def analyze_channels(freq, filename, function):
"""
Given a filename, run the given analyzer function on each channel of the
file
"""
sample_rate, signal = scipy.io.wavfile.read(filename)
print 'Analyzing "' + filename + '"...'
if len(signal.shape) == 1:
# Monaural
function(freq, signal, sample_rate)
elif signal.shape[1] == 2:
# Stereo
if np.array_equal(signal[:, 0], signal[:, 1]):
print '-- Left and Right channels are identical --'
function(freq, signal[:, 0], sample_rate)
else:
print '-- Left channel --'
function(freq, signal[:, 0], sample_rate)
print '-- Right channel --'
function(freq, signal[:, 1], sample_rate)
else:
# Multi-channel
for ch_no, channel in enumerate(signal.transpose()):
print '-- Channel %d --' % (ch_no + 1)
function(freq, channel, sample_rate)
def transform_beat(sig, train=False):
# 前置不可或缺的步骤
# sig = resample(sig, config.target_point_num)
# # 数据增强
if train:
if np.random.randn() > 0.5: sig = scaling(sig)
if np.random.randn() > 0.5: sig = verflip(sig)
if np.random.randn() > 0.5: sig = shift(sig)
# if np.random.randn() > 0.4: sig = wavelet_db6(sig)
# if np.random.randn() > 0.5: sig = wavelet_db4(sig) # time consuming
# if np.random.randn() > 0.3: sig = wavelet_sym(sig)
# 后置不可或缺的步骤
sig = sig.transpose()
sig = torch.tensor(sig.copy(), dtype=torch.float)
return sig
def transform(sig, train=True):
# 前置不可或缺的步骤
#sig = resample(sig, config.target_point_num)
# sig_ext = np.zeros([config.target_point_num,12])
# sig = resample(sig, int(sig.shape[0]/500 * config.target_fs))
#print(sig.shape)
# if sig.shape[0] < config.target_point_num:
# sig_ext[:sig.shape[0],:] = sig
# if sig.shape[0] > config.target_point_num:
# sig_ext = sig[:config.target_point_num,:]
# sig = sig_ext
# # 数据增强
if train:
if np.random.randn() > 0.5: sig = scaling(sig)
if np.random.randn() > 0.3: sig = verflip(sig)
if np.random.randn() > 0.5: sig = shift(sig)
if np.random.randn() > 0.3:
sig = butter_bandpass_filter(sig,0.05,46,256)
# if np.random.randn() > -1:
# fi = np.random.randint(11)
# if fi % 2 == 0 and fi != 2 and fi != 0 :
# sig = wavelet_db6(sig,'db{}'.format(fi) ,8)
# else:#if fi % 2 != 0:
# if np.random.randn() > -0.5:
# sig = butter_bandpass_filter(sig,0.05,40,256)
# else:
# sig = butter_bandpass_forward_backward_filter(sig,0.05,40,256)
else:
#sig = butter_bandpass_filter(sig,0.05,46,256)
pass
# 后置不可或缺的步骤
sig = sig.transpose()
sig = torch.tensor(sig.copy(), dtype=torch.float)
return sig
def calc_rxd(data, band_idx, size_column, size_row):
"""
Calcualte abnormal using RXD (Reed-Xiao) algorithm
"""
if data.shape[0] < band_idx.max():
print "Error: Number of band is larger then actual"
return None
num_bands = len(band_idx)
num_pixels = size_column * size_row
rxd = np.zeros(num_pixels)
if num_bands > 1:
# reshaping
GG = np.zeros((num_bands, num_pixels))
for i in range(num_bands):
band = band_idx[i]
GG[i, ] = data[band].reshape(num_pixels)
# calculating covariance matrix
M = np.cov(GG)
M_i = inv(M)
avg = [np.mean(GG[i]) for i in range(num_bands)]
for i in range(num_pixels):
signal = GG[:, i] - avg
rxd[i] = np.dot(np.dot(signal, M_i), signal.transpose())
elif num_bands == 1:
band = band_idx[0]
avg = np.mean(data[band])
std = np.std(data[band])
GG = data[band].reshape(num_pixels)
for i in range(num_pixels):
rxd[i] = abs((GG[i] - avg) / std)
abnormal = rxd.reshape((size_row, size_column))
return abnormal
def calc_rxd(data, band_idx, size_column, size_row): """ Calcualte abnormal using RXD (Reed-Xiao) algorithm """ if data.shape[0] < band_idx.max(): print "Error: Number of band is larger then actual" return None num_bands = len(band_idx) num_pixels = size_column * size_row rxd = np.zeros(num_pixels) if num_bands > 1: # reshaping GG = np.zeros((num_bands, num_pixels)) for i in range(num_bands): band = band_idx[i] GG[i, ] = data[band].reshape(num_pixels) # calculating covariance matrix M = np.cov(GG) M_i = inv(M) avg = [np.mean(GG[i]) for i in range(num_bands)] for i in range(num_pixels): signal = GG[:, i] - avg rxd[i] = np.dot(np.dot(signal, M_i), signal.transpose()) elif num_bands == 1: band = band_idx[0] avg = np.mean(data[band]) std = np.std(data[band]) GG = data[band].reshape(num_pixels) for i in range(num_pixels): rxd[i] = abs((GG[i] - avg) / std) abnormal = rxd.reshape((size_row, size_column)) return abnormal
def arange_tx(signal, num_tx, vx_axis=2, axis=1):
"""Separate interleaved radar data from separate TX along a certain axis to account for TDM radars.
