def fileRegression(inputFile, modelName, modelType):
# Load classifier:
if not os.path.isfile(inputFile):
print("fileClassification: wav file not found!")
return (-1, -1, -1)
regressionModels = glob.glob(modelName + "_*")
regressionModels2 = []
for r in regressionModels:
if r[-5::] != "MEANS":
regressionModels2.append(r)
regressionModels = regressionModels2
regressionNames = []
for r in regressionModels:
regressionNames.append(r[r.rfind("_") + 1::])
# FEATURE EXTRACTION
# LOAD ONLY THE FIRST MODEL (for mtWin, etc)
if modelType == 'svm' or modelType == "svm_rbf":
[_, _, _, mtWin, mtStep, stWin, stStep,
computeBEAT] = loadSVModel(regressionModels[0], True)
elif modelType == 'randomforest':
[_, _, _, mtWin, mtStep, stWin, stStep,
computeBEAT] = loadRandomForestModel(regressionModels[0], True)
[Fs, x] = audio_basic_io.read_audio_file(
inputFile) # read audio file and convert to mono
x = audio_basic_io.stereo2mono(x)
# feature extraction:
[MidTermFeatures, s] = aF.mt_feature_extraction(x, Fs, mtWin * Fs,
mtStep * Fs,
round(Fs * stWin),
round(Fs * stStep))
MidTermFeatures = MidTermFeatures.mean(
axis=1) # long term averaging of mid-term statistics
if computeBEAT:
[beat, beatConf] = aF.beatExtraction(s, stStep)
MidTermFeatures = numpy.append(MidTermFeatures, beat)
MidTermFeatures = numpy.append(MidTermFeatures, beatConf)
# REGRESSION
R = []
for ir, r in enumerate(regressionModels):
if not os.path.isfile(r):
print("fileClassification: input modelName not found!")
return (-1, -1, -1)
if modelType == 'svm' or modelType == "svm_rbf":
[Model, MEAN, STD, mtWin, mtStep, stWin, stStep,
computeBEAT] = loadSVModel(r, True)
elif modelType == 'randomforest':
[Model, MEAN, STD, mtWin, mtStep, stWin, stStep,
computeBEAT] = loadRandomForestModel(r, True)
curFV = (MidTermFeatures - MEAN) / STD # normalization
R.append(regressionWrapper(Model, modelType, curFV)) # classification
return R, regressionNames
def hmmSegmentation(wavFileName, hmmModelName, PLOT=False, gtFileName=""):
[Fs, x] = audio_basic_io.read_audio_file(wavFileName) # read audio data
try:
fo = open(hmmModelName, "rb")
except IOError:
print("didn't find file")
return
try:
hmm = cPickle.load(fo)
classesAll = cPickle.load(fo)
mtWin = cPickle.load(fo)
mtStep = cPickle.load(fo)
except:
fo.close()
fo.close()
# Features = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050*Fs, 0.050*Fs); # feature extraction
[Features, _] = aF.mt_feature_extraction(x, Fs, mtWin * Fs, mtStep * Fs, round(Fs * 0.050), round(Fs * 0.050))
flagsInd = hmm.predict(Features.T) # apply model
# for i in range(len(flagsInd)):
# if classesAll[flagsInd[i]]=="silence":
# flagsInd[i]=classesAll.index("speech")
# plot results
if os.path.isfile(gtFileName):
[segStart, segEnd, segLabels] = readSegmentGT(gtFileName)
flagsGT, classNamesGT = segs2flags(segStart, segEnd, segLabels, mtStep)
flagsGTNew = []
for j, fl in enumerate(flagsGT): # "align" labels with GT
if classNamesGT[flagsGT[j]] in classesAll:
flagsGTNew.append(classesAll.index(classNamesGT[flagsGT[j]]))
else:
flagsGTNew.append(-1)
CM = np.zeros((len(classNamesGT), len(classNamesGT)))
flagsIndGT = np.array(flagsGTNew)
for i in range(min(flagsInd.shape[0], flagsIndGT.shape[0])):
CM[int(flagsIndGT[i]), int(flagsInd[i])] += 1
else:
flagsIndGT = np.array([])
acc = plotSegmentationResults(flagsInd, flagsIndGT, classesAll, mtStep, not PLOT)
if acc >= 0:
print("Overall Accuracy: {0:.2f}".format(acc))
