/
main_compute_indices_from_dir
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/
main_compute_indices_from_dir
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__author__ = 'guyot'
#!/usr/bin/env python
"""
Compute and output acoustic indices from a directory
"""
__author__ = "Patrice Guyot"
__version__ = "0.1"
__credits__ = ["Patrice Guyot"]
__email__ = ["patrice.guyot@mines-ales.fr"]
__status__ = "Development"
from compute_indice import *
from acoustic_index import *
import yaml
from scipy import signal
from csv import writer
import argparse
import os
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("config_file", help='yaml config file', nargs='?', const='yaml/config_014_butter.yaml', default='yaml/config_014_butter.yaml', type=str)
parser.add_argument("audio_dir", help='audio directory', nargs='?', const='audio_files', default='audio_files', type=str)
parser.add_argument("output_csv_file", help='output csv file', nargs='?', const='dict_all.csv', default='dict_all.csv', type=str)
args =parser.parse_args()
#Set config file
yml_file = args.config_file
print("Config file: ", yml_file)
with open(yml_file, 'r') as stream:
data_config = yaml.load(stream, Loader=yaml.FullLoader)
print("audio directory: ", args.audio_dir)
print("output_csv_file: ", args.output_csv_file)
# Get audio files
all_audio_file_path = []
for path, subdirs, files in os.walk(args.audio_dir):
for name in files :
if name.endswith(".wav") and not name.startswith("."):
all_audio_file_path.append(os.path.join(path, name))
print("-", len(all_audio_file_path), "files found in the directory", args.audio_dir,':\n')
for idx_file, filename in enumerate(all_audio_file_path):
# Read signal -------------------------------------
file = AudioFile(filename, verbose=True)
# Pre-processing -----------------------------------------------------------------------------------
if 'Filtering' in data_config:
if data_config['Filtering']['type'] == 'butterworth':
print('- Pre-processing - High-Pass Filtering:', data_config['Filtering'])
freq_filter = data_config['Filtering']['frequency']
Wn = freq_filter/float(file.niquist)
order = data_config['Filtering']['order']
[b,a] = signal.butter(order, Wn, btype='highpass')
# to plot the frequency response
#w, h = signal.freqz(b, a, worN=2000)
#plt.plot((file.sr * 0.5 / np.pi) * w, abs(h))
#plt.show()
file.process_filtering(signal.filtfilt(b, a, file.sig_float))
elif data_config['Filtering']['type'] == 'windowed_sinc':
print('- Pre-processing - High-Pass Filtering:', data_config['Filtering'])
freq_filter = data_config['Filtering']['frequency']
fc = freq_filter / float(file.sr)
roll_off = data_config['Filtering']['roll_off']
b = roll_off / float(file.sr)
N = int(np.ceil((4 / b)))
if not N % 2: N += 1 # Make sure that N is odd.
n = np.arange(N)
# Compute a low-pass filter.
h = np.sinc(2 * fc * (n - (N - 1) / 2.))
w = np.blackman(N)
h = h * w
h = h / np.sum(h)
# Create a high-pass filter from the low-pass filter through spectral inversion.
h = -h
h[(N - 1) / 2] += 1
file.process_filtering(np.convolve(file.sig_float, h))
# Compute Indices -----------------------------------------------------------------------------------
print('- Compute Indices')
ci = data_config['Indices'] # use to simplify the notation
for index_name in ci: # iterate over the index names (key of dictionary in the yml file)
if index_name == 'Acoustic_Complexity_Index':
print('\tCompute', index_name)
spectro, _ = compute_spectrogram(file, **ci[index_name]['spectro'])
methodToCall = globals().get(ci[index_name]['function'])
j_bin = int(ci[index_name]['arguments']['j_bin'] * file.sr / ci[index_name]['spectro']['windowHop']) # transform j_bin in samples
main_value, temporal_values = methodToCall(spectro, j_bin)
file.indices[index_name] = Index(index_name, temporal_values=temporal_values, main_value=main_value)
elif index_name == 'Acoustic_Diversity_Index':
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
freq_band_Hz = ci[index_name]['arguments']['max_freq'] / ci[index_name]['arguments']['freq_step']
windowLength = int(file.sr / freq_band_Hz)
spectro,_ = compute_spectrogram(file, windowLength=windowLength, windowHop= windowLength, scale_audio=True, square=False, windowType='hanning', centered=False, normalized= False )
main_value = methodToCall(spectro, freq_band_Hz, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'Acoustic_Evenness_Index':
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
freq_band_Hz = ci[index_name]['arguments']['max_freq'] / ci[index_name]['arguments']['freq_step']
windowLength = int(file.sr / freq_band_Hz)
spectro,_ = compute_spectrogram(file, windowLength=windowLength, windowHop= windowLength, scale_audio=True, square=False, windowType='hanning', centered=False, normalized= False )
main_value = methodToCall(spectro, freq_band_Hz, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'Bio_acoustic_Index':
print('\tCompute', index_name)
spectro, frequencies = compute_spectrogram(file, **ci[index_name]['spectro'])
methodToCall = globals().get(ci[index_name]['function'])
