def test_shift_invariance(self, x_wav, actual_label):
"""
test model against shift invariance
"""
# init lists
pred_label_list, probs = [], []
# feature extraction
x, _ = self.feature_extractor.extract_mfcc(
x_wav, reduce_to_best_onset=False
) if self.net_handler.nn_arch != 'wavenet' else (x_wav[np.newaxis, :],
0)
# windowed
x_win = np.squeeze(
view_as_windows(
x, self.data_size, step=self.cfg_tb['shift_frame_step']),
axis=(0,
1)) if self.net_handler.nn_arch != 'wavenet' else np.squeeze(
view_as_windows(x,
self.data_size,
step=self.cfg_tb['shift_frame_step'] *
self.feature_extractor.hop),
axis=0)
for i, x in enumerate(x_win):
# classify
y_hat, o, pred_label = self.net_handler.classify_sample(x)
# append predicted label
pred_label_list.append(pred_label)
probs.append(float(o[0, self.class_dict[actual_label]]))
# plot
time_s = frames_to_sample(i * self.cfg_tb['shift_frame_step'],
self.feature_params['fs'],
self.feature_extractor.hop)
time_e = frames_to_sample(
i * self.cfg_tb['shift_frame_step'] +
self.feature_params['frame_size'], self.feature_params['fs'],
self.feature_extractor.hop)
# plot waveform
if self.cfg_tb['enable_plot']:
plot_waveform(x_wav[time_s:time_e],
self.feature_params['fs'],
title='frame{} actual: [{}] pred: [{}]'.format(
i, actual_label, pred_label),
plot_path=self.paths['shift_wavs'],
name='{}_frame{}'.format(actual_label, i))
# correct list
corrects = [int(actual_label == l) for l in pred_label_list]
# print message
print("test bench shift acc: ", np.sum(corrects) / len(corrects))
return corrects, probs
def create_sets(self):
"""
cut and copy recorded wavs
"""
print("wav: ", self.wav_folders)
# get all .wav files
raw_wavs = glob(self.dataset_path + '*' + self.dataset_cfg['file_ext'])
# get all wav files and save them
for i, wav in enumerate(raw_wavs):
print("wav: ", wav)
# filename extraction
file_name, file_index, label = self.file_naming_extraction(wav, file_ext=self.dataset_cfg['file_ext'])
# read audio from file
x, _ = librosa.load(wav, sr=self.feature_params['fs'])
# calc onsets
onsets = calc_onsets(x, self.feature_params['fs'], N=self.N, hop=self.hop, adapt_frames=5, adapt_alpha=0.09, adapt_beta=0.8)
onsets = self.clean_onsets(onsets)
# cut examples to one second
x_cut = self.cut_signal(x, onsets, time=1, alpha=0.4)
# plot onsets
plot_onsets(x, self.feature_params['fs'], self.N, self.hop, onsets, title=label, plot_path=self.plot_paths['onsets'], name='onsets_{}'.format(label))
for j, xj in enumerate(x_cut):
# plot
plot_waveform(xj, self.feature_params['fs'], title='{}-{}'.format(label, j), plot_path=self.plot_paths['waveform'], name='example_{}-{}'.format(label, j))
# save file
soundfile.write('{}{}{}.wav'.format(self.wav_folders[0], label, j), xj, self.feature_params['fs'], subtype=None, endian=None, format=None, closefd=True)
def show_waveform_colors(x, cmaps):
"""
waveform colors
"""
for i, cmap in enumerate(cmaps):
# plot weight matrices
fig = plot_waveform(x, 16000, e=None, cmap=cmap, hop=None, onset_frames=None, title='none', xlim=None, ylim=None, plot_path=None, name='None', show_plot=False)
# positioning
if i >= 3: i, j = i%3, 600
else: i, j = i, 0
fig.canvas.manager.window.setGeometry(i*600, j, 600, 500)
def showcase_wavs(cfg,
raw_plot=True,
spec_plot=True,
mfcc_plot=True,
show_plot=False):
"""
showcase wavs
"""
# plot path
plot_path = '../docu/thesis/3_signal/figs/'
# change params
feature_params = cfg['feature_params'].copy()
feature_params['n_ceps_coeff'] = 32
feature_params['norm_features'] = True
# init feature extractor
feature_extractor = FeatureExtractor(feature_params)
# wav, anno dir
wav_dir, anno_dir = '../ignore/my_recordings/showcase_wavs/', '../ignore/my_recordings/showcase_wavs/annotation/'
# analyze some wavs
for wav, anno in zip(glob(wav_dir + '*.wav'),
glob(anno_dir + '*.TextGrid')):
# info
print("\nwav: ", wav), print("anno: ", anno)