return (flagsInd, classNamesGT, acc, CM)
else:
return (flagsInd, classesAll, -1, -1)
def trainHMM_fromFile(wavFile, gtFile, hmmModelName, mtWin, mtStep):
'''
This function trains a HMM model for segmentation-classification using a single annotated audio file
ARGUMENTS:
- wavFile: the path of the audio filename
- gtFile: the path of the ground truth filename
(a csv file of the form <segment start in seconds>,<segment end in seconds>,<segment label> in each row
- hmmModelName: the name of the HMM model to be stored
- mtWin: mid-term window size
- mtStep: mid-term window step
RETURNS:
- hmm: an object to the resulting HMM
- classNames: a list of classNames
After training, hmm, classNames, along with the mtWin and mtStep values are stored in the hmmModelName file
'''
[segStart, segEnd, segLabels] = readSegmentGT(gtFile) # read ground truth data
flags, classNames = segs2flags(segStart, segEnd, segLabels, mtStep) # convert to fix-sized sequence of flags
[Fs, x] = audio_basic_io.read_audio_file(wavFile) # read audio data
# F = aF.stFeatureExtraction(x, Fs, 0.050*Fs, 0.050*Fs);
[F, _] = aF.mt_feature_extraction(x, Fs, mtWin * Fs, mtStep * Fs, round(Fs * 0.050),
round(Fs * 0.050)) # feature extraction
startprob, transmat, means, cov = trainHMM_computeStatistics(F,
flags) # compute HMM statistics (priors, transition matrix, etc)
hmm = hmmlearn.hmm.GaussianHMM(startprob.shape[0], "diag") # hmm training
hmm.startprob_ = startprob
hmm.transmat_ = transmat
hmm.means_ = means
hmm.covars_ = cov
fo = open(hmmModelName, "wb") # output to file
cPickle.dump(hmm, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(classNames, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mtWin, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mtStep, fo, protocol=cPickle.HIGHEST_PROTOCOL)
fo.close()
return hmm, classNames
def speaker_diarization(file_name, num_speaker, mt_size=2.0,
mt_step=0.2, st_win=0.05, st_step=0.025,
lda_dim=35,
plot=False):
'''
ARGUMENTS:
- fileName: the name of the WAV file to be analyzed
- numOfSpeakers the number of speakers (clusters) in the recording (<=0 for unknown)
- mtSize (opt) mid-term window size
- mtStep (opt) mid-term window step
- stWin (opt) short-term window size
- LDAdim (opt) LDA dimension (0 for no LDA)
- PLOT (opt) 0 for not plotting the results 1 for plottingy
'''
fr, x = audio_basic_io.read_audio_file(file_name)
x = audio_basic_io.stereo2mono(x)
duration = len(x) / fr
classifier1, mean1, std1, class_names1, mt_win1, mt_step1, st_win1, st_step1, compute_beta1 = aT.loadKNNModel(
os.path.join("data", "knnSpeakerAll"))
classifier2, mean2, std2, class_names2, mt_win2, mt_step2, st_win2, st_step2, compute_beta2 = aT.loadKNNModel(
os.path.join("data", "knnSpeakerFemaleMale"))
mid_term_features, short_term_features = aF.mt_feature_extraction(signal=x,
fr=fr,
mt_win=mt_size * fr,
mt_step=mt_step * fr,
st_win=round(fr * st_win),
st_step=round(fr * st_step))
# (68, 329) (34, 2630)
print(mid_term_features.shape, short_term_features.shape)
mid_term_features2 = np.zeros((mid_term_features.shape[0] + len(class_names1) + len(class_names2),
mid_term_features.shape[1]))
for i in range(mid_term_features.shape[1]):
cur_f1 = (mid_term_features[:, i] - mean1) / std1
cur_f2 = (mid_term_features[:, i] - mean2) / std2
result, p1 = aT.classifierWrapper(classifier1, "knn", cur_f1)
result, p2 = aT.classifierWrapper(classifier2, "knn", cur_f2)
