def gerador_de_audio():
files = []
for base in bases_B:
instances_list = []
rate_list = []
files.append(func.get_filenames(path=base, filetype='.wav'))
for i in range(len(files[-1])):
instance, rate = librosa.load(base + '/' + files[-1][i])
# filtragem e decomposicao
rec_signal = func.wavelet_filtering(instance, th)
instances_list.append(rec_signal)
rate_list.append(rate)
print('Done with: ' + files[-1][i])
print('Done with: ' + base + '\n')
# mudanca p/ novo diretorio
new_path = base + "/filtered"
os.chdir(new_path)
for i in range(len(files[-1])):
# geracao de arquivo
# maxv = np.iinfo(np.int16).max
# librosa.output.write_wav(
# files[-1][i] + "_filtered_int16.wav", (instances_list[i] * maxv).astype(np.int16), rate_list[i]
# )
librosa.output.write_wav(files[-1][i] + "_filtered.wav",
instances_list[i], rate_list[i])
print('Done with audio: ' + files[-1][i])
print('Done with: ' + base + '\n')
def main_a():
files = []
all_features = []
for base in bases_A:
instances_list = []
features_list = []
filtered_instances_list = []
files.append(func.get_filenames(
path=base, filetype='.wav')) # gera array com nome dos audios
for i in range(len(files[-1])):
# instance, rate = func.load_sound_files(base + '/' + files[-1][i])
# rate, instance = wavfile.read(base + '/' + files[-1][i])
instance, rate = librosa.load(base + '/' + files[-1][i])
# [rate, instance] = audioBasicIO.readAudioFile(base + '/' + files[-1][i])
instances_list.append(instance)
# filtragem e decomposicao
rec_signal = func.wavelet_filtering(instance, th, rate)
filtered_instances_list.append(rec_signal)
# extracao de caracteriticas
features = func.extract_feature(instance, rate) # librosa
# features = audioFeatureExtraction.stFeatureExtraction(instance, rate, 0.5 * rate, 0.25 * rate)
# features = audioFeatureExtraction.mtFeatureExtraction(
# instance, rate, 0.5 * rate, 0.5 * rate,0.25 * rate, 0.25 * rate
# )
# rotulacao
if base == atraning_normal:
features.append("Normal")
elif base == atraning_mumur:
features.append("Murmur")
elif base == atraining_extrahls:
features.append("Extra Heart Sound")
elif base == atraining_artifact:
features.append("Artifact")
features_list.append(features)
all_features.append(features)
print('Done with: ' + files[-1][i])
# escritas no CVS
dir_db = os.path.basename(base)
func.write_csv('librosa_filtered_features_of_' + dir_db, features_list)
print('Done with: ' + base + '\n')
os.chdir(path_A)
func.write_csv("BaseA_librosa_filtered_features", all_features)
print 'Done!'
def main_pn():
files = []
labels = []
for base in bases_Physioef:
instances_list = []
features_list = []
files.append(func.get_filenames(path=base, filetype='.wav'))
labels.append(func.get_filenames(path=base, filetype='.hea'))
for i in range(len(files[-1])):
# instance, rate = func.load_sound_files(base + '/' + files[-1][i])
# rate, instance = wavfile.read(base + '/' + files[-1][i])
instance, rate = librosa.load(base + '/' + files[-1][i])
# filtragem e decomposicao
rec_signal = func.wavelet_filtering(instance, th)
instances_list.append(rec_signal)
# extracao de caracteriticas
features = func.extract_feature(rec_signal, rate)
# rotulacao physionet
label_object = open(base + '/' + labels[-1][i], 'r')
label = label_object.read().split('\n')[-2]
features.append(label)
# features = func.pyAudioAnalysis_features(recSignal, rate)
features_list.append(features)
print('Done with: ' + files[-1][i])
func.write_csv(path_Physio + base[-1] + '_filtered', features_list)
# dir_db = os.path.basename(base)
# func.write_csv('librosa_features_of_' + dir_db, features_list)
print('Done with: ' + base + '\n')
print 'Done!'
files = []
labels = []
file_paths = []
for base in bases_B:
instances = []
files.append(get_filenames(path=base, filetype='.wav'))
# labels.append(get_filenames(path=base, filetype='.hea'))
# a maneira mais correta de fazer seria dividir em dois loops
# assim o entendimento poderia ficar mais facil (?)
for i in range(len(files[-1])):
# instance, rate = load_sound_files(base + '/' + files[-1][i])
instance, rate = librosa.load(base + '/' + files[-1][i])
rec_signal = func.wavelet_filtering(instance, th=288)
plot_imagens(rec_signal, files[-1][i] + " filtered waveplot")
# plot_spectogram(rec_signal, files[-1][i] + ' filtered')
print (files[-1][i] + ' done!')
print('Done with: ' + base + '\n')
# print (base + '/' + files[-1][0])
# x, sr = librosa.load(base + '/' + files[-1][0])
# librosa.display.waveplot(x, sr=sr)
# plt.show()
# files = get_filenames(btraning_mumur, '.wav')
#
# # carrega arquivos de audio