Args:
signal (ndarray): Received signal.
num_tx (int): Number of transmit antennas.
vx_axis (int): Axis in which to accumulate the separated data.
axis (int): Axis in which the data is interleaved.
Returns:
ndarray: Separated received data in the
"""
# Reorder the axes
reordering = np.arange(len(signal.shape))
reordering[0] = axis
reordering[axis] = 0
signal = signal.transpose(reordering)
out = np.concatenate([signal[i::num_tx, ...] for i in range(num_tx)],
axis=vx_axis)
return out.transpose(reordering)
figs = figure()
#################
## TEACH
#################
n_neu_scale = [256]
for this_readout in range(len(n_neu_scale)):
res.Nn = n_neu_scale[this_readout]
res.reset()
for this_trial in range(3):
out = nsetup.stimulate({},send_reset_event=True,duration=200)
out = nsetup.stimulate({},send_reset_event=False,duration=300)
out = nsetup.stimulate({},send_reset_event=True,duration=duration_rec)
signal = go_reconstruct_signal_from_out(out,figs,upch=300,dnch=305,delta_up=0.1,delta_dn=0.1,do_detrend=False)
signal = [signal[:,0]-np.min(signal[:,0]),signal[:,1]]
signal = np.array(signal)
signal = signal.transpose()
#extract input and output
raw_data = out[0].raw_data()
dnch = 300
upch = 305
index_dn = np.where(raw_data[:,1] == dnch)[0]
index_up = np.where(raw_data[:,1] == upch)[0]
raw_data_input = []
raw_data_input.extend(raw_data[index_dn,:])
raw_data_input.extend(raw_data[index_up,:])
raw_data_input = np.reshape(raw_data_input,[len(index_dn)+len(index_up),2])
index_up = np.where(raw_data_input[:,1] == upch)[0]
index_dn = np.where(raw_data_input[:,1] == dnch)[0]
raw_data_input[index_dn,1] = 1
raw_data_input[index_up,1] = 0
raw_data = out[0].raw_data()
def prepare_data(mode, train_or_test, min=None, max=None):
if min:
config.MIN_MIX = min
if max:
config.MAX_MIX = max
mix_speechs = []
aim_fea = []
aim_spkid = []
aim_spkname = []
query = []
multi_spk_fea_list = []
multi_spk_wav_list = []
direction = []
if config.MODE == 1:
if config.DATASET == 'WSJ0':
spk_file_tr = open(
'/mnt/lustre/xushuang2/lcx/data/amcc-data/2channel/wav_spk.txt',
'r')
all_spk_train = [i.replace("\n", "") for i in spk_file_tr]
all_spk_train = sorted(all_spk_train)
print(all_spk_train)
spk_file_tt = open(
'/mnt/lustre/xushuang2/lcx/data/amcc-data/2channel/test/wav_spk.txt',
'r')
all_spk_test = [i.replace("\n", "") for i in spk_file_tt]
all_spk_test = sorted(all_spk_test)
print(all_spk_test)
all_spk = all_spk_train + all_spk_test
print(all_spk)
all_dir = [i for i in range(1, 20)]
dicDirFile = open(
'/mnt/lustre/xushuang2/lcx/data/amcc-data/2channel/wav_dirLabel2.txt',
'r') #打开数据
dirDict = {}
while True:
line = dicDirFile.readline()
if line == '':
break
index = line.find(' ')
key = line[:index]
#print(key)
value = line[index:]
dirDict[key] = value.replace("\n", "").replace(" ", "")
dicDirFile.close()
spk_samples_list = {}
batch_idx = 0
list_path = '/mnt/lustre/xushuang2/lcx/data/create-speaker-mixtures/'
all_samples_list = {}
sample_idx = {}
number_samples = {}
batch_mix = {}
mix_number_list = range(config.MIN_MIX, config.MAX_MIX + 1)
number_samples_all = 0
for mix_k in mix_number_list:
if train_or_test == 'train':
aim_list_path = list_path + 'mix_{}_spk_tr.txt'.format(
mix_k)
if train_or_test == 'valid':
aim_list_path = list_path + 'mix_{}_spk_cv.txt'.format(
mix_k)
if train_or_test == 'test':