mid_term_features2[0:mid_term_features.shape[0], i] = mid_term_features[:, i]
mid_term_features2[mid_term_features.shape[0]:mid_term_features.shape[0] + len(class_names1), i] = p1 + 0.0001
mid_term_features2[mid_term_features.shape[0] + len(class_names1)::, i] = p2 + 0.0001
mid_term_features = mid_term_features2 # TODO
# SELECT FEATURES:
# iFeaturesSelect = [8,9,10,11,12,13,14,15,16,17,18,19,20]; # SET 0A
# iFeaturesSelect = [8,9,10,11,12,13,14,15,16,17,18,19,20, 99,100]; # SET 0B
# iFeaturesSelect = [8,9,10,11,12,13,14,15,16,17,18,19,20, 68,69,70,71,72,73,
# 74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,
# 97,98, 99,100]; # SET 0C
i_features_select = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 41,
42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53] # SET 1A
# iFeaturesSelect = [8,9,10,11,12,13,14,15,16,17,18,19,20,41,42,43,44,45,46,47,48,49,50,51,52,53, 99,100]; # SET 1B
# iFeaturesSelect = [8,9,10,11,12,13,14,15,16,17,18,19,20,41,42,43,44,45,46,47,
# 48,49,50,51,52,53, 68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,
# 87,88,89,90,91,92,93,94,95,96,97,98, 99,100]; # SET 1C
# iFeaturesSelect = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,34,35,
# 36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53]; # SET 2A
# iFeaturesSelect = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,34,35,
# 36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53, 99,100]; # SET 2B
# iFeaturesSelect = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,34,35,
# 36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53, 68,69,70,71,72,73,74,75,
# 76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98, 99,100]; # SET 2C
# iFeaturesSelect = range(100); # SET 3
# MidTermFeatures += np.random.rand(MidTermFeatures.shape[0], MidTermFeatures.shape[1]) * 0.000000010
mid_term_features = mid_term_features[i_features_select, :]
mid_term_features_norm, mean, std = aT.normalizeFeatures([mid_term_features.T])
mid_term_features_norm = mid_term_features_norm[0].T
num_of_windows = mid_term_features.shape[1]
# remove outliers:
distances_all = np.sum(distance.squareform(distance.pdist(mid_term_features_norm.T)), axis=0)
m_distances_all = np.mean(distances_all)
i_non_out_liers = np.nonzero(distances_all < 1.2 * m_distances_all)[0]
# TODO: Combine energy threshold for outlier removal:
# EnergyMin = np.min(MidTermFeatures[1,:])
# EnergyMean = np.mean(MidTermFeatures[1,:])
# Thres = (1.5*EnergyMin + 0.5*EnergyMean) / 2.0
# iNonOutLiers = np.nonzero(MidTermFeatures[1,:] > Thres)[0]
# print(iNonOutLiers
# per_out_lier = (100.0 * (num_of_windows - i_non_out_liers.shape[0])) / num_of_windows
mid_term_features_norm_or = mid_term_features_norm
mid_term_features_norm = mid_term_features_norm[:, i_non_out_liers]
# LDA dimensionality reduction:
if lda_dim > 0:
mt_win_ratio = int(round(mt_size / st_win))
mt_step_ratio = int(round(st_win / st_win))
mt_features_to_reduce = []
num_of_features = len(short_term_features)
num_of_statistics = 2
# for i in range(numOfStatistics * numOfFeatures + 1):
for i in range(num_of_statistics * num_of_features):
mt_features_to_reduce.append([])
for i in range(num_of_features): # for each of the short-term features:
cur_pos = 0
n = len(short_term_features[i])
while cur_pos < n:
n1 = cur_pos
n2 = cur_pos + mt_win_ratio
if n2 > n:
n2 = n
cur_st_features = short_term_features[i][n1:n2]
mt_features_to_reduce[i].append(np.mean(cur_st_features))
