def train_classifier(dataset, n_classes, Fs=16000):
datalist = os.listdir(dataset)
n_data_set = len(datalist)
for i in range(n_data_set):
filepath = dataset + '/' + datalist[i]
if datalist[i].find('.wav') == 1:
try:
[x, Fs, n_channels, n_samples] = read_wave(filepath)
except:
print(e.msg)
elif datalist[i].find('.raw'):
try:
x = read_raw(filepath)
if len(x.shape) > 1:
n_channels = x.shape[1]
n_samples = x.shape[0]
else:
n_channels = 1
n_samples = len(x)
except:
print(e.msg)
features = get_mfcc(x, Fs)
dimension = features.shape[1]
size = features.shape[0]
emHMM_algorithm(features, dimension, 2, size)
def train_classifier(dataset,n_classes,Fs=16000):
datalist = os.listdir(dataset)
n_data_set = len(datalist)
for i in range(n_data_set) :
filepath = dataset + '/' + datalist[i]
if datalist[i].find('.wav')==1 :
try:
[x, Fs, n_channels, n_samples] = read_wave(filepath)
except:
print(e.msg)
elif datalist[i].find('.raw') :
try:
x = read_raw(filepath)
if len(x.shape)>1:
n_channels = x.shape[1]
n_samples = x.shape[0]
else:
n_channels = 1
n_samples = len(x)
except:
print(e.msg)
features = get_mfcc(x,Fs)
dimension = features.shape[1]
size = features.shape[0]
emHMM_algorithm(features,dimension,2,size)
def main():
if dimension==1 :
# gmm = np.zeros(number_of_components*size)
# mu = np.zeros(number_of_components)
# sigma = np.zeros(number_of_components)
# for i in range(number_of_components) :
# gmm[i*size:(i+1)*size], mu[i], sigma[i] = create_data(dimension,size,i)
gmm = np.zeros((1,number_of_components*size),dtype=float)
mu = np.zeros((number_of_components,1),dtype=float)
sigma = np.zeros((number_of_components,1,1),dtype=float)
matrix = np.zeros((number_of_components,number_of_components),dtype=float)
# for i in range(number_of_components):
# x, mu[i,0], sigma[i,0,0] = create_data(dimension,size,i)
else:
gmm = np.zeros((dimension,number_of_components*size),dtype=float)
mu = np.zeros((number_of_components,dimension),dtype=float)
sigma = np.zeros((number_of_components,dimension,dimension),dtype=float)
matrix = np.zeros((number_of_components,number_of_components),dtype=float)
# for i in range(number_of_components):
# x, mu[i,:], sigma[i,:,:] = create_data(dimension,size,i)
weights = np.array([0.6, 0.4])
matrix = np.array([[0.7, 0.3], [0.1, 0.9]])
model = hmm.GaussianHMM(2, "full", weights, matrix)
model.means_ = mu
model.covars_ = sigma
gmm, Z = model.sample(number_of_components*size)
# else :
# gmm = np.zeros((dimension,number_of_components*size))
# mu = np.zeros((number_of_components,dimension))
# sigma = np.zeros((number_of_components,dimension,dimension))
# for i in range(number_of_components) :
# gmm[:,i*size:(i+1)*size], mu[i,:], sigma[i,:,:] = create_data(dimension,size,i)
means, variances, pi, a = emHMM_algorithm(gmm,dimension,number_of_components,number_of_components*size)
# num_bins = 50
# n, bins, patches = plt.hist(gmm, num_bins, normed=1, facecolor='green', alpha=0.5)
# # add a 'best fit' line
# for i in range(number_of_components) :
# y = mlab.normpdf(bins, means[i], variances[i])
# plt.plot(bins, y, 'r--')
# plt.xlabel('Values')
# plt.ylabel('Probability')
# plt.title('Data Histogram vs predicted distribution')
#
# # Tweak spacing to prevent clipping of ylabel
# plt.subplots_adjust(left=0.15)
# plt.show()
b = np.zeros((number_of_components,number_of_components*size))
#Evaluate posterior
if dimension==1:
for i in range(number_of_components) :
# Calculate the probability of seeing the observation given each state
pdf = pi[i]*mlab.normpdf(gmm, means[i], variances[i,0])
b[i,:] = pdf[:,0]
else:
centered_data = np.zeros((number_of_components,number_of_components*size,dimension))
den = np.zeros((number_of_components,number_of_components*size))
num = np.zeros((number_of_components,number_of_components*size))
for i in range(number_of_components) :
# Calculate the probability of seeing the observation given each state
for n in range(number_of_components*size):
centered_data[i, n, :] = gmm[n, :]-means[i, :]
den[i,n] = np.sqrt((2*math.pi)**(dimension)*np.linalg.det(variances[i,:,:]))
num[i,n] = np.exp((-1/2)*np.dot(np.dot(centered_data[i,n,:][np.newaxis],np.linalg.inv(variances[i,:,:])),centered_data[i,n,:][:,np.newaxis]))
b[i,n] = num[i,n] / den[i,n]
# Predict
path, x, y = viterbi(size*number_of_components,a,b,pi)
plt.figure();
plt.plot(path[0,:],'ro')
plt.plot(path[0,:],'r')
plt.plot(Z,'g')
plt.show()
if dimension==1:
print "initial means: ", mu[:,0], "\n", "initial variances: ", sigma[:,0,0], "\n", "initial weights: ", weights, "\n"
print "means:", means, "\n" "sigmas:", variances, "\n", "weights:", pi, "\n"
print "initial mixing mgmmatrix:", matrix, "\n"
print "mixing matrix:", a, "\n"
else:
print "initial means: ", mu, "\n", "initial variances: ", sigma, "\n", "initial weights: ", weights, "\n"
print "means:", means, "\n" "sigmas:", variances, "\n", "weights:", pi, "\n"
print "initial mixing matrix:", matrix, "\n"
print "mixing matrix:", a, "\n"