aim_list_path = list_path + 'mix_{}_spk_tt.txt'.format(
mix_k)
config.batch_size = 1
all_samples_list[mix_k] = open(
aim_list_path).readlines() # [:31]
number_samples[mix_k] = len(all_samples_list[mix_k])
batch_mix[mix_k] = len(
all_samples_list[mix_k]) / config.batch_size
number_samples_all += len(all_samples_list[mix_k])
sample_idx[mix_k] = 0
if train_or_test == 'train' and config.SHUFFLE_BATCH:
random.shuffle(all_samples_list[mix_k])
print('shuffle success!', all_samples_list[mix_k][0])
batch_total = number_samples_all / config.batch_size
mix_k = random.sample(mix_number_list, 1)[0]
# while True:
for ___ in range(number_samples_all):
if ___ == number_samples_all - 1:
print('ends here.___')
yield False
mix_len = 0
if sample_idx[mix_k] >= batch_mix[mix_k] * config.batch_size:
mix_number_list.remove(mix_k)
try:
mix_k = random.sample(mix_number_list, 1)[0]
except ValueError:
print('seems there gets all over.')
if len(mix_number_list) == 0:
print('all mix number is over~!')
yield False
batch_idx = 0
mix_speechs = np.zeros((config.batch_size, config.MAX_LEN))
mix_feas = []
mix_phase = []
aim_fea = []
aim_spkid = []
aim_spkname = []
query = []
multi_spk_fea_list = []
multi_spk_order_list = []
multi_spk_wav_list = []
continue
all_over = 1
for kkkkk in mix_number_list:
if not sample_idx[
kkkkk] >= batch_mix[mix_k] * config.batch_size:
all_over = 0
break
if all_over:
print('all mix number is over~!')
yield False
# mix_k=random.sample(mix_number_list,1)[0]
if train_or_test == 'train':
aim_spk_k = random.sample(all_spk_train, mix_k)
elif train_or_test == 'test':
aim_spk_k = random.sample(all_spk_test, mix_k)
aim_spk_k = re.findall(
'/([0-9][0-9].)/',
all_samples_list[mix_k][sample_idx[mix_k]])
aim_spk_db_k = [
float(dd) for dd in re.findall(
' (.*?) ', all_samples_list[mix_k][sample_idx[mix_k]])
]
aim_spk_samplename_k = re.findall(
'/(.{8})\.wav ',
all_samples_list[mix_k][sample_idx[mix_k]])
assert len(aim_spk_k) == mix_k == len(aim_spk_db_k) == len(
aim_spk_samplename_k)
multi_fea_dict_this_sample = {}
multi_wav_dict_this_sample = {}
multi_name_list_this_sample = []
multi_db_dict_this_sample = {}
direction_sample = {}
for k, spk in enumerate(aim_spk_k):
sample_name = aim_spk_samplename_k[k]
if aim_spk_db_k[k] == 0:
aim_spk_db_k[k] = int(aim_spk_db_k[k])
if train_or_test != 'test':
spk_speech_path = data_path + '/' + 'train' + '/' + sample_name + '_' + str(
aim_spk_db_k[k]) + '_simu_nore.wav'
else:
spk_speech_path = data_path + '/' + 'test' + '/' + sample_name + '_' + str(
aim_spk_db_k[k]) + '_simu_nore.wav'
signal, rate = sf.read(spk_speech_path)
wav_name = sample_name + '_' + str(
aim_spk_db_k[k]) + '_simu_nore.wav'
direction_sample[spk] = dirDict[wav_name]
if rate != config.FRAME_RATE:
print("config.FRAME_RATE", config.FRAME_RATE)
signal = signal.transpose()
signal = resampy.resample(signal,
rate,
config.FRAME_RATE,
filter='kaiser_best')
signal = signal.transpose()
if signal.shape[0] > config.MAX_LEN:
signal = signal[:config.MAX_LEN, :]
if signal.shape[0] > mix_len:
mix_len = signal.shape[0]
signal -= np.mean(signal)
signal /= np.max(np.abs(signal))
if signal.shape[0] < config.MAX_LEN:
signal = np.r_[signal,
np.zeros(
(config.MAX_LEN - signal.shape[0],
signal.shape[1]))]
if k == 0:
ratio = 10**(aim_spk_db_k[k] / 20.0)
signal = ratio * signal
