commit e900081dc766a6ef5d05036aa29ea90e5eca7dff Author: kicap1992 Date: Mon Mar 21 00:20:19 2022 +0800 first commit diff --git a/.ipynb_checkpoints/Pengujian Notebook-checkpoint.ipynb b/.ipynb_checkpoints/Pengujian Notebook-checkpoint.ipynb new file mode 100644 index 0000000..04178a6 --- /dev/null +++ b/.ipynb_checkpoints/Pengujian Notebook-checkpoint.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "18344f66", + "metadata": {}, + "source": [ + "### Load Suara Ke Librosa dan Menghitung nilai MFCC\n", + "#### MFCC (Mel-frequency cepstral coefficients) dihitung dalam bentuk 2D array (List) " + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "id": "d1249996", + "metadata": {}, + "outputs": [], + "source": [ + "import librosa\n", + "import librosa.display as display\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "y1, sr1 = librosa.load('t1.wav')\n", + "y2, sr2 = librosa.load('tp2.wav')\n", + "\n", + "\n", + "# plt.subplot(1, 2, 1) \n", + "mfcc1 = librosa.feature.mfcc(y1,sr1) #Computing MFCC values\n", + "# # print(mfcc1)\n", + "# display.specshow(mfcc1)\n", + "\n", + "# plt.subplot(1, 2, 2)\n", + "mfcc2 = librosa.feature.mfcc(y2, sr2)\n", + "# # print(mfcc2)\n", + "# display.specshow(mfcc2)" + ] + }, + { + "cell_type": "markdown", + "id": "d6cf40b3", + "metadata": {}, + "source": [ + "#### Menampilkan visualisasi suara 1 dalam bentuk waveshow " + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "id": "b181d059", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# fig, ax = plt.subplots()\n", + "plt.figure(figsize=(14, 5))\n", + "display.waveshow(y1, sr=sr1)\n", + "plt.savefig('spec.png')" + ] + }, + { + "cell_type": "markdown", + "id": "ed9cd674", + "metadata": {}, + "source": [ + "#### Menampilkan visualisasi suara 2 dalam bentuk waveshow " + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "id": "63d741cd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zpYU+HgkFqhOF+juhm/rLVtX2/d0a/aXMSilVSxmeuSLbre1bTJ2LAa8swrA3/sH6w2exO/cchr6+RJWxuerwmWLM9fF32MFTXLND/o+BDgWFP7Ycx73fbvTpMY+cUfaqpNJ+3XRM6yG4rPpJxtki/wjSiDy1YGfNa0q8MeVn9YIoT20/VoiOz8xXZd+eFJsoMpX3zjelHh86XYyyCoPNOk9fmbstF3d87drM4/GCCw5T84iCDQMdCgoHT/l+ce6P63N8fkx3VPhR2eZ9J62fvzns/0NBQM3O9Ht0VsBDzUpxSsUmLafNRZPH52D6b9uV2aFKft5w1GFqnj2u/HvKKw04fFrfF++I7GGgQ6oq00kFm49ULhm6P+88tuTkm++XlFfirYXur8/5Yd0RGBwEIOwnc9Hr8/doPYSA8ciPm/0mjTHYtH5ynsPPA299q8P0NTUcyDuPUwqvNZm5ItvlNDtvnr8v3Ey5q2Jv1ummz9Y4DJxdSe37ZcNR9H91Eb5fGxyvGwocDHRINXtPnEPLabYLHAPRA99vwlXvrzTfzy3wrFzto7O24LSdtCwpJZo9McerbunVPTfbfxum2vsbAcaZn+qzP1SzH9fnBG0u/odL9mN/nr5fL+6eJrs6M/LZ8my3x6Km7UfV6RMz+N/qrK1x9BlflbK8ZE8ehr+xBF2e/wvTf9uOLz2oDPmUnZmWcw6e39yCElz94Sqrxz5eegAAsPrAaSzanYdCO8GZq4FYVQPbx37SX9ojUU0Y6JBq8n3Ui0YP8s6VorTCgNu/XI/ySgNe/VP58qgAUG7QxwyZ3qzNPgMAuHnmWtzyuW9LiOtVYUk58tiQtkYvzt2FH9fpO8XUXTf5uIS+Ul6Ys9P5Rjp3orAE/V5ZBADYciQfe06cR35xOWauyMaTv7qe7lZYUo6eL9ovtvLndusm1ydMPaAOnS7CygOnUVZhwE5TM+Dn/zD+TSeaAqAQO5X8XC3qwsqW5K8Y6JCizpdWoLTCOOuweLdxMe1vKjc6c2bfSfdz0YVwvbRr9qki85fAvO25KC6rxBkvFstf/u5y3Rcy0MKiXScc/qyqQeLhM8U4dLoYP2/IwfnSCl8NTZce+XEzrnp/hdbDIB9bf+isy9vuztXXOh1/V15pvBB1wMEs4TrTBZmaVBoknvltO3IdNLGtmoEprzSgtKISPV5caJUiPnfbcfyx5eIaRst0tZLySmRM+QM7jhWisKQcf+86gZNOLoaUVRhw3Ser8fK8XebH9La+i6gmDHRIUVe+twLTftmG3IISvLPIWBb43u82aTqmolL3071cDXMMBomBry22+lKylx7gjpyzF2z6/VheTftx3RGPqgj5u5tmrsNjs7a4tO2DP2zGUtPf8LU/d5tPQLSgVZWm7ccKkc3Fw1SDv3epV9nNG1q+X10VExFq81hV0OAoXe6Xjc4v+s3ecgw/bah5u6d/3YYWU+ea0+ekRYJj9Qs8rZ+cZ769P8+Yojr6raV47vcduHnmOqfjOXiqCEv3nrJ6bPuxAlzJiyjkJxjokKJ25Z7Dj+tzbKbDmz0xx+FVLrWpWblIrVPYaf/bhk+WHTT3u7DMu39k1hZ8tSo4S4d+v+6Iw59VnwWrem7+u2gfzvp4sf2OY4U4dLoIf+04gT4v/+3TYxP5O38ojvHYT7YXXSzTBj9eZlsA5+vVzhfy3+fkwuCzs3fg85XGz/8Bry62+flTNaTI3fDpGvNtb6qCPvD9ZqzLdn3mkEhLDHQI248VuLwg99DpIpRXGlBeacCED1a6fLW60iA1a8r3hAeN90a/tVSFkTgmpcTNM61z65+bvQNTft6KFftO2WyvViUmf1aVG1+lrMKACo2uDE/4YCXu+24TdhwrxLF8zwpTqGHMW0uxdG+ezePBnuZH+hIZbjtbojcr9p823z5bVIazRWVWldgcVWX7sYaLNYNeW+z0uPbeq1nPL8Ah04UepatzVnBdKPk5BjpBTkqJMW8tw51fudaIbMCri/G/jUdRUl6JNQfP4HxpBTKm/OHS7+7KLXRamrKswoDn/9iBp37dpljKT9V0vTuOF5Q4nYGqNEhsOpJv87gnZVtv/2q9wzSSR2Ztsflyi48Od/sYvuL66ibPuLrm6v7vN9m94ukL50srsOlIPt5YoH0Z7DZPzsPWnAIAxnS2zXZes9P+53rPDQC49fO1Ti+OSCkVrRJI5K5vXJhB8UbVd9T/fbkOt3zuPA0MMH6eT/pwpd33j6fVD8+VVOBRF9N63TV7i+OeZZe/uxyfLVe3dQORtxjoBLmqqz/ufMBWdYoG3Cuj/MzvO2osTbntaAFaTpuLj5cexBcrD2le5WXNwZoXjq7LPoMr3rPNU3538X63j1W9ko6lo/kXcJepI/aBakGblFJ3sztqj6Zq7ZcrqtYy5Zz1zZqmXbmFKCjWV7XBC+WVOHK25rU6h9xYy7N83yks2HkSGw/nAwBOFpbY/fz4bu0RtHlqHjYc1neKy/tL3H+/6lVxmXszc0fzA3sNl9qlw+8wXSBcf+isW6/zVQfO+EUhiBABvFfD99nGw/lWhQ+I9IiBDgFwXrb45LkS7Dhm7HNg+YE+7I1/ABhPFjxJEbP07/nWJZldaWKmpSiF0isenbXZ6TabTVfkL3tnudXjbyzYi2s/Wa3IOPyFKwt6q5v4wUrnG3nobFGZOSge+eZSDHtDnb4dVR74fpOmwUPVyWNV0Y2HZ23B5e8ut9muquzt6gPOK02RMj5f4d7avToxESqNxDv2Zso98YmddTJKmrc91+PfvfPrDdiSk49fNx3VTWPt6nR2DY3IIwx0CACQFGv7hbfx8Fk88fNWHM2/gId+2GxetzJ3W67NFeAZc3e5nCZQlYa10iLHGQAW7bZeO/CszhtahtRQgnq3G+U3f/Cgj0dhSTnKKw1YuPOEVa64L6mdoqak8kqpeHf0Ku8v2Y8JFoGUs3Kt3vpl41Es2Z3nVmWqdxfvMxe2UErV+/PQ6SKrWd4qby7Yq+jxyLmfN7j3WVKs09RCvZ74K23sf5fjvu824b3F+9wqC05ErmOgQ2bbjhbg141H8d7ifdiVW4gnf92Gb9YcxqVvL7MpL+lN9/nS8kocy7+Aqz9a5fQLzWCQbqdjWNJqMbo3fXRc8cGSA/j3/D3m9L4ThSXIfGpezb+kMH+72PemSutlPvjngCr7rclXqw6hxdS5Lvdb2na0EFN+ZkdzsmavMhi5p6S80us06zcW7DWnJxORssK0HgDpxyVvLzPf/n7tEXMPDnsn7fd/v8nj43R9foHV/exTRfjAQZ7823/vw1erD2Ht1KFuH6ekvNKqh4C7zpXouxLVzuOF2HHcmE546nwpissqseNYITIbxgMwrp1IiY/Scoi6UnDB+Hx+teoQftt8DD/c1sur/a3Ydwrt0hLM92sqnrFo90kMapXi1fEsnTa9J/u9sgjrpg1F3bhIl3/3tfm2AV9kWAgOnS5C46RYr8ZVXmnAtqMFyPeylxQFFzVaAGz0UXqno+pq7nLUIJSIvKPIjI4QYqQQYrcQYp8QYoqdn/cXQmwQQlQIIa5U4pikjAoHSbhqp99UOXS6CANfW4xv19ovufnGgj3I83Asp72cVXlhzk6vfl9tlicHHy81Xpkd/dZSrDpgTGXr/uJCvDhnp2JfxP5uw6GzmDF3J5bsyXNaaMKZikoDrvl4NTpMn29+bGMN6wqOqdjgdddx7xc1l1YYPKpQd/JciVUa6z978nD5uyuwzbSmDAAqWZ42aEkpa0yx/GHdEfy++RjmbLVd0O5t+trl7/qmoeUjKlU7IyJleB3oCCFCAbwDYBSATABXCyEyq212GMCNAL7x9niknLH/XebVjIcSqooZOONu7jkA9JkR2I0aV1ucrFsu0H/+j4trmz785wBmbzlmvm8wSJxWaa2K3h3Nv4D3lxywWRvmLkczNzdaNOPzxJytx1FaUfOVbTWb37qqovLiv9+yQWJ5pcFuCk+iThe8k/pmzN1ltX4NALbmFGBttvGz69FZW/DEL1vtBjUz5u7yyRi99c8e275UwWQd1xaRzikxo9MdwD4p5QEpZRmA7wCMs9xASpktpdwCgJf2dGSLxVXXU+f13Yn6OZ0XJtCT4lLrk+HwUOPbvLzSgK/XHLZJHQw2njbHLKswoLisAk0en4MPl9quyyn0MtXxzq831Lggec3BM7oohWxZJGS7qRIjALSYOhfREbaVCPPOlequBLo/MbjRlHmvF2sn1bB8/ylzGfIqd3+7AVe9v9KcEn2upMLu+rGNRzw/gWb/JiKqosQanVQAlnlHOQB6KLBf0pDeqvHobTx6lne+1KqJ6/82HkX3jDr4YuUhfGpq7nbyHPPBXXWmqAyrD5zGrPU5WGhq6vrKvN1OfsszUhpn3bYeLUDHRolWP6t+ZVyPvliZDQBYuu9i8ZL/LNyLtg3jMbxtfeSdK0VyLdfXE5ExEKhjpyqmpR3HCvHlqmzfDMgDD/2wCY+MaI3YyFBzgNP/lUU1/k6LlDiPj3fXN1zYT0RGuipGIISYDGAyAKSnp2s8GtKT0iApN6qE6kUUVuw/jYGvLUZW49rmx9xpEBnsflx3BC95mUbjTmGLrUcLMO6d5cieMcb8mB5S1lxR1fi2+hqos8VlWHXgNCZ9uMrq30XekVLimd93YOfxQqtUVr35acNRLN6dh3apF4t3eDqz6oq/TRckiIiUSF07CqCRxf0002Nuk1J+KKXMklJmJScnKzA0InVIKXG2WN/pftUxl9qaq+kt79bQGdxVrqYfAbC7eLvr8395PQYt5Z0rxaQPV2k9jIBiMEi8v+QAZq7I9ouCI6eLyrBkT57uq1kSUWBRItBZC6CFEKKJECICwCQAvymwX1LRURWrQAWD1QfP4HovF59r6ar33U+DCvGnDqEuyHrBteBBiZNIew01LVXN2Fz3yWqbK90/rc9BUal7MzplFQbknNXPe9zb9Utk682Fe/HyPH0v2N92tND5Rg6420h5w+Gz+Pd8dVJKqWZFKs7OEXnL60BHSlkB4G4AfwLYCeAHKeV2IcSzQoixACCE6CaEyAFwFYAPhBDbvT0uecdfKtrolbMT10AUaOvJ3Q0evPHbpmM1/vzdRfsAGP/GN5oqmZ0+X4oLZZV46MfNbh9v7jbbcr1K8IeZg2AgpcRbC/dqPQxd+X7NEbz99z78uM5+qwJSz6t/MsAk/VJkjY6Ucg6AOdUee8ri9loYU9pIJ+z1LQgki3erm6NdGWhn/aQqZ80Aj9iZfXn9rz2o52HDV7VmUBZx7YMuVL/Q4k5qZKDj+hzf87TXHZEv6KoYAfmOP56obz6Sb1OJyhG1Z6we+nGTqvsn+nr1Ya2HYKWkvBL3f7/Jo9/1VZf6QLQrtxC9m9WtcZs9J/RVVpqCy87jnqcoEqlNiTU6pKG8c6XYlRscHzJbjxY438hkV6733eJrUlLOKnBKEQG29scVJeWV6PrcX9inQN8TCeUuWmw7WoB+LyvfaHdt9sVAhz1O3POySqXMA833TFnTzKkgbUJN/oGBjp+b/vt2jHxzqdbDIPJYMGbdfLLsIE4XlSkS6Nhb8H2uxLO1NPvzzttNo1PSa1ww7pYDdl4jv2z0qLCpz5RXGrDvpLoXmxyZuy1Xk+MGMxYbIT1joOPnThQYc/9/33wMX606pPFo1LVs7ynnGynMX/qXkLLKKw04ln9BtV4fJ8xrdmS1++6zV3nrQF6Rx/tT24E8plm5Y0S7+ubbUkp8vfoQnp29Q8MROTd7yzEMff0fr/dTYafUOhGRO7hGJ0Dc8+1GAMC1PRtrPBL1zNvu+yt1ZZUGRIWH+vy4pK0f1+XgiV+2onX9WorvO7+4DH9sMRYDOXXe2IspWuHX2Kz17pXmdYVSTXsX7c5TZD/B6MCpIkz9ZZvWw3DqWL7ngbuledtzcUmHhorsi4iCEwMdP8cmkETe23wkH6/N343CC+XYnHNxLZiSa71+WHsEoSHCqlx0RJjyk+pbc1xfy1bd078aK/9/tvwgburTxOpnSvbKWJd9BlkZdRTbXyDLLbgYNBw6rd+ZOkvhocosvHO1jP+kD93vC0bKKi6rQEwETylJf/iq9CMl5ZWICA1BiKlz43drbKsyFZVWIDaSTyuRO8a9s1z1Yzz60xabx2bM2YV/9uRZBVeeqKg0ICzUGDQt3+95ime+qU/OM7/vQIuUONSKCjdXOlSyo/2V769E9owxiu0vkMVGXpztu+/bTdoNxEW5BSWYuTzbZ8fblVuIVQfO+Ox4ZN/VH63Cr3f11XoYRDa4RsePdH3+Lzz+81YUlxlPOKb8vNVmG7XWFPiT11VY7PzrpqO46bM1iu+XtPetnQsGvnKmuAyztxz3umpRkQqVzK79ZA0e/GGT+f6WnHzF9j2oVbJi+wom5/zg8/2PrcdxrECZ1LWzRWVOt3l30X5FjkXe2XzEu4s1RGphoONHikor8f26I8h86k+sP8QrWI58qWBRhokfrMSRM8W477tNXFuggRAflJ7+bdMx9Q+isiNnilXZ77mSCvOC8Edm2c5IeYrvJeeCsOq6jU+WH3S6TRAWbSQXfL36EJ75fbvWwyAdYKDjpwLh5MwTrlRBO+tiXrcrdh4/Z9UMLb/Y+RVGUo4f9rXVxL3fbVRlvyfPlaL51LnY5kYPK1JG9Ze+2hUg/7NgjyKfb5UG5SqlubJGx9NS6hTYZi7Pxmc+TKEk/WKgo2Pzt+ci86l5eOiHTfh6tfUsxecrPZ+18Ocu5VqUe7Y82b555lqfH5/IGcsF62UKVUeztGyf70u700UnCkvQ+sl5qu1/V24h3liwF5uO5Hu1n/OlFXhxjm25czUt5uygbshgbIpGusdV6zq2fN8pFJdV4qcNR/HTBmUaxO04VoiPlh5QZF9aEBokdNz+1Xrz7Q2H831+fFLXygOntR6CoorK9L+Og1y39+R59HhxoWr7rzRIvKRQcHKejSOD2uEzxWicFKv1MIiscEYnyDwyazPmbGXnaG+4uh6COfb6t+eENt3blVZcVml+XWrRWNcTewPkb682tZu/bjx8VpVZQKX1f2URFu0+qfUwqAahvlhUSeQmBjo6tSUnH6dcqDhTnbMPmu3HCmv8ub877WX1Kle88MdOl7bjJL7+DX/D++7tevHEL8YqjP7yHt/oZZqU3tj76PWH0z4lgxw1A5HDZ4qxw09e2/5EKPgiDQvRzyllJdPoyEQ/r0oCYMy1P1tUhrH/XW7unu6OV/9UvrSy0ry56DP8zSU1NkR8aa76+eHztnNGjPQnIlS9j3NXGze6w5u+PEfOFOOfPfpam2GvcIa3p1oqPqVW8kwXiBy9hgpLyl1qVvq4nZYHaiqv1P9MlN4FajxQ9VquZEWboMdAR2cmfLASQ19f4vHvf7/2iIKjUYc3nzsnCktx6X+XOfx53jn1Z3Rc4Q9XcimwGFQ8YylWYd2PpwuXf1h7GLd/tR7Xf8q+VkqpOil0dBHnxT92YvC/Pf9eUtqu3EKsPnAaLabO1Xoois6IOD2W7w7l96oap+/K5SxgsGOgozOHzxTjtAcpazW58+v12HfyvKL71Jq9crePzdqCJTq5yuvoFI5fVOSPvvCiyqMjxS42OV194DQKLGaUHv1pq9+k53nL11fbHT3POWcvOL0yrlbFLXvpyCPfXIqPljrvseMLlv9sV4Ieb74DtJ6b8Mfvr0CdsSLXMdDRiYpKA0or1CmdPGdrLjYcOotVAVRd6pK3l1mV1AWA79fpfzaLn7lERgdPubbIfuKHq/DZCvsntRsOn8UZhS8MBbuS8kqXCq5sPHwWb/61x3zf1cDVXb9stF9xdMHOE6oczxuunFT74jtAQJ2gxJ++v9Yf8t82GqQsBjo60XzqXCxRsR9ATGQoJn24SrX9a+HhHzd7vQ8hrL8Q/PGKFREAFJX6vseUO6q/t065UTikuKwSi3aftJk1GP/uCry1cK8Co9MPX38GFVwoxw6LpsjvLNqHfq8sstrGXlPO79YcwZs++Nv74xoLrb9HJLQJSrhmivSIgY4O3P6lsU/L5C/XO9nSv6j9Ya9EE0Mprb8Q/O8rlchoTfYZrYdQo+rvrciwUOe/YxHY3PTZWrspuL9uOupyyXelqVFN19efQXd8vcHq/tt/7zPf/mVjDlpNm4sKU7Bhr6Ho7txz2Hj4rO6KQ2jJ2XOoxMtG62DKnveW7Nd6CEQ2GOjogNJVvAou2F59e3meb7tVA777wn7i562YuVwf+dqO6PFLiQJPSx0szlbSDFMVxaWm3kAf/GPb7PhscTlem69