mt_features_to_reduce[i + num_of_features].append(np.std(cur_st_features))
cur_pos += mt_step_ratio
mt_features_to_reduce = np.array(mt_features_to_reduce)
mt_features_to_reduce2 = np.zeros((mt_features_to_reduce.shape[0] + len(class_names1) + len(class_names2),
mt_features_to_reduce.shape[1]))
for i in range(mt_features_to_reduce.shape[1]):
cur_f1 = (mt_features_to_reduce[:, i] - mean1) / std1
cur_f2 = (mt_features_to_reduce[:, i] - mean2) / std2
result, p1 = aT.classifierWrapper(classifier1, "knn", cur_f1)
result, p2 = aT.classifierWrapper(classifier2, "knn", cur_f2)
mt_features_to_reduce2[0:mt_features_to_reduce.shape[0], i] = mt_features_to_reduce[:, i]
mt_features_to_reduce2[mt_features_to_reduce.shape[0]:mt_features_to_reduce.shape[0] + len(class_names1),
i] = p1 + 0.0001
mt_features_to_reduce2[mt_features_to_reduce.shape[0] + len(class_names1)::, i] = p2 + 0.0001
mt_features_to_reduce = mt_features_to_reduce2
mt_features_to_reduce = mt_features_to_reduce[i_features_select, :]
# mtFeaturesToReduce += np.random.rand(mtFeaturesToReduce.shape[0], mtFeaturesToReduce.shape[1]) * 0.0000010
mt_features_to_reduce, mean, std = aT.normalizeFeatures([mt_features_to_reduce.T])
mt_features_to_reduce = mt_features_to_reduce[0].T
# DistancesAll = np.sum(distance.squareform(distance.pdist(mtFeaturesToReduce.T)), axis=0)
# MDistancesAll = np.mean(DistancesAll)
# iNonOutLiers2 = np.nonzero(DistancesAll < 3.0*MDistancesAll)[0]
# mtFeaturesToReduce = mtFeaturesToReduce[:, iNonOutLiers2]
labels = np.zeros((mt_features_to_reduce.shape[1],))
lda_step = 1.0
lda_step_ratio = lda_step / st_win
# print(LDAstep, LDAstepRatio
for i in range(labels.shape[0]):
labels[i] = int(i * st_win / lda_step_ratio)
clf = sklearn.discriminant_analysis.LinearDiscriminantAnalysis(n_components=lda_dim)
clf.fit(mt_features_to_reduce.T, labels)
mid_term_features_norm = (clf.transform(mid_term_features_norm.T)).T
if num_speaker <= 0:
s_range = range(2, 10)
else:
s_range = [num_speaker]
cls_all = []
sil_all = []
centers_all = []
# (26, 314)
print('mid_term_features_norm', mid_term_features_norm.shape)
for i_speakers in s_range:
k_means = sklearn.cluster.KMeans(n_clusters=i_speakers)
k_means.fit(mid_term_features_norm.T)
cls = k_means.labels_
means = k_means.cluster_centers_
# Y = distance.squareform(distance.pdist(MidTermFeaturesNorm.T))
cls_all.append(cls)
centers_all.append(means)
sil_a = []
sil_b = []
for c in range(i_speakers): # for each speaker (i.e. for each extracted cluster)
cluster_percent = np.nonzero(cls == c)[0].shape[0] / float(len(cls))
if cluster_percent < 0.020:
sil_a.append(0.0)
sil_b.append(0.0)
else:
mid_term_features_norm_temp = mid_term_features_norm[:, cls == c] # get subset of feature vectors
# compute average distance between samples that belong to the cluster (a values)
yt = distance.pdist(mid_term_features_norm_temp.T)
sil_a.append(np.mean(yt) * cluster_percent)
sil_bs = []
for c2 in range(i_speakers): # compute distances from samples of other clusters
if c2 != c:
cluster_percent2 = np.nonzero(cls == c2)[0].shape[0] / float(len(cls))
mid_term_features_norm_temp2 = mid_term_features_norm[:, cls == c2]
yt = distance.cdist(mid_term_features_norm_temp.T, mid_term_features_norm_temp2.T)
sil_bs.append(np.mean(yt) * (cluster_percent + cluster_percent2) / 2.0)
sil_bs = np.array(sil_bs)
# ... and keep the minimum value (i.e. the distance from the "nearest" cluster)
sil_b.append(min(sil_bs))
