def lr_preprocess(self, x):
global LR_HOP_DURATION
global HOP_DURATION
global AUDIO_SAMPLE_RATE
if self.raw_max_length is None:
self.raw_max_length = get_max_length(x)
scale = self.raw_max_length/84000
LR_HOP_DURATION = max(BASE_LR_HOP_DURATION,BASE_LR_HOP_DURATION*scale)
HOP_DURATION = max(BASE_HOP_DURATION,BASE_HOP_DURATION*scale/2)
AUDIO_SAMPLE_RATE = max(BASE_AUDIO_SAMPLE_RATE,int(BASE_AUDIO_SAMPLE_RATE*scale/3))
print('LR_HOP_DURATION---%s'%LR_HOP_DURATION)
print('HOP_DURATION---%s'%HOP_DURATION)
print('AUDIO_SAMPLE_RATE---%s'%AUDIO_SAMPLE_RATE)
x = [sample[0:MAX_AUDIO_DURATION * AUDIO_SAMPLE_RATE] for sample in x]
x_mel = extract_melspectrogram_parallel(
x, n_mels=30, use_power_db=True,lr=True)
# x_contrast = extract_bandwidth_parallel(x)
x_feas = []
for i in range(len(x_mel)):
mel = np.mean(x_mel[i], axis=0).reshape(-1)
mel_std = np.std(x_mel[i], axis=0).reshape(-1)
# contrast = np.mean(x_contrast[i], axis=0).reshape(-1)
# contrast_std = np.std(x_contrast[i], axis=0).reshape(-1)
# contrast, contrast_std
x_feas.append(np.concatenate([mel, mel_std], axis=-1))
x_feas = np.asarray(x_feas)
scaler = StandardScaler()
X = scaler.fit_transform(x_feas[:, :])
return X
def nn_preprocess(self, x, n_mfcc=96, max_duration=5, is_mfcc=True):
if self.raw_max_length is None:
self.raw_max_length = get_max_length(x)
if self.raw_max_length > (MIDDLE_DURATION * AUDIO_SAMPLE_RATE):
self.need_30s = True
if len(self._train_y) < 1000 and self._num_classes < 30:
self.crnn_first = True
self.raw_max_length = min(max_duration * AUDIO_SAMPLE_RATE,
self.raw_max_length)
self.raw_max_length = max(MAX_AUDIO_DURATION * AUDIO_SAMPLE_RATE,
self.raw_max_length)
x = [sample[0:self.raw_max_length] for sample in x]
if is_mfcc:
# extract mfcc
x = extract_mfcc_parallel(x, n_mfcc=n_mfcc)
else:
x = extract_melspectrogram_parallel(x,
n_mels=128,
use_power_db=True)
if self.fea_max_length is None:
self.fea_max_length = get_max_length(x)
self.fea_max_length = min(MAX_FRAME_NUM, self.fea_max_length)
x = pad_seq(x, pad_len=self.fea_max_length)
return x
def preprocess_data(self, x):
if IS_CUT_AUDIO:
x = [sample[0:MAX_AUDIO_DURATION * AUDIO_SAMPLE_RATE]
for sample in x]
x_mel = extract_melspectrogram_parallel(
x, n_mels=128, use_power_db=True)
# x_mel = extract_mfcc_parallel(x, n_mfcc=96)
if self.max_length is None:
self.max_length = get_max_length(x_mel)
self.max_length = min(MAX_FRAME_NUM, self.max_length)
x_mel = pad_seq(x_mel, pad_len=self.max_length)
x_mel = x_mel[:, :, :, np.newaxis]
return x_mel
def preprocess_data(self, x):
if IS_CUT_AUDIO:
x = [sample[0:MAX_AUDIO_DURATION * AUDIO_SAMPLE_RATE] for sample in x]
# extract mfcc
x_mfcc = extract_mfcc_parallel(x, n_mfcc=64)
x_mel = extract_melspectrogram_parallel(x, n_mels=64, use_power_db=True)
if self.max_length is None:
self.max_length = get_max_length(x_mfcc)
self.max_length = min(MAX_FRAME_NUM, self.max_length)
