def DTW_multiple_alignment(Sref, S):
cost = dtw(Sref, S)
L = len(Sref)
alignment = [None] * len(Sref)
i, j = len(Sref) - 1, len(S) - 1
while i >= 0 and j >= 0:
if alignment[i] != None:
alignment[i].extend([S[j]])
else:
alignment[i] = [S[j]]
if i == 0:
j = j - 1
elif j == 0:
i = i - 1
else:
score = min(cost[i - 1][j - 1], cost[i][j - 1], cost[i - 1][j])
if score == cost[i - 1][j - 1]:
i = i - 1
j = j - 1
elif score == cost[i - 1][j]:
i = i - 1
else:
j = j - 1
return alignment
def sliding_window(sequence, templates, threshold):
matches = {}
sliding_window_size = 1 #sec
step_size = 50 * (sliding_window_size / 4) # 50 Hz, 25% overlap
for i in range(len(sequence), step_size):
segment = sequence[i:i + step_size]
for template in templates:
dist = dtw(segment, template)[0]
if dist < threshold:
matches[segment] = template
# print(k)
word1 = get_formants("Search.wav")
counter = 0
speaker = ""
recorded_word = ""
found = 0
with open("Database.txt", "r") as f:
for line in f:
if counter % 3 == 0:
speaker = line
elif counter % 3 == 1:
recorded_word = line
else:
word2 = numpy.loadtxt(line[:-1])
warnings.simplefilter("ignore")
dist = dtw(word1, word2, euclidean_distances)
if dist < 0.485:
found = 1
print("Prepoznao rec ", recorded_word,
" koju je izgovorio ", speaker,
" a razdaljina iznosi: ", dist)
counter += 1
if found == 0:
print("Nije prepoznao rec")
elif option == "Multisearch":
record_to_file("Search.wav")
k = split("Search.wav")
print("Recording finished, search in progress")
print(k)
word1 = get_formants("Search.wav")
counter = 0
def test_dtw_path(s1, s2):
assert (dtw(s1, s2, None, None)[1] == fastdtw.dtw(s1, s2)[1])
print "Test path passed"
def test_dtw_distance(s1, s2):
assert (dtw(s1, s2, None, None)[0] == fastdtw.dtw(s1, s2)[0])
print "Test distance passed"
from DTW import dtw
import fastdtw
import numpy as np
x = np.array([1, 2, 3, 4, 5], dtype='float')
y = np.array([1, 2, 3, 4, 5], dtype='float')
window = []
print fastdtw.dtw(x, y)
print dtw(x, y, None, None)
def test_dtw_distance(s1, s2):
assert (dtw(s1, s2, None, None)[0] == fastdtw.dtw(s1, s2)[0])
print "Test distance passed"
def test_dtw_path(s1, s2):
assert (dtw(s1, s2, None, None)[1] == fastdtw.dtw(s1, s2)[1])
print "Test path passed"
test_dtw_distance(x, y)
test_dtw_path(x, y)
x = np.array([1, 2, 3, 4, 5], dtype='float')
y = np.array([2, 3, 4], dtype='float')
test_dtw_distance(x, y)
test_dtw_path(x, y)
x = np.array([[1, 2, 3, 4, 5]], dtype='float')
y1, sr1 = librosa.load(ref)
y2, sr2 = librosa.load(test1)
y3, sr3 = librosa.load(test2)
y_harmonic1, y_percussive1 = librosa.effects.hpss(y1)
y_harmonic2, y_percussive2 = librosa.effects.hpss(y2)
y_harmonic3, y_percussive3 = librosa.effects.hpss(y3)
mfcc1 = librosa.feature.mfcc(y=y_percussive1, sr=sr1, n_mfcc=13)
mfcc2 = librosa.feature.mfcc(y=y_percussive2, sr=sr2, n_mfcc=13)
mfcc3 = librosa.feature.mfcc(y=y_percussive3, sr=sr3, n_mfcc=13)
x = np.amax(mfcc1, axis=1)
y = np.amax(mfcc2, axis=1)
z = np.amax(mfcc3, axis=1)
x_matrix = np.array(dtw(x, y))
y_matrix = np.array(dtw(x, z))
print(x_matrix)
print()
print(y_matrix)
print()
print("The distance between", ref[ref.find(start) + len(start):ref.rfind(end)],
"and", test1[test1.find(start) + len(start):test1.rfind(end)],
"is equal to", x_matrix[x_matrix.shape[0] - 1, x_matrix.shape[1] - 1])
print("The distance between", ref[ref.find(start) + len(start):ref.rfind(end)],
"and", test2[ref.find(start) + len(start):test2.rfind(end)],
"is equal to", y_matrix[y_matrix.shape[0] - 1, y_matrix.shape[1] - 1])