sil_a = np.array(sil_a)
sil_b = np.array(sil_b)
sil = []
for c in range(i_speakers): # for each cluster (speaker)
sil.append((sil_b[c] - sil_a[c]) / (max(sil_b[c], sil_a[c]) + 0.00001)) # compute silhouette
sil_all.append(np.mean(sil)) # keep the AVERAGE SILLOUETTE
# silAll = silAll * (1.0/(np.power(np.array(sRange),0.5)))
imax = np.argmax(sil_all) # position of the maximum sillouette value
n_speakers_final = s_range[imax] # optimal number of clusters
# generate the final set of cluster labels
# (important: need to retrieve the outlier windows:
# this is achieved by giving them the value of their nearest non-outlier window)
cls = np.zeros((num_of_windows,))
for i in range(num_of_windows):
j = np.argmin(np.abs(i - i_non_out_liers))
cls[i] = cls_all[imax][j]
# Post-process method 1: hmm smoothing
for i in range(1):
startprob, transmat, means, cov = trainHMM_computeStatistics(mid_term_features_norm_or, cls)
hmm = hmmlearn.hmm.GaussianHMM(startprob.shape[0], "diag") # hmm training
hmm.startprob_ = startprob
hmm.transmat_ = transmat
hmm.means_ = means
hmm.covars_ = cov
cls = hmm.predict(mid_term_features_norm_or.T)
# Post-process method 2: median filtering:
cls = scipy.signal.medfilt(cls, 13)
cls = scipy.signal.medfilt(cls, 11)
sil = sil_all[imax] # final sillouette
class_names = ["speaker{0:d}".format(c) for c in range(n_speakers_final)]
# load ground-truth if available
gt_file = file_name.replace('.wav', '.segments') # open for annotated file
if os.path.isfile(gt_file): # if groundturh exists
seg_start, seg_end, seg_labels = readSegmentGT(gt_file) # read GT data
flags_gt, class_names_gt = segs2flags(seg_start, seg_end, seg_labels, mt_step) # convert to flags
x = np.arange(len(cls)) * mt_step + mt_step / 2.0
if plot:
fig = plt.figure()
if num_speaker > 0:
ax1 = fig.add_subplot(111)
else:
ax1 = fig.add_subplot(211)
ax1.set_yticks(np.array(range(len(class_names))))
ax1.axis((0, duration, -1, len(class_names)))
ax1.set_yticklabels(class_names)
ax1.plot(x, cls)
if os.path.isfile(gt_file):
if plot:
ax1.plot(np.array(range(len(flags_gt))) * mt_step + mt_step / 2.0, flags_gt, 'r')
purity_cluster_mean, purity_speaker_mean = evaluateSpeakerDiarization(cls, flags_gt)
print("{0:.1f}\t{1:.1f}".format(100 * purity_cluster_mean, 100 * purity_speaker_mean))
if plot:
plt.title("Cluster purity: {0:.1f}% - Speaker purity: {1:.1f}%".format(100 * purity_cluster_mean,
100 * purity_speaker_mean))
if plot:
plt.xlabel("time (seconds)")
# print(sRange, silAll)
if num_speaker <= 0:
plt.subplot(212)
plt.plot(s_range, sil_all)
plt.xlabel("number of clusters")
plt.ylabel("average clustering's sillouette")
plt.show()
return x, cls
def mtFileClassification(inputFile, modelName, modelType, plotResults=False, gtFile=""):
'''
This function performs mid-term classification of an audio stream.
Towards this end, supervised knowledge is used, i.e. a pre-trained classifier.
ARGUMENTS:
- inputFile: path of the input WAV file
- modelName: name of the classification model
- modelType: svm or knn depending on the classifier type
- plotResults: True if results are to be plotted using matplotlib along with a set of statistics
RETURNS:
- segs: a sequence of segment's endpoints: segs[i] is the endpoint of the i-th segment (in seconds)
- classes: a sequence of class flags: class[i] is the class ID of the i-th segment
'''
if not os.path.isfile(modelName):
print("mtFileClassificationError: input modelType not found!")
