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 = extract_mfcc_parallel(x, n_mfcc=96)
if self.max_length is None:
self.max_length = get_max_length(x)
self.max_length = min(MAX_FRAME_NUM, self.max_length)
x = pad_seq(x, pad_len=self.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
]
# 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 preprocess_data(self, x):
# mel-spectrogram parameters
SR = 16000
N_FFT = 512
N_MELS = 96
HOP_LEN = 256
DURA = 21.84 # to make it 1366 frame.
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_mfcc = extract_mfcc_parallel(x, n_mfcc=96)
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, pad_len=self.max_length)
x_mfcc = x_mfcc[:, :, :, np.newaxis]
return x_mfcc
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 = extract_mfcc_parallel(x, n_mfcc=96)
if self.max_length is None:
self.max_length = get_max_length(x)
x = pad_seq(x, self.max_length)
# if self.scaler is None:
# self.scaler = []
# for i in range(x.shape[2]):
# self.scaler.append(StandardScaler().fit(x[:, :, i]))
# for i in range(x.shape[2]):
# x[:, :, i] = self.scaler[i].transform(x[:, :, i])
# feature scale
# if self.mean is None or self.std is None:
# self.mean = np.mean(x)
# self.std = np.std(x)
# x = (x - self.mean) / self.std
# s0, s1, s2 = x.shape[0], x.shape[1], x.shape[2]
# x = x.reshape(s0 * s1, s2)
# if not self.scaler:
# self.scaler = MinMaxScaler().fit(x)
# x = self.scaler.transform(x)
# x = x.reshape(s0, s1, s2)
# 4 dimension?
# (120, 437, 24) to (120, 437, 24, 1)
# 120 is the number of instance
# 437 is the max length
# 24 frame in mfcc
# log(f"max {np.max(x)} min {np.min(x)} mean {np.mean(x)}")
x = x[:, :, :, np.newaxis]
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=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