aim_spkname.append(aim_spk_k[0])
aim_spk_speech = signal
aim_spkid.append(aim_spkname)
wav_mix = signal
signal_c0 = signal[:, 0]
a, b, frq = scipy.signal.stft(
signal_c0,
fs=8000,
nfft=config.FRAME_LENGTH,
noverlap=config.FRAME_SHIFT)
aim_fea_clean = np.transpose(np.abs(frq))
aim_fea.append(aim_fea_clean)
multi_fea_dict_this_sample[spk] = aim_fea_clean
multi_wav_dict_this_sample[spk] = signal[:, 0]
else:
ratio = 10**(aim_spk_db_k[k] / 20.0)
signal = ratio * signal
wav_mix = wav_mix + signal
a, b, frq = scipy.signal.stft(
signal[:, 0],
fs=8000,
nfft=config.FRAME_LENGTH,
noverlap=config.FRAME_SHIFT)
some_fea_clean = np.transpose(np.abs(frq))
multi_fea_dict_this_sample[spk] = some_fea_clean
multi_wav_dict_this_sample[spk] = signal[:, 0]
multi_spk_fea_list.append(multi_fea_dict_this_sample)
multi_spk_wav_list.append(multi_wav_dict_this_sample)
mix_speechs.append(wav_mix)
direction.append(direction_sample)
batch_idx += 1
if batch_idx == config.batch_size:
mix_k = random.sample(mix_number_list, 1)[0]
aim_fea = np.array(aim_fea)
query = np.array(query)
print('spk_list_from_this_gen:{}'.format(aim_spkname))
print('aim spk list:',
[one.keys() for one in multi_spk_fea_list])
batch_ordre = get_energy_order(multi_spk_wav_list)
direction = get_spk_order(direction, batch_ordre)
if mode == 'global':
all_spk = sorted(all_spk)
all_spk = sorted(all_spk_train)
all_spk.insert(0, '<BOS>') # 添加两个结构符号，来标识开始或结束。
all_spk.append('<EOS>')
all_dir = sorted(all_dir)
all_dir.insert(0, '<BOS>')
all_dir.append('<EOS>')
all_spk_test = sorted(all_spk_test)
dict_spk_to_idx = {
spk: idx
for idx, spk in enumerate(all_spk)
}
dict_idx_to_spk = {
idx: spk
for idx, spk in enumerate(all_spk)
}
dict_dir_to_idx = {
dire: idx
for idx, dire in enumerate(all_dir)
}
dict_idx_to_dir = {
idx: dire
for idx, dire in enumerate(all_dir)
}
yield {
'all_spk': all_spk,
'dict_spk_to_idx': dict_spk_to_idx,
'dict_idx_to_spk': dict_idx_to_spk,
'all_dir': all_dir,
'dict_dir_to_idx': dict_dir_to_idx,
'dict_idx_to_dir': dict_idx_to_dir,
'num_fre': aim_fea.shape[2],
'num_frames': aim_fea.shape[1],
'total_spk_num': len(all_spk),
'total_batch_num': batch_total
}
elif mode == 'once':
yield {
'mix_wav':
mix_speechs,
'aim_fea':
aim_fea,
'aim_spkname':
aim_spkname,
'direction':
direction,
'query':
query,
'num_all_spk':
len(all_spk),
'multi_spk_fea_list':
multi_spk_fea_list,
'multi_spk_wav_list':
multi_spk_wav_list,
'batch_order':
batch_ordre,
'batch_total':
batch_total,
'tas_zip':
_collate_fn(mix_speechs, multi_spk_wav_list,
batch_ordre)
}
elif mode == 'tasnet':
yield _collate_fn(mix_speechs, multi_spk_wav_list)
batch_idx = 0
mix_speechs = []
aim_fea = []
aim_spkid = []
aim_spkname = []
query = []
multi_spk_fea_list = []
multi_spk_wav_list = []
direction = []
sample_idx[mix_k] += 1
else:
raise ValueError('No such dataset:{} for Speech.'.format(
config.DATASET))
pass
else:
raise ValueError('No such Model:{}'.format(config.MODE))
for this_trial in range(3):
out = nsetup.stimulate({}, send_reset_event=True, duration=200)
out = nsetup.stimulate({}, send_reset_event=False, duration=300)
out = nsetup.stimulate({},
send_reset_event=True,
duration=duration_rec)
signal = go_reconstruct_signal_from_out(out,
figs,
upch=300,
dnch=305,
delta_up=0.1,
delta_dn=0.1,
do_detrend=False)
signal = [signal[:, 0] - np.min(signal[:, 0]), signal[:, 1]]