return (-1, -1, -1, -1)
# Load classifier:
if (modelType == 'svm') or (modelType == 'svm_rbf'):
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadSVModel(modelName)
elif modelType == 'knn':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadKNNModel(modelName)
elif modelType == 'randomforest':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadRandomForestModel(
modelName)
elif modelType == 'gradientboosting':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadGradientBoostingModel(
modelName)
elif modelType == 'extratrees':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadExtraTreesModel(
modelName)
if computeBEAT:
print("Model " + modelName + " contains long-term music features (beat etc) and cannot be used in segmentation")
return (-1, -1, -1, -1)
[Fs, x] = audio_basic_io.read_audio_file(inputFile) # load input file
if Fs == -1: # could not read file
return (-1, -1, -1, -1)
x = audio_basic_io.stereo2mono(x) # convert stereo (if) to mono
Duration = len(x) / Fs
# mid-term feature extraction:
[MidTermFeatures, _] = aF.mt_feature_extraction(x, Fs, mtWin * Fs, mtStep * Fs, round(Fs * stWin),
round(Fs * stStep))
flags = []
Ps = []
flagsInd = []
for i in range(MidTermFeatures.shape[1]): # for each feature vector (i.e. for each fix-sized segment):
curFV = (MidTermFeatures[:, i] - MEAN) / STD # normalize current feature vector
[Result, P] = aT.classifierWrapper(Classifier, modelType, curFV) # classify vector
flagsInd.append(Result)
flags.append(classNames[int(Result)]) # update class label matrix
Ps.append(np.max(P)) # update probability matrix
flagsInd = np.array(flagsInd)
# 1-window smoothing
for i in range(1, len(flagsInd) - 1):
if flagsInd[i - 1] == flagsInd[i + 1]:
flagsInd[i] = flagsInd[i + 1]
(segs, classes) = flags2segs(flags, mtStep) # convert fix-sized flags to segments and classes
segs[-1] = len(x) / float(Fs)
# Load grount-truth:
if os.path.isfile(gtFile):
[segStartGT, segEndGT, segLabelsGT] = readSegmentGT(gtFile)
flagsGT, classNamesGT = segs2flags(segStartGT, segEndGT, segLabelsGT, mtStep)
flagsIndGT = []
for j, fl in enumerate(flagsGT): # "align" labels with GT
if classNamesGT[flagsGT[j]] in classNames:
flagsIndGT.append(classNames.index(classNamesGT[flagsGT[j]]))
else:
flagsIndGT.append(-1)
flagsIndGT = np.array(flagsIndGT)
CM = np.zeros((len(classNamesGT), len(classNamesGT)))
for i in range(min(flagsInd.shape[0], flagsIndGT.shape[0])):
CM[int(flagsIndGT[i]), int(flagsInd[i])] += 1
else:
CM = []
flagsIndGT = np.array([])
acc = plotSegmentationResults(flagsInd, flagsIndGT, classNames, mtStep, not plotResults)
if acc >= 0:
print("Overall Accuracy: {0:.3f}".format(acc))
return (flagsInd, classNamesGT, acc, CM)
else:
return (flagsInd, classNames, acc, CM)
def trainHMM_fromDir(dirPath, hmmModelName, mtWin, mtStep):
'''
This function trains a HMM model for segmentation-classification using a where WAV files and .segment (ground-truth files) are stored
ARGUMENTS:
- dirPath: the path of the data diretory
- hmmModelName: the name of the HMM model to be stored
- mtWin: mid-term window size
- mtStep: mid-term window step
RETURNS:
- hmm: an object to the resulting HMM
- classNames: a list of classNames
After training, hmm, classNames, along with the mtWin and mtStep values are stored in the hmmModelName file
'''
flagsAll = np.array([])
classesAll = []
for i, f in enumerate(glob.glob(dirPath + os.sep + '*.wav')): # for each WAV file
wavFile = f
gtFile = f.replace('.wav', '.segments') # open for annotated file
if not os.path.isfile(gtFile): # if current WAV file does not have annotation -> skip
continue
[segStart, segEnd, segLabels] = readSegmentGT(gtFile) # read GT data
flags, classNames = segs2flags(segStart, segEnd, segLabels, mtStep) # convert to flags
for c in classNames: # update classnames:
if c not in classesAll:
classesAll.append(c)
[Fs, x] = audio_basic_io.read_audio_file(wavFile) # read audio data
[F, _] = aF.mt_feature_extraction(x, Fs, mtWin * Fs, mtStep * Fs, round(Fs * 0.050),
round(Fs * 0.050)) # feature extraction
lenF = F.shape[1]
lenL = len(flags)
MIN = min(lenF, lenL)
F = F[:, 0:MIN]
flags = flags[0:MIN]
flagsNew = []
for j, fl in enumerate(flags): # append features and labels
flagsNew.append(classesAll.index(classNames[flags[j]]))
flagsAll = np.append(flagsAll, np.array(flagsNew))
if i == 0:
Fall = F
else:
Fall = np.concatenate((Fall, F), axis=1)
startprob, transmat, means, cov = trainHMM_computeStatistics(Fall, flagsAll) # compute HMM statistics
hmm = hmmlearn.hmm.GaussianHMM(startprob.shape[0], "diag") # train HMM
hmm.startprob_ = startprob
hmm.transmat_ = transmat
hmm.means_ = means
hmm.covars_ = cov
fo = open(hmmModelName, "wb") # save HMM model
cPickle.dump(hmm, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(classesAll, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mtWin, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mtStep, fo, protocol=cPickle.HIGHEST_PROTOCOL)
fo.close()
return hmm, classesAll
def fileClassification(inputFile, modelName, modelType):
# Load classifier:
if not os.path.isfile(modelName):
print("fileClassification: input modelName not found!")
