def spectral_rolloff(data): return feature_extraction.spectral_rolloff(data, 0.9)
def reload_and_feature(picall, feature_type, average, nmel, order_frft, nmfcc,
saveprojectpath, savedata, savepic, savetestdata,
savepreprocess, savefeature, path, downsample_rate,
frame_time, frame_length, frame_overlap, test_rate):
'''
fe.stft, # 0
fe.zero_crossing_rate, # 1
fe.energy, # 2
fe.entropy_of_energy, # 3
fe.spectral_centroid_spread, # 4
fe.spectral_entropy, # 5
fe.spectral_flux, # 6
fe.spectral_rolloff, # 7
fe.bandwidth, # 8
fe.mfccs, # 9
fe.rms # 10
fe.stfrft # 11
fe.frft_mfcc # 12
'''
labelname = os.listdir(path) # 获取该数据集路径下的子文件名
if not os.path.exists(savefeature):
os.mkdir(savefeature) # 创建保存特征结果的文件
for i in range(len(labelname)):
if not os.path.exists(savefeature + '\\' + labelname[i]):
os.mkdir(savefeature + '\\' + labelname[i])
datafile = open(savepreprocess, encoding='utf-8') # 读取预处理结果
csv_reader = csv.reader(datafile) # 以这种方式读取文件得到的结果是一个迭代器
feature_set = [] # 当使用统计量作为特征时，将所有样本的特征缓存入该变量以进行归一化
for row in csv_reader: # row中的元素是字符类型
time_series = np.array(row[2:]).astype(
'float32') # row的前两个元素分别是标签和对应文件次序
#######################################################################
frames = preprocessing.frame(time_series, frame_length,
frame_overlap) # 分帧
f, t, stft = fe.stft(time_series,
pic=None,
fs=downsample_rate,
nperseg=frame_length,
noverlap=frame_length - frame_overlap,
nfft=8192,
boundary=None,
padded=False)
# if stft.shape[1] != frames.shape[1]: # 防止stft的时间个数和帧的个数不一样
# dim = min(stft.shape[1], frames.shape[1])
# stft = stft[:, 0:dim]
# frames = frames[:, 0:dim]
# Mel = lib.feature.melspectrogram(S=np.abs(stft), sr=downsample_rate, n_fft=2*(stft.shape[0]-1), n_mels=512)
feature_list = [] # 用于存放各种类型的特征，每个帧对应一个特征向量，其元素分别是每种类型的特征
if picall: # 用于绘图控制
pic = savepic + '\\' + row[0] + '_' + row[1]
else:
pic = None
for i in feature_type:
if i == 0:
feature0 = np.abs(stft)
feature_list.append(feature0)
elif i == 1:
feature1 = fe.zero_crossing_rate(frames, pic=pic)
feature_list.append(feature1)
elif i == 2:
feature2 = fe.energy(frames, pic=pic)
feature_list.append(feature2)
elif i == 3:
feature3 = fe.entropy_of_energy(frames, pic=pic)
feature_list.append(feature3)
elif i == 4:
feature4, feature41 = fe.spectral_centroid_spread(
stft, downsample_rate, pic=pic)
feature_list.append(feature4)
feature_list.append(feature41)
elif i == 5:
feature5 = fe.spectral_entropy(stft, pic=pic)
feature_list.append(feature5)
elif i == 6:
feature6 = fe.spectral_flux(stft, pic=pic)
feature_list.append(feature6)
elif i == 7:
feature7 = fe.spectral_rolloff(stft,
0.85,
downsample_rate,
pic=pic)
feature_list.append(feature7)
elif i == 8:
feature8 = fe.bandwidth(stft, f, pic=pic)
feature_list.append(feature8)
elif i == 9:
feature9 = fe.mfccs(
X=stft,
fs=downsample_rate,
# nfft=2*(stft.shape[0]-1),
nfft=8192,
n_mels=nmel,
n_mfcc=nmfcc,
pic=pic)
feature_list.append(feature9)
elif i == 10:
feature10 = fe.rms(stft, pic=pic)
feature_list.append(feature10)
elif i == 11:
feature11 = fe.stfrft(frames,
p=order_frft[int(row[0])],
pic=pic)
feature_list.append(feature11)
elif i == 12:
tmp = fe.stfrft(frames, p=order_frft[int(row[0])])
feature12 = fe.frft_MFCC(S=tmp,
fs=downsample_rate,
n_mfcc=nmfcc,
n_mels=nmel,
pic=pic)
feature_list.append(feature12)
elif i == 13:
feature13, feature13_ = fe.fundalmental_freq(
frames=frames, fs=downsample_rate, pic=pic)
feature_list.append(feature13)
elif i == 14:
feature14 = fe.chroma_stft(S=stft,
n_chroma=12,
A440=440.0,
ctroct=5.0,
octwidth=2,
base_c=True,
norm=2)
feature_list.append(feature14)
elif i == 15:
feature15 = fe.log_attack_time(x=time_series,
lower_ratio=0.02,
upper_ratio=0.99,
fs=downsample_rate,
n=frames.shape[1])
feature_list.append(feature15)
elif i == 16:
feature16 = fe.temoporal_centroid(S=stft,
hop_length=frame_overlap,
fs=downsample_rate)
feature_list.append(feature16)
elif i == 17:
# harm_freq, harm_mag = fe.harmonics(nfft=8192, nht=0.15, f=f, S=stft, fs=downsample_rate, fmin=50, fmax=500, threshold=0.2)
# hsc = fe.harmonic_spectral_centroid(harm_freq, harm_mag)
# hsd = fe.harmonic_spectral_deviation(harm_mag)
# hss = fe.harmonic_spectral_spread(hsc, harm_freq, harm_mag)
# hsv = fe.harmonic_spectral_variation(harm_mag)
# feature17 = np.concatenate([hsc, hsd, hss, hsv], axis=0)
# feature_list.append(feature17)
harm_freq, harm_mag = timbral.harmonics(frames=frames,
fs=downsample_rate,
S=stft,
f=f,
nfft=8192,
fmin=50,
fmax=500,
nht=0.15)
hsc = timbral.harmonic_spectral_centroid(harm_freq, harm_mag)
hsd = timbral.harmonic_spectral_deviation(harm_mag)
hss = timbral.harmonic_spectral_spread(hsc, harm_freq,
harm_mag)
hsv = timbral.harmonic_spectral_variation(harm_mag)
feature17 = np.concatenate([hsc, hsd, hss, hsv], axis=0)
feature_list.append(feature17)
elif i == 18:
feature18 = fe.pitches_mag_CDSV(f=f,
S=stft,
fs=downsample_rate,
fmin=50,
fmax=downsample_rate / 2,
threshold=0.2)
feature_list.append(feature18)
elif i == 19:
feature19 = fe.delta_features(feature9, order=1)
feature_list.append(feature19)
elif i == 20:
feature20 = fe.delta_features(feature9, order=2)
feature_list.append(feature20)
features = np.concatenate([j for j in feature_list],
axis=0) # 我很欣赏这一句代码，将各种特征拼在一起
long = list(range(features.shape[1])) # 删除含有nan的帧
for t in long[::-1]:
if np.isnan(features[:, t]).any():
features = np.delete(features, t, 1)
if average: # 使用统计量作为特征
mean = np.mean(features, axis=1).reshape(
1, features.shape[0]) # 原来的特征向量是列向量，这里转成行向量
var = np.var(features, axis=1).reshape(1, features.shape[0])
# std = np.std(features, axis=1).reshape(1, features.shape[0])
# ske = np.zeros((1, features.shape[0]))
# kur = np.zeros((1, features.shape[0]))
# for n in range(features.shape[0]):
# ske[0, i] = sts.skewness(features[i, :])
# kur[0, i] = sts.kurtosis(features[i, :])
features = np.concatenate([
mean, var,
np.array([int(row[0]), int(row[1])]).reshape(1, 2)
],
axis=1) # 使用统计平均代替每个帧的特征
feature_set.append(features)
else:
scale = StandardScaler().fit(features)
features = scale.transform(features) # 进行归一化
csv_path = savefeature + '\\' + labelname[int(
row[0])] + '\\' + row[0] + '_' + row[1] + '.csv'
with open(csv_path, 'w', encoding='utf-8', newline='') as csvfile:
csv_writer = csv.writer(csvfile)
buffer = np.concatenate([
features.T,
int(row[0]) * np.ones((features.shape[1], 1)),
int(row[1]) * np.ones((features.shape[1], 1))
],
axis=1)
csv_writer.writerows(buffer)
print('featuring:', row[0], row[1])
datafile.close() # 关闭文件，避免不必要的错误
if average: # 使用统计量作为特征
features = np.concatenate([k for k in feature_set],
axis=0) # 我很欣赏这一句代码 行表示样本数，列表示特征数
tmp = features[:, -2:] # 防止归一化的时候把标签也归一化
features = features[:, 0:-2]
scale = StandardScaler().fit(features)
features = scale.transform(features) # 进行归一化
features = np.concatenate([features, tmp], axis=1) # 把之前分开的特征和标签拼在一起
for k in range(features.shape[0]):
csv_path = savefeature + '\\' + labelname[int(features[k, -2])] + \
'\\' + str(int(features[k, -2])) + '_' + str(int(features[k, -1])) + '.csv'
with open(csv_path, 'w', encoding='utf-8', newline='') as csvfile:
csv_writer = csv.writer(csvfile) # 每个音频文件只有一个特征向量，并存入一个csv文件
csv_writer.writerow(features[k, :]) # 注意这里写入的是一行，要用writerow
pic=None,
fs=fs,
nperseg=frame_length,
noverlap=frame_length - frame_lap,
nfft=8192,
boundary=None,
padded=False)
pic = None
feature1 = fe.zero_crossing_rate(frames, pic=pic)
feature2 = fe.energy(frames, pic=pic)
feature3 = fe.entropy_of_energy(frames, pic=pic)
feature4, feature41 = fe.spectral_centroid_spread(stft, fs, pic=pic)
feature5 = fe.spectral_entropy(stft, pic=pic)
feature6 = fe.spectral_flux(stft, pic=pic)
feature7 = fe.spectral_rolloff(stft, 0.85, fs, pic=pic)
feature8 = fe.bandwidth(stft, f, pic=pic)
feature9 = fe.mfccs(X=stft, fs=fs, nfft=8192, n_mels=128, n_mfcc=13, pic=pic)
feature10 = fe.rms(stft, pic=pic)
feature11 = fe.stfrft(frames, p=0.95, pic=pic)
tmp = fe.stfrft(frames, p=0.95)
feature12 = fe.frft_MFCC(S=tmp, fs=fs, n_mfcc=13, n_mels=128, pic=pic)
feature19 = fe.delta_features(feature9, order=1)
feature20 = fe.delta_features(feature9, order=2)
plt.figure()
ax1 = plt.subplot(411)
plt.plot(data)
ax1.set_ylabel('original signal')
ax2 = plt.subplot(412)
plt.plot(feature1[0, :])