x_mfcc = pad_seq(x_mfcc, self.max_length)
x_mel = pad_seq(x_mel, self.max_length)
x_feas = np.concatenate([x_mfcc, x_mel], axis=-1)
x_feas = x_feas[:, :, :, np.newaxis]
# x_mel = pad_seq(x_mel, self.max_length)
# x_mel = x_mel[:, :, :, np.newaxis]
return x_feas
def preprocess_data(self, x):
# cut down
x = [sample[0:MAX_AUDIO_DURATION * AUDIO_SAMPLE_RATE] for sample in x]
# extract mfcc
# x_mfcc = extract_mfcc_parallel(x, n_mfcc=63)
x_mel = extract_melspectrogram_parallel(x,
n_mels=40,
use_power_db=True)
# x_chroma_stft = extract_chroma_stft_parallel(x, n_chroma=12)
# x_rms = extract_rms_parallel(x)
# x_contrast = extract_spectral_contrast_parallel(x, n_bands=6)
# x_flatness = extract_spectral_flatness_parallel(x)
# x_polyfeatures = extract_poly_features_parallel(x, order=1)
# x_cent = extract_spectral_centroid_parallel(x)
# x_bw = extract_bandwidth_parallel(x)
# x_rolloff = extract_spectral_rolloff_parallel(x)
# x_zcr = extract_zero_crossing_rate_parallel(x)
x_feas = []
for i in range(len(x_mel)):
mel = np.mean(x_mel[i], axis=0).reshape(-1)
mel_std = np.std(x_mel[i], axis=0).reshape(-1)
# mel = np.mean(x_mel[i], axis=0).reshape(-1)
# mel_std = np.std(x_mel[i], axis=0).reshape(-1)
# chroma_stft = np.mean(x_chroma_stft[i], axis=0).reshape(-1)
# chroma_stft_std = np.std(x_chroma_stft[i], axis=0).reshape(-1)
# rms = np.mean(x_rms[i], axis=0).reshape(-1)
# contrast = np.mean(x_contrast[i], axis=0).reshape(-1)
# contrast_std = np.std(x_contrast[i], axis=0).reshape(-1)
# flatness = np.mean(x_flatness[i], axis=0).reshape(-1)
# polyfeatures = np.mean(x_polyfeatures[i], axis=0).reshape(-1)
# cent = np.mean(x_cent[i], axis=0).reshape(-1)
# cent_std = np.std(x_cent[i], axis=0).reshape(-1)
# bw = np.mean(x_bw[i], axis=0).reshape(-1)
# bw_std = np.std(x_bw[i], axis=0).reshape(-1)
# rolloff = np.mean(x_rolloff[i], axis=0).reshape(-1)
# zcr = np.mean(x_zcr[i], axis=0).reshape(-1)
x_feas.append(np.concatenate([mel, mel_std], axis=-1))
# x_feas.append(np.concatenate([mfcc, mel, contrast, bw, cent, mfcc_std, mel_std, contrast_std, bw_std, cent_std]))
x_feas = np.asarray(x_feas)
scaler = StandardScaler()
X = scaler.fit_transform(x_feas[:, :])
# log( 'x_feas shape: {X.shape}\n'
# 'x_feas[0]: {X[0]}')
return X
def preprocess_data(self, x):
if IS_CUT_AUDIO:
x = [
sample[0:MAX_AUDIO_DURATION * AUDIO_SAMPLE_RATE]
for sample in x
]
# extract mfcc
x_mfcc = extract_mfcc_parallel(x, n_mfcc=20)
x_mel = extract_melspectrogram_parallel(x,
n_mels=20,
use_power_db=True)
x_chroma_stft = extract_chroma_stft_parallel(x, n_chroma=12)
# x_rms = extract_rms_parallel(x)
x_contrast = extract_spectral_contrast_parallel(x, n_bands=6)
x_flatness = extract_spectral_flatness_parallel(x)
# x_polyfeatures = extract_poly_features_parallel(x, order=1)
x_cent = extract_spectral_centroid_parallel(x)
x_bw = extract_bandwidth_parallel(x)