return (-1, -1, -1)
if not os.path.isfile(inputFile):
print("fileClassification: wav file not found!")
return (-1, -1, -1)
if (modelType) == 'svm' or (modelType == 'svm_rbf'):
[
Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep,
computeBEAT
] = loadSVModel(modelName)
elif modelType == 'knn':
[
Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep,
computeBEAT
] = loadKNNModel(modelName)
elif modelType == 'randomforest':
[
Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep,
computeBEAT
] = loadRandomForestModel(modelName)
elif modelType == 'gradientboosting':
[
Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep,
computeBEAT
] = loadGradientBoostingModel(modelName)
elif modelType == 'extratrees':
[
Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep,
computeBEAT
] = loadExtraTreesModel(modelName)
[Fs, x] = audio_basic_io.read_audio_file(
inputFile) # read audio file and convert to mono
x = audio_basic_io.stereo2mono(x)
if isinstance(x, int): # audio file IO problem
return (-1, -1, -1)
if x.shape[0] / float(Fs) <= mtWin:
return (-1, -1, -1)
# feature extraction:
[MidTermFeatures, s] = aF.mt_feature_extraction(x, Fs, mtWin * Fs,
mtStep * Fs,
round(Fs * stWin),
round(Fs * stStep))
MidTermFeatures = MidTermFeatures.mean(
axis=1) # long term averaging of mid-term statistics
if computeBEAT:
[beat, beatConf] = aF.beatExtraction(s, stStep)
MidTermFeatures = numpy.append(MidTermFeatures, beat)
MidTermFeatures = numpy.append(MidTermFeatures, beatConf)
curFV = (MidTermFeatures - MEAN) / STD # normalization
[Result, P] = classifierWrapper(Classifier, modelType,
curFV) # classification
return Result, P, classNames
def recordAnalyzeAudio(duration, outputWavFile, midTermBufferSizeSec, modelName, modelType):
'''
recordAnalyzeAudio(duration, outputWavFile, midTermBufferSizeSec, modelName, modelType)
This function is used to record and analyze audio segments, in a fix window basis.
ARGUMENTS:
- duration total recording duration
- outputWavFile path of the output WAV file
- midTermBufferSizeSec (fix)segment length in seconds
- modelName classification model name
- modelType classification model type
'''
if modelType == 'svm':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadSVModel(modelName)
elif modelType == 'knn':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadKNNModel(modelName)
else:
Classifier = None
inp = alsaaudio.PCM(alsaaudio.PCM_CAPTURE, alsaaudio.PCM_NONBLOCK)
inp.setchannels(1)
inp.setrate(Fs)
inp.setformat(alsaaudio.PCM_FORMAT_S16_LE)
inp.setperiodsize(512)
midTermBufferSize = int(midTermBufferSizeSec * Fs)
allData = []
midTermBuffer = []
curWindow = []
count = 0
while len(allData) < duration * Fs:
# Read data from device
l, data = inp.read()
if l:
for i in range(l):
curWindow.append(audioop.getsample(data, 2, i))
if (len(curWindow) + len(midTermBuffer) > midTermBufferSize):
samplesToCopyToMidBuffer = midTermBufferSize - len(midTermBuffer)
else:
samplesToCopyToMidBuffer = len(curWindow)
midTermBuffer = midTermBuffer + curWindow[0:samplesToCopyToMidBuffer];
del (curWindow[0:samplesToCopyToMidBuffer])
if len(midTermBuffer) == midTermBufferSize:
count += 1
if Classifier != None:
[mtFeatures, stFeatures] = aF.mt_feature_extraction(midTermBuffer, Fs, 2.0 * Fs, 2.0 * Fs, 0.020 * Fs,
0.020 * Fs)
curFV = (mtFeatures[:, 0] - MEAN) / STD;
[result, P] = aT.classifierWrapper(Classifier, modelType, curFV)
print(classNames[int(result)])
allData = allData + midTermBuffer
plt.clf()
plt.plot(midTermBuffer)
plt.show(block=False)
plt.draw()
midTermBuffer = []
allDataArray = numpy.int16(allData)
wavfile.write(outputWavFile, Fs, allDataArray)