# load file
x, _ = librosa.load(wav, sr=feature_params['fs'])
# raw waveform
if raw_plot:
plot_waveform(x,
feature_params['fs'],
anno_file=anno,
hop=feature_extractor.hop,
plot_path=plot_path,
name='signal_raw_' +
wav.split('/')[-1].split('.')[0] + '_my',
show_plot=show_plot)
# spectogram
if spec_plot:
plot_spec_profile(x,
feature_extractor.calc_spectogram(x).T,
feature_params['fs'],
feature_extractor.N,
feature_extractor.hop,
anno_file=anno,
plot_path=plot_path,
title=wav.split('/')[-1].split('.')[0] + '_my',
name='signal_spec-lin_' +
wav.split('/')[-1].split('.')[0] + '_my',
show_plot=show_plot)
plot_spec_profile(x,
feature_extractor.calc_spectogram(x).T,
feature_params['fs'],
feature_extractor.N,
feature_extractor.hop,
log_scale=True,
anno_file=anno,
plot_path=plot_path,
title=wav.split('/')[-1].split('.')[0] + '_my',
name='signal_spec-log_' +
wav.split('/')[-1].split('.')[0] + '_my',
show_plot=show_plot)
# mfcc
if mfcc_plot:
mfcc, bon_pos = feature_extractor.extract_mfcc(
x, reduce_to_best_onset=False)
plot_mfcc_profile(x,
cfg['feature_params']['fs'],
feature_extractor.N,
feature_extractor.hop,
mfcc,
anno_file=anno,
sep_features=True,
bon_pos=bon_pos,
frame_size=cfg['feature_params']['frame_size'],
plot_path=plot_path,
name='signal_mfcc_' +
wav.split('/')[-1].split('.')[0] + '_my',
close_plot=False,
show_plot=show_plot)
command = mic.update_read_command()
# check if command
if command is not None:
# print command
print("command: ", command)
# clear queue
mic.clear_mic_queue()
# some prints
print("x_all: ", mic.collector.x_all.shape)
print("e_all: ", mic.collector.e_all.shape)
print("on_all: ", mic.collector.on_all.shape)
# plot waveform
plot_waveform(mic.collector.x_all,
cfg['feature_params']['fs'],
e=mic.collector.e_all * 10,
hop=mic.hop,
onset_frames=mic.collector.on_all,
title='input stream',
ylim=(-1, 1),
plot_path=None,
name='None',
show_plot=True)
# save audio
mic.save_audio_file()
# check if command
if command is not None:
# print command
print("command: ", command)
# clear queue
mic.clear_mic_queue()
# some prints
print("x_all: ", mic.collector.x_all.shape)
print("e_all: ", mic.collector.e_all.shape)
print("on_all: ", mic.collector.on_all.shape)
# plot waveform
plot_waveform(mic.collector.x_all,
cfg['feature_params']['fs'],
mic.collector.e_all * 10,
hop,
mic.collector.on_all,
title='input stream',
ylim=(-1, 1),
plot_path=None,
name='None')
# save audio
mic.save_audio_file()
# show plots
plt.show()
def extract_raw_data(self, wavs, annos, n_examples, set_name=None):
"""
raw data extraction
"""
# raw data: [n x m], labels and index
raw_data, label_data, target_data, index_data = np.empty(shape=(0, self.channel_size, self.raw_frame_size), dtype=np.float64), [], np.empty(shape=(0, self.raw_frame_size), dtype=np.int64), []
# extract class wavs
for class_wavs, class_annos in zip(wavs, annos):
# class annotation file names extraction
class_annos_file_names = [l + i for f, i, l in [self.file_naming_extraction(a, file_ext='.TextGrid') for a in class_annos]]
# number of class examples
num_class_examples = 0
# run through each example in class wavs
for wav in class_wavs:
# extract file namings
file_name, file_index, label = self.file_naming_extraction(wav, file_ext=self.dataset_cfg['file_ext'])
# get annotation if available
anno = class_annos[class_annos_file_names.index(label + file_index)] if label + file_index in class_annos_file_names else None
# load and pre-process audio
x, wav_is_useless = self.wav_pre_processing(wav)
if wav_is_useless: continue
# print some info
if self.verbose: print("wav: [{}] with label: [{}], samples=[{}], time=[{}]s".format(wav, label, len(x), len(x) / self.feature_params['fs']))
# extract raw samples from region of energy
raw, bon_pos = self.feature_extractor.get_best_raw_samples(x)
# add dither and do normalization
raw = self.wav_post_processing(raw)
# quantize data
t = self.feature_extractor.quantize(raw)
# plot waveform
if self.dataset_cfg['enable_plot']: plot_waveform(x, self.feature_params['fs'], bon_samples=[bon_pos, bon_pos+self.raw_frame_size], title=label + file_index, plot_path=self.plot_paths['waveform'], name=label + file_index, show_plot=False, close_plot=True)
# collect wavs
if self.collect_wavs: self.pre_wavs.append((librosa.util.normalize(x), label + str(file_index) + '_' + set_name, bon_pos / self.hop))
# add to mfcc_data container
raw_data = np.vstack((raw_data, raw[np.newaxis, :]))
target_data = np.vstack((target_data, t))
label_data.append(label)
index_data.append(label + file_index)
# update number of examples per class
num_class_examples += 1
# stop if desired examples are reached
if num_class_examples >= n_examples: break
return raw_data, label_data, target_data, index_data
# load audio
x, _ = librosa.load(wav, sr=16000)
# feature extraction
mfcc, bon_pos = feature_extractor.extract_mfcc(x, reduce_to_best_onset=False)
print("mfcc: ", mfcc.shape)
# invert mfcc
x_hat = feature_extractor.invert_mfcc(np.squeeze(mfcc))
# save invert mfcc
soundfile.write(wav.split('.wav')[0] + '_inv_mfcc.wav', x_hat, 16000, subtype=None, endian=None, format=None, closefd=True)
print("x_hat: ", x_hat.shape)
plot_mfcc_profile(x, 16000, feature_extractor.N, feature_extractor.hop, mfcc, anno_file=anno, sep_features=True, diff_plot=False, bon_pos=bon_pos, frame_size=cfg['feature_params']['frame_size'], plot_path=wav_dir, name=wav.split('/')[-1].split('.')[0], show_plot=False, close_plot=False)
plot_waveform(x, 16000, anno_file=anno, hop=feature_extractor.hop, title=wav.split('/')[-1].split('.')[0]+'_my', plot_path=wav_dir, name=wav.split('/')[-1].split('.')[0], show_plot=False)
# random
#x = np.random.randn(16000)
#mfcc, bon_pos = feature_extractor.extract_mfcc(x, reduce_to_best_onset=False)
#plot_mfcc_profile(x, 16000, feature_extractor.N, feature_extractor.hop, mfcc, bon_pos=bon_pos, name='rand', show_plot=True)
def test_noise_invariance(self, x_wav, actual_label, mu=0):
"""
test model against noise invariance
"""
# init lists
pred_label_list, probs = [], []
# origin
if self.cfg_tb['enable_plot']:
plot_waveform(x_wav,
self.feature_params['fs'],
title='origin actual: [{}]'.format(actual_label),
plot_path=self.paths['shift_wavs'],
name='{}_origin'.format(actual_label))
# test model with different snr values
for snr in self.cfg_tb['snrs']:
# signal power
p_x_eff = x_wav @ x_wav.T / len(x_wav)
# calculate noise signal power
sigma = np.sqrt(p_x_eff / (10**(snr / 10)))
# noise generation
n = np.random.normal(mu, sigma, len(x_wav))
# add noise
x_noise = x_wav + n
# noise signal power
p_n_eff = n @ n.T / len(n)
# print energy info
# print("sigma: ", sigma), print("p_x: ", p_x_eff), print("p_n: ", p_n_eff), print("db: ", 10 * np.log10(p_x_eff / p_n_eff))
# feature extraction
#x_mfcc, _ = self.feature_extractor.extract_mfcc(x_noise, reduce_to_best_onset=True)
# feature extraction
x, _ = self.feature_extractor.extract_mfcc(
x_noise, reduce_to_best_onset=True
) if self.net_handler.nn_arch != 'wavenet' else self.feature_extractor.get_best_raw_samples(
x_noise, add_channel_dim=True)
# classify
y_hat, o, pred_label = self.net_handler.classify_sample(x)
# append predicted label and probs
pred_label_list.append(pred_label)
probs.append(float(o[0, self.class_dict[actual_label]]))
# plot wavs
if self.cfg_tb['enable_plot']:
plot_waveform(x_noise,
self.feature_params['fs'],
title='snr: [{}] actual: [{}] pred: [{}]'.format(
snr, actual_label, pred_label),
plot_path=self.paths['noise_wavs'],
name='{}_snr{}'.format(actual_label, snr))
# correct list
corrects = [int(actual_label == l) for l in pred_label_list]
# print message
print("test bench noise acc: ", np.sum(corrects) / len(corrects))
return corrects, probs