x_rolloff = extract_spectral_rolloff_parallel(x)
x_zcr = extract_zero_crossing_rate_parallel(x)
x_feas = []
for i in range(len(x_mfcc)):
mfcc = np.mean(x_mfcc[i], axis=0).reshape(-1)
mel = np.mean(x_mel[i], axis=0).reshape(-1)
chroma_stft = np.mean(x_chroma_stft[i], axis=0).reshape(-1)
# rms = np.mean(x_rms[i], axis=0).reshape(-1)
contrast = np.mean(x_contrast[i], axis=0).reshape(-1)
flatness = np.mean(x_flatness[i], axis=0).reshape(-1)
# polyfeatures = np.mean(x_polyfeatures[i], axis=0).reshape(-1)
cent = np.mean(x_cent[i], axis=0).reshape(-1)
bw = np.mean(x_bw[i], axis=0).reshape(-1)
rolloff = np.mean(x_rolloff[i], axis=0).reshape(-1)
zcr = np.mean(x_zcr[i], axis=0).reshape(-1)
x_feas.append(
np.concatenate([
mfcc, mel, chroma_stft, contrast, flatness, cent, bw,
rolloff, zcr
],
axis=-1))
x_feas = np.asarray(x_feas)
scaler = StandardScaler()
X = scaler.fit_transform(x_feas[:, :])
return X
def lr_preprocess(self, x):
x = [sample[0:MAX_AUDIO_DURATION * AUDIO_SAMPLE_RATE] for sample in x]
x_mel = extract_melspectrogram_parallel(x, n_mels=30, use_power_db=True)
# x_contrast = extract_bandwidth_parallel(x)
x_feas = []
for i in range(len(x_mel)):
mel = np.mean(x_mel[i], axis=0).reshape(-1)
mel_std = np.std(x_mel[i], axis=0).reshape(-1)
# contrast = np.mean(x_contrast[i], axis=0).reshape(-1)
# contrast_std = np.std(x_contrast[i], axis=0).reshape(-1)
# contrast, contrast_std
x_feas.append(np.concatenate([mel, mel_std], axis=-1))
x_feas = np.asarray(x_feas)
scaler = StandardScaler()
X = scaler.fit_transform(x_feas[:, :])
return X
def nn_preprocess(self, x, n_mfcc=96, max_duration=5, is_mfcc=True):
global LR_HOP_DURATION
global HOP_DURATION
global AUDIO_SAMPLE_RATE
if self.raw_max_length is None:
self.raw_max_length = get_max_length(x)
scale = self.raw_max_length/84000
LR_HOP_DURATION = max(BASE_LR_HOP_DURATION,BASE_LR_HOP_DURATION*scale)
HOP_DURATION = max(BASE_HOP_DURATION,BASE_HOP_DURATION*scale/2)
AUDIO_SAMPLE_RATE = max(BASE_AUDIO_SAMPLE_RATE,int(BASE_AUDIO_SAMPLE_RATE*scale/3))
print('LR_HOP_DURATION---%s'%LR_HOP_DURATION)
print('HOP_DURATION---%s'%HOP_DURATION)
print('AUDIO_SAMPLE_RATE---%s'%AUDIO_SAMPLE_RATE)
if self.raw_max_length > (MIDDLE_DURATION * AUDIO_SAMPLE_RATE):
self.need_30s = True
if len(self._train_y) < 1000 and self._num_classes < 30:
self.crnn_first = True
self.raw_max_length = min(
max_duration * AUDIO_SAMPLE_RATE,
self.raw_max_length)
self.raw_max_length = max(
MAX_AUDIO_DURATION *
AUDIO_SAMPLE_RATE,
self.raw_max_length)
x = [sample[0:self.raw_max_length] for sample in x]
if is_mfcc:
# extract mfcc
x = extract_mfcc_parallel(x, n_mfcc=n_mfcc)
else:
x = extract_melspectrogram_parallel(
x, n_mels=128, use_power_db=True)
if self.fea_max_length is None:
self.fea_max_length = get_max_length(x)
self.fea_max_length = min(MAX_FRAME_NUM, self.fea_max_length)
x = pad_seq(x, pad_len=self.fea_max_length)
return x
