spaces):
text_buffer.append(' ')
text_buffer.append(token.norm)
def run_processor(process, args, doc_class=None, automagic=False, dealer_url='inproc://workers'):
if any(getattr(args, a, None) for a in ('serve_url', 'worker_url')):
context = zmq.Context(1)
if args.serve_url:
clients = context.socket(zmq.ROUTER)
clients.bind(args.serve_url)
workers = context.socket(zmq.DEALER)
workers.bind(dealer_url)
else:
dealer_url = args.worker_url
run = lambda: process(zmq_coroutine(context, dealer_url, doc_class, automagic))
threads = [threading.Thread(target=run) for i in xrange(args.nthreads)]
for thread in threads:
thread.start()
if args.serve_url:
zmq.device(zmq.QUEUE, clients, workers)
else:
process(stream_coroutine(sys.stdin, sys.stdout, doc_class, automagic))
int32_ary_sound = sp.int32(ary_sound)
int32_ary_noise = sp.int32(ary_noise)
ary2 = sp.int16(int32_ary_sound + int32_ary_noise)
data2 = ary2.tostring()
synth.writeframes(data2)
remain = remain - s
sound.close()
noise.close()
synth.close()
infile = 'tools/sound/noisy.wav'
signal, params = read_signal(infile, WINSIZE)
nf = len(signal) / (WINSIZE / 2) - 1
sig_out = sp.zeros(len(signal), sp.float32)
window = sp.hanning(WINSIZE)
ms = MinimumStatistics(WINSIZE, window, params[2])
NP_lambda = compute_avgpowerspectrum(signal[0:WINSIZE * int(params[2] / float(WINSIZE) / 3.0)],
WINSIZE, window)
ms.init_noise_profile(NP_lambda)
ss = JointMap(WINSIZE, window)
for no in xrange(nf):
frame = get_frame(signal, WINSIZE, no)
n_pow = ms.compute(frame, no)
res = ss.compute_by_noise_pow(frame, n_pow)
add_signal(sig_out, res, WINSIZE, no)
ms.show_debug_result()
write_signal("tools/sound/noise_reduction.wav", params, sig_out)
int32_ary_sound = sp.int32(ary_sound)
int32_ary_noise = sp.int32(ary_noise)
ary2 = sp.int16(int32_ary_sound + int32_ary_noise)
data2 = ary2.tostring()
synth.writeframes(data2)
remain = remain - s
sound.close()
noise.close()
synth.close()
infile = 'tools/sound/noisy.wav'
signal, params = read_signal(infile, WINSIZE)
nf = len(signal) / (WINSIZE / 2) - 1
sig_out = sp.zeros(len(signal), sp.float32)
window = sp.hanning(WINSIZE)
ms = MinimumStatistics(WINSIZE, window, params[2])
NP_lambda = compute_avgpowerspectrum(
signal[0:WINSIZE * int(params[2] / float(WINSIZE) / 3.0)], WINSIZE,
window)
ms.init_noise_profile(NP_lambda)
ss = JointMap(WINSIZE, window)
for no in xrange(nf):
frame = get_frame(signal, WINSIZE, no)
n_pow = ms.compute(frame, no)
res = ss.compute_by_noise_pow(frame, n_pow)
add_signal(sig_out, res, WINSIZE, no)
ms.show_debug_result()
write_signal("tools/sound/noise_reduction.wav", params, sig_out)