NtUm1Jxu0XF9w+nwpDuQVobTCYF4Tddk7y7H9WIFV2uGIN//B5e+usAmYlFK9wIxaa4F8SYl/gR7/Ct+sPqzp8bcdLfCLnlDkWwx0NGYvJcBbHZ+Zj53HC3Hy3MU1LEfOXFD8OHogpcQ3aw5j+u87tB5KjZT6UmLARDUpUyl15KY+GYrvs6ZKbrtzzyHvXCnmbDOW2N9pSq2atT7H7vb2Lu4EAi1PZrs+v8BucZcxby3Dvd9u9Nk4Pl5mfRGryeNzfHZs8g8f/XMAD36/CZe8vQzzd7h+4dhgkJi3je0iAh0DHY2ptWBu1H+WYvpv21XZt56UlLt/YuePzZurhqzHq3gU+D5bnq34Plfsd1wcZcSb/+Dp37a5fIFmsYrrG4PZFgc9y7YeLfDpeoxAmMXxBV+WutaTL1Zl42dT0YrqRTFqeumcPFeK279az1mgAMdAR2NqfnzP334Cs9arX4nMWeCg5odvm6fmuf07/rS2VYkAJ5C+++z9W7QKXAPp76onGVP+AGCsFumONxfscb6RTvnjCWpiTITPjvXWwn3ONzLxwz+lYvQQD365Mtvnx6zpgsjZYsdVGUNMZ8AlKlW8JX1goKOx0+fVK41aYZB4+MctLm/v6ReEs8BBDx++/ngiASify+2nfwYze3+Pml5/av57dfCy9pq/vx4svbnAf6uvefsZqcXz+M0a5XsrObJsn3HGzpXP8ep/ykB6jfuDJ3/drlqrDE+EWVwJ+2l9jtVM5PuLjcUTSlQqjU76wEBHY0Wlyncc91QgnLg5oodgSw+C7c9g79/LE5+LfPF6cGfG7ds13i1mDrYUFC1TWj1JG/ZU1QXBt92Y2akSbJ95elB4Qd3zmoOnirDcQdXV0vJK5FvM4lzz8WoAxs+Gh37cbE6ZLasw4FMXU3J9nTppMEhdpGv+teMELnl7qdbD8BoDHY2Fh/Ip8Af+OiNEtrT/+gguNc24VV/n8fjPW7061vdrta365GvBktJ6wFTl7UI5r7z7g9NFxkp5Gw+fRXmlASXllSivNGDjYes1yQaDxJEzxTia7zj17KEfNuHf83ej7dPz8PvmYygqrcCg1xbjXx+vRsaUP6yKLgHG10h5pfU740DeeUjTu+VEYQkenbUZ/av1itp0JB93fb0B+/OsS9j/e/5u3DxzLQwGiVnrc7CiWoC1bO8pHC9QtthTrxkLHRbdqDRI/L3LN81y/9qRi21HC51vqHNhWg8g2MVGOu8l4YiAvk7a9DYeJeng4gppJFQAlXz+bSjxfp+z9TjGdUoFoEzltCd/3Y7remV4vR9/58pzEygv6UD+3vFXI99cirpxETh1vgyxEaFIqxODJ0a3wQ2frsFvd/dB85Q4ZJ8qxui3liK5ViTqxUdi9j39cLaoDHO2Hse/ejY27+unDUfNt+/5diNa1ouzOtapc9bp/3nnSm2qMw7+9xLz7Udn2abzrz901lwePa1ONB4b0RolFZWIiQjDvG252HvyPB7+cbO54EH2jDHm3732k9W4smsaXruqo0t/Gyklrnp/Je4b2gL9WiSj4EI5QgRQKyrcvM2JQsfNlDfn5OPmmetwS58MPHlpWwDA9Z+sxpRRbZDZMN6lMbjKlV5n/oDTCRo7W+T5Gh1ffLi7M5Mhof5VQlf2709XKsl3XHld2Euzqgpy+LqypsTnz0qLymtP/OLdbE6V4rIKlFUYzGlsh04XYc+Jc5BSIregxMlvB4ZgOvEPpn+rPzllSjcsKqvE7txzqDQY349j/7scmU/9iX2mmZO8c6XYdrQQp8+XYvbW45j6v23mfSzYYTtzseeE9YzL6LesU6s+WnrQ7b6B1XtAfbYiG52e/QuztxzDXlOT4qogBwDWHDyD8sqLnzH5xbYXafadPIc/t+di3rbj2HfyPHbnngMA3DxzLdYdOovVB85gz4lzGPvfZbjnm4vl2n+yCNJKyitRUFyOQos2JFXriT4xpd0dy7+Af/aespnlGfXmP3j8560osZgFnbXuCA6ftt9cucP0P7Fi3ylIKc19sv409XgsKa