def fileRegression(inputFile, model_name, model_type, feats=["gfcc", "mfcc"]):
# Load classifier:
if not os.path.isfile(inputFile):
print("fileClassification: wav file not found!")
return (-1, -1, -1)
regression_models = glob.glob(model_name + "_*")
regression_models2 = []
for r in regression_models:
if r[-5::] != "MEANS":
regression_models2.append(r)
regression_models = regression_models2
regression_names = []
for r in regression_models:
regression_names.append(r[r.rfind("_") + 1::])
# FEATURE EXTRACTION
# LOAD ONLY THE FIRST MODEL (for mt_win, etc)
if model_type == 'svm' or model_type == "svm_rbf" or model_type == 'randomforest':
[_, _, _, mt_win, mt_step, st_win, st_step,
compute_beat] = load_model(regression_models[0], True)
[Fs, x] = audioBasicIO.readAudioFile(
inputFile) # read audio file and convert to mono
x = audioBasicIO.stereo2mono(x)
# feature extraction:
[mt_features, s, _] = aF.mtFeatureExtraction(x, Fs, mt_win * Fs,
mt_step * Fs,
round(Fs * st_win),
round(Fs * st_step), feats)
mt_features = mt_features.mean(
axis=1) # long term averaging of mid-term statistics
if compute_beat:
[beat, beatConf] = aF.beatExtraction(s, st_step)
mt_features = numpy.append(mt_features, beat)
mt_features = numpy.append(mt_features, beatConf)
# REGRESSION
R = []
for ir, r in enumerate(regression_models):
if not os.path.isfile(r):
print("fileClassification: input model_name not found!")
return (-1, -1, -1)
if model_type == 'svm' or model_type == "svm_rbf" \
or model_type == 'randomforest':
[model, MEAN, STD, mt_win, mt_step, st_win, st_step, compute_beat] = \
load_model(r, True)
curFV = (mt_features - MEAN) / STD # normalization
R.append(regressionWrapper(model, model_type, curFV)) # classification
return R, regression_names
def fileClassification(inputFile,
model_name,
model_type,
feats=["gfcc", "mfcc"]):
# Load classifier:
if not os.path.isfile(model_name):
print("fileClassification: input model_name not found!")
return (-1, -1, -1)
if not os.path.isfile(inputFile):
print("fileClassification: wav file not found!")
return (-1, -1, -1)
if model_type == 'knn':
[
classifier, MEAN, STD, classNames, mt_win, mt_step, st_win,
st_step, compute_beat
] = load_model_knn(model_name)
else:
[
classifier, MEAN, STD, classNames, mt_win, mt_step, st_win,
st_step, compute_beat
] = load_model(model_name)
[Fs, x] = audioBasicIO.readAudioFile(
inputFile) # read audio file and convert to mono
x = audioBasicIO.stereo2mono(x)
if isinstance(x, int): # audio file IO problem
return (-1, -1, -1)
if x.shape[0] / float(Fs) <= mt_win:
return (-1, -1, -1)
# feature extraction:
[mt_features, s, _] = aF.mtFeatureExtraction(x, Fs, mt_win * Fs,
mt_step * Fs,
round(Fs * st_win),
round(Fs * st_step), feats)
mt_features = mt_features.mean(
axis=1) # long term averaging of mid-term statistics
if compute_beat:
[beat, beatConf] = aF.beatExtraction(s, st_step)
mt_features = numpy.append(mt_features, beat)
mt_features = numpy.append(mt_features, beatConf)
curFV = (mt_features - MEAN) / STD # normalization
[Result, P] = classifierWrapperHead(classifier, model_type,
curFV) # classification
return Result, P, classNames