signal = np.array(signal)
signal = signal.transpose()
#extract input and output
raw_data = out[0].raw_data()
dnch = 300
upch = 305
index_dn = np.where(raw_data[:, 1] == dnch)[0]
index_up = np.where(raw_data[:, 1] == upch)[0]
raw_data_input = []
raw_data_input.extend(raw_data[index_dn, :])
raw_data_input.extend(raw_data[index_up, :])
raw_data_input = np.reshape(raw_data_input,
[len(index_dn) + len(index_up), 2])
index_up = np.where(raw_data_input[:, 1] == upch)[0]
index_dn = np.where(raw_data_input[:, 1] == dnch)[0]
raw_data_input[index_dn, 1] = 1
raw_data_input[index_up, 1] = 0
def fourier_filter(tod,
filt_function,
detrend='linear',
resize='zero_pad',
axis_name='samps',
signal_name='signal',
time_name='timestamps',
**kwargs):
"""Return a filtered tod.signal_name along the axis axis_name.
Does not change the data in the axis manager.
Arguments:
tod: axis manager
filt_function: function( freqs, tod ) function that takes a set of
frequencies and the axis manager and returns the filter in
fouier space
detrend: Method of detrending to be done before ffting. Can
be 'linear', 'mean', or None.
resize: How to resize the axis to increase fft speed. 'zero_pad'
will increase to the next 2**N. 'trim' will cut out so the
factorization of N contains only low primes. None will not
change the axis length and might be quite slow. Trim will be
kinda weird here, because signal will not be returned as the same
size as it is input
axis_name: name of axis you would like to fft along
signal_name: name of the variable in tod to fft
time_name: name for getting time of data (in seconds) from tod
Returns:
signal: filtered tod.signal_name
"""
if len(tod._assignments[signal_name]) > 2:
raise ValueError('fouier_filter only works for 1D or 2D data streams')
axis = getattr(tod, axis_name)
times = getattr(tod, time_name)
delta_t = (times[-1] - times[0]) / axis.count
if len(tod._assignments[signal_name]) == 1:
n_det = 1
## signal will be at least 2D
main_idx = 1
other_idx = None
elif len(tod._assignments[signal_name]) == 2:
checks = np.array(
[x == axis_name for x in tod._assignments[signal_name]],
dtype='bool')
main_idx = np.where(checks)[0][0]
other_idx = np.where(~checks)[0][0]
other_axis = getattr(tod, tod._assignments[signal_name][other_idx])
n_det = other_axis.count
if detrend is None:
signal = np.atleast_2d(getattr(tod, signal_name))
else:
signal = detrend_data(tod,
detrend,
axis_name=axis_name,
signal_name=signal_name)
if other_idx is not None and other_idx != 0:
## so that code can be written always along axis 1
signal = signal.transpose()
if resize == 'zero_pad':
k = int(np.ceil(np.log(axis.count) / np.log(2)))
n = 2**k
elif resize == 'trim':
n = fft.find_inferior_integer(axis.count)
elif resize is None:
n = axis.count
else:
raise ValueError('resize must be "zero_pad", "trim", or None')
a, b, t_1, t_2 = build_rfft_object(n_det, n, 'BOTH')
if resize == 'zero_pad':
a[:, :axis.count] = signal
a[:, axis.count:] = 0
elif resize == 'trim':
a[:] = signal[:, :n]
else:
a[:] = signal[:]
## FFT Signal
t_1()
## Get Filter
freqs = np.fft.rfftfreq(n, delta_t)
filt = filt_function(freqs, tod, **kwargs)
b[:] *= filt
## FFT Back
t_2()
if resize == 'zero_pad':
signal = a[:, :axis.count]
else:
signal = a[:]
if other_idx is not None and other_idx != 0:
return signal.transpose()
return signal
