first commit

app.py

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+import librosa
+import librosa.display as display
+import matplotlib.pyplot as plt
+import math
+
+y1, sr1 = librosa.load('tp4.wav')
+y2, sr2 = librosa.load('t2.wav')
+
+def dot(A,B): 
+    return (sum(a*b for a,b in zip(A,B)))
+
+def cosine_similarity(a,b):
+    return dot(a,b) / ( (dot(a,a) **.5) * (dot(b,b) ** .5) )
+  
+def cosine_similarity1(v1,v2):
+    "compute cosine similarity of v1 to v2: (v1 dot v2)/{||v1||*||v2||)"
+    sumxx, sumxy, sumyy = 0, 0, 0
+    for i in range(len(v1)):
+        x = v1[i]; y = v2[i]
+        sumxx += x*x
+        sumyy += y*y
+        sumxy += x*y
+    return sumxy/math.sqrt(sumxx*sumyy)
+  
+
+
+#Showing multiple plots using subplot
+plt.subplot(1, 2, 1) 
+mfcc1 = librosa.feature.mfcc(y1,sr1)   #Computing MFCC values
+display.specshow(mfcc1)
+
+plt.subplot(1, 2, 2)
+mfcc2 = librosa.feature.mfcc(y2, sr2)
+display.specshow(mfcc2)
+
+from dtw import dtw
+from numpy.linalg import norm
+
+dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T, dist=lambda x, y: norm(x - y, ord=1))
+print('Normalized distance between the two sounds: '+ dist.__str__())
+
+# import numpy as np
+# array1 = np.array(mfcc1)
+# array2 = np.array(mfcc2)
+
+# number_of_equal_element = np.sum(array1 == array2)
+# total_elements = np.multiply(*array1.shape)
+# percentage = number_of_equal_element/total_elements
+# print('Number of equal elements: '+ format(number_of_equal_element))
+# print('number of identical elements: \t\t{}'.format(number_of_equal_element))
+# print('number of different elements: \t\t{}'.format(total_elements-number_of_equal_element))
+# print('percentage of identical elements: \t{:.2f}%'.format(percentage*100))
+
+array1 = []
+for nums in mfcc1:
+    for val in nums:
+        array1.append(val)
+        
+array2 = []
+for nums in mfcc2:
+    for val in nums:
+        array2.append(val)
+        
+print(cosine_similarity(array1, array2))
+        
+# print(array1)
+# print(array2)
+
+
+
+# set1 = set(array1)
+# set2 = set(array2)
+# total = sorted(set1|set2)
+
+# new_list1 = [x if x in set1 else "MISSING" for x in total]
+# new_list2 = [x if x in set2 else "MISSING" for x in total]
+
+# # print(new_list1)
+# # print(new_list2)
+# from numpy import dot
+# from numpy.linalg import norm
+
+# cos_sim = dot(new_list1, new_list2) / (norm(new_list1) * norm(new_list2))
+# print('Cosine similarity: '+ cos_sim.__str__())
+
+
+# def cosine_similarity1(v1,v2):
+# #     "compute cosine similarity of v1 to v2: (v1 dot v2)/{||v1||*||v2||)"
+#     sumxx, sumxy, sumyy = 0, 0, 0
+#     for i in range(len(v1)):
+#         x = v1[i]; y = v2[i]
+#         sumxx += x*x
+#         sumyy += y*y
+#         sumxy += x*y
+#     return sumxy/math.sqrt(sumxx*sumyy)
+
+
+# -------------------------------------------------------#
+# hop_length = 1024
+# y_ref, sr = librosa.load("t1.wav")
+# y_comp, sr = librosa.load("coba.wav")
+# chroma_ref = librosa.feature.chroma_cqt(y=y_ref, 
+#   sr=sr,hop_length=hop_length)
+# chroma_comp = librosa.feature.chroma_cqt(y=y_comp, 
+#   sr=sr, hop_length=hop_length)
+
+# x_ref = librosa.feature.stack_memory(
+#   chroma_ref, n_steps=10, delay=3)
+# x_comp = librosa.feature.stack_memory(
+#   chroma_comp, n_steps=10, delay=3)
+# xsim = librosa.segment.cross_similarity(x_comp, x_ref)
+
+# # print(xsim)
+
+# fig, ax = plt.subplots()
+# display.specshow(xsim, x_axis='s', y_axis='time', hop_length=hop_length, ax=ax)
+# plt.show()
+
+
+# ------------------------------------------------ #
+# plt.imshow(cost.T, origin='lower', cmap=plt.get_cmap('gray'), interpolation='nearest')
+# plt.plot(path[0], path[1], 'w')   #creating plot for DTW
+
+# plt.show()

readme.md

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+### ini merupakan pengujian untuk aplikasi bacaan_tulusan_arab menggunakan python jupyter notebook
+### audio kemudiannya diload oleh librosa dengan menggunakan  librosa.load() dan menggunakan mfcc() untuk menconvert audio ke dalam bentuk mfcc 2D list
+### dimana dengan menggunakan librosa akan dapat menampilkan visualisasi antara 2 audio dengan menggunakan librosa.display.specshow()
+### audio yang diconvert menjadi mfcc 2D list akan diconvert menjadi 1D list untuk dicheck similarity dengan menggunakan consine similarity
+### terdapat juga normalize distance yang mana jika mendekati 0 maka suara sama
+
+### sound similarity metode menggunakan cosine similarity 

test.py

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+import cv2 as cv
+import numpy as np
+
+base = cv.imread('spec.png')
+test = cv.imread('spec1.png')
+# test2 = cv.imread('test2.jpg')
+
+hsv_base = cv.cvtColor(base, cv.COLOR_BGR2HSV)
+hsv_test = cv.cvtColor(test, cv.COLOR_BGR2HSV)
+# hsv_test2 = cv.cvtColor(test2, cv.COLOR_BGR2HSV)
+
+h_bins = 50
+s_bins = 60
+histSize = [h_bins, s_bins]
+h_ranges = [0, 180]
+s_ranges = [0, 256]
+ranges = h_ranges + s_ranges
+channels = [0, 1]
+
+hist_base = cv.calcHist([hsv_base], channels, None, histSize, ranges, accumulate=False)
+cv.normalize(hist_base, hist_base, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)
+hist_test = cv.calcHist([hsv_test], channels, None, histSize, ranges, accumulate=False)
+cv.normalize(hist_test, hist_test, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)
+# hist_test2 = cv.calcHist([hsv_test2], channels, None, histSize, ranges, accumulate=False)
+# cv.normalize(hist_test2, hist_test2, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)
+
+compare_method = cv.HISTCMP_CORREL
+
+base_base = cv.compareHist(hist_base, hist_base, compare_method)
+base_test = cv.compareHist(hist_base, hist_test, compare_method)
+# base_test2 = cv.compareHist(hist_base, hist_test2, compare_method)
+
+print('base_base Similarity = ', base_base)
+print('base_test Similarity = ', base_test)
+# print('base_test2 Similarity = ', base_test2)
+
+cv.imshow('base',base)
+cv.imshow('test1',test)
+# cv.imshow('test2',test2)
+# cv.waitKey(0)