def train_svm(paths,
mid_window,
mid_step,
short_window,
short_step,
model_name,
compute_beat=False,
train_percentage=0.90):
# STEP A: Feature Extraction:
[features, class_names,
_] = aF.multiple_directory_feature_extraction(paths,
mid_window,
mid_step,
short_window,
short_step,
compute_beat=compute_beat)
n_feats = features[0].shape[1]
feature_names = ["features" + str(d + 1) for d in range(n_feats)]
write_modearff_file(model_name, features, class_names, feature_names)
# STEP B: classifier Evaluation and Parameter Selection and get optimal classifeir parameter::
#
temp_features = []
for feat in features:
temp = []
for i in range(feat.shape[0]):
temp_fv = feat[i, :]
if (not np.isnan(temp_fv).any()) and (not np.isinf(temp_fv).any()):
temp.append(temp_fv.tolist())
else:
print("NaN Found! Feature vector not used for training")
temp_features.append(np.array(temp))
features = temp_features
best_param = aT.evaluate_classifier(features, class_names, 100, "svm",
step_params.container.get_svm(), 0,
train_percentage)
print("Selected params: {0:.5f}".format(best_param))
features_norm, mean, std = normalize_features(features)
mean = mean.tolist()
std = std.tolist()
# STEP C: Save the classifier to file
classifier = aT.train_svm(features_norm, best_param)
write_mode_file(model_name, classifier, mean, std, class_names, mid_window,
mid_step, short_window, short_step, compute_beat)
def silence_removal(signal,
sampling_rate,
st_win,
st_step,
smooth_window=0.5,
weight=0.5,
plot=False):
"""
Event Detection (silence removal)
ARGUMENTS:
- signal: the input audio signal
- sampling_rate: sampling freq
- st_win, st_step: window size and step in seconds
- smoothWindow: (optinal) smooth window (in seconds)
- weight: (optinal) weight factor (0 < weight < 1)
the higher, the more strict
- plot: (optinal) True if results are to be plotted
RETURNS:
- seg_limits: list of segment limits in seconds (e.g [[0.1, 0.9],
[1.4, 3.0]] means that
the resulting segments are (0.1 - 0.9) seconds
and (1.4, 3.0) seconds
"""
if weight >= 1:
weight = 0.99
if weight <= 0:
weight = 0.01
# Step 1: feature extraction
signal = audioBasicIO.stereo_to_mono(signal)
st_feats, _ = stf.feature_extraction(signal, sampling_rate,
st_win * sampling_rate,
st_step * sampling_rate)
# Step 2: train binary svm classifier of low vs high energy frames
# keep only the energy short-term sequence (2nd feature)
st_energy = st_feats[1, :]
en = np.sort(st_energy)
# number of 10% of the total short-term windows
st_windows_fraction = int(len(en) / 10)
# compute "lower" 10% energy threshold
low_threshold = np.mean(en[0:st_windows_fraction]) + 1e-15
# compute "higher" 10% energy threshold
high_threshold = np.mean(en[-st_windows_fraction:-1]) + 1e-15
# get all features that correspond to low energy
low_energy = st_feats[:, np.where(st_energy <= low_threshold)[0]]
# get all features that correspond to high energy
high_energy = st_feats[:, np.where(st_energy >= high_threshold)[0]]
# form the binary classification task and ...
features = [low_energy.T, high_energy.T]
# normalize and train the respective svm probabilistic model
# (ONSET vs SILENCE)
features_norm, mean, std = at.normalize_features(features)
svm = at.train_svm(features_norm, 1.0)
# Step 3: compute onset probability based on the trained svm
prob_on_set = []
for index in range(st_feats.shape[1]):
# for each frame
cur_fv = (st_feats[:, index] - mean) / std
# get svm probability (that it belongs to the ONSET class)
prob_on_set.append(svm.predict_proba(cur_fv.reshape(1, -1))[0][1])
prob_on_set = np.array(prob_on_set)
# smooth probability:
prob_on_set = smooth_moving_avg(prob_on_set, smooth_window / st_step)
# Step 4A: detect onset frame indices:
prog_on_set_sort = np.sort(prob_on_set)
# find probability Threshold as a weighted average
# of top 10% and lower 10% of the values
nt = int(prog_on_set_sort.shape[0] / 10)
threshold = (np.mean((1 - weight) * prog_on_set_sort[0:nt]) +
weight * np.mean(prog_on_set_sort[-nt::]))
max_indices = np.where(prob_on_set > threshold)[0]
# get the indices of the frames that satisfy the thresholding
index = 0
seg_limits = []
time_clusters = []
# Step 4B: group frame indices to onset segments
while index < len(max_indices):
# for each of the detected onset indices
cur_cluster = [max_indices[index]]
if index == len(max_indices) - 1:
break
while max_indices[index + 1] - cur_cluster[-1] <= 2:
cur_cluster.append(max_indices[index + 1])
index += 1
if index == len(max_indices) - 1:
break
index += 1
time_clusters.append(cur_cluster)
seg_limits.append(
[cur_cluster[0] * st_step, cur_cluster[-1] * st_step])
# Step 5: Post process: remove very small segments:
min_duration = 0.2
seg_limits_2 = []
for s_lim in seg_limits:
if s_lim[1] - s_lim[0] > min_duration:
seg_limits_2.append(s_lim)
seg_limits = seg_limits_2
if plot:
time_x = np.arange(0, signal.shape[0] / float(sampling_rate),
1.0 / sampling_rate)
plt.subplot(2, 1, 1)
plt.plot(time_x, signal)
for s_lim in seg_limits:
plt.axvline(x=s_lim[0], color='red')
plt.axvline(x=s_lim[1], color='red')
plt.subplot(2, 1, 2)
plt.plot(np.arange(0, prob_on_set.shape[0] * st_step, st_step),
prob_on_set)
plt.title('Signal')
for s_lim in seg_limits:
plt.axvline(x=s_lim[0], color='red')
plt.axvline(x=s_lim[1], color='red')
plt.title('svm Probability')
plt.show()
return seg_limits