main_value = methodToCall(spectro, frequencies, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'Normalized_Difference_Sound_Index':
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
main_value = methodToCall(file, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'RMS_energy':
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
temporal_values = methodToCall(file, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, temporal_values=temporal_values)
elif index_name == 'Spectral_centroid':
print('\tCompute', index_name)
spectro, frequencies = compute_spectrogram(file, **ci[index_name]['spectro'])
methodToCall = globals().get(ci[index_name]['function'])
temporal_values = methodToCall(spectro, frequencies)
file.indices[index_name] = Index(index_name, temporal_values=temporal_values)
elif index_name == 'Spectral_Entropy':
print('\tCompute', index_name)
spectro, _ = compute_spectrogram(file, **ci[index_name]['spectro'])
methodToCall = globals().get(ci[index_name]['function'])
main_value = methodToCall(spectro)
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'Temporal_Entropy':
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
main_value = methodToCall(file, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'ZCR':
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
temporal_values = methodToCall(file, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, temporal_values=temporal_values)
elif index_name == 'Wave_SNR':
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
values = methodToCall(file, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, values=values)
elif index_name == 'NB_peaks':
print('\tCompute', index_name)
spectro, frequencies = compute_spectrogram(file, **ci[index_name]['spectro'])
methodToCall = globals().get(ci[index_name]['function'])
main_value = methodToCall(spectro, frequencies, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'Acoustic_Diversity_Index_NR': # Acoustic_Diversity_Index with Noise Removed spectrograms
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
freq_band_Hz = ci[index_name]['arguments']['max_freq'] / ci[index_name]['arguments']['freq_step']
windowLength = int(file.sr / freq_band_Hz)
spectro,_ = compute_spectrogram(file, windowLength=windowLength, windowHop= windowLength, scale_audio=True, square=False, windowType='hanning', centered=False, normalized= False )
spectro_noise_removed = remove_noiseInSpectro(spectro, **ci[index_name]['remove_noiseInSpectro'])
main_value = methodToCall(spectro_noise_removed, freq_band_Hz, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'Acoustic_Evenness_Index_NR': # Acoustic_Evenness_Index with Noise Removed spectrograms
print('\tCompute', index_name)
methodToCall = globals().get(ci[index_name]['function'])
freq_band_Hz = ci[index_name]['arguments']['max_freq'] / ci[index_name]['arguments']['freq_step']
windowLength = int(file.sr / freq_band_Hz)
spectro,_ = compute_spectrogram(file, windowLength=windowLength, windowHop= windowLength, scale_audio=True, square=False, windowType='hanning', centered=False, normalized= False )
spectro_noise_removed = remove_noiseInSpectro(spectro, **ci[index_name]['remove_noiseInSpectro'])
main_value = methodToCall(spectro_noise_removed, freq_band_Hz, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'Bio_acoustic_Index_NR': # Bio_acoustic_Index with Noise Removed spectrograms
print('\tCompute', index_name)
spectro, frequencies = compute_spectrogram(file, **ci[index_name]['spectro'])
spectro_noise_removed = remove_noiseInSpectro(spectro, **ci[index_name]['remove_noiseInSpectro'])
methodToCall = globals().get(ci[index_name]['function'])
main_value = methodToCall(spectro_noise_removed, frequencies, **ci[index_name]['arguments'])
file.indices[index_name] = Index(index_name, main_value=main_value)
elif index_name == 'Spectral_Entropy_NR': # Spectral_Entropy with Noise Removed spectrograms
print('\tCompute', index_name)
spectro, _ = compute_spectrogram(file, **ci[index_name]['spectro'])
spectro_noise_removed = remove_noiseInSpectro(spectro, **ci[index_name]['remove_noiseInSpectro'])
methodToCall = globals().get(ci[index_name]['function'])
main_value = methodToCall(spectro_noise_removed)
file.indices[index_name] = Index(index_name, main_value=main_value)
# Output Indices -----------------------------------------------------------------------------------
if idx_file == 0:
with open(args.output_csv_file, 'w') as f_object:
writer_object = writer(f_object)
keys = ['filename']
values = [file.file_name]
for idx, current_index in file.indices.items():
for key, value in current_index.__dict__.items():
if key != 'name':
keys.append(idx + '__' + key)
values.append(value)
writer_object.writerow(keys)
writer_object.writerow(values)
f_object.close()
else:
with open(args.output_csv_file, 'a') as f_object:
writer_object = writer(f_object)
values = [file.file_name]
for idx, current_index in file.indices.items():
for key, value in current_index.__dict__.items():
if key != 'name':
values.append(value)
writer_object.writerow(values)
f_object.close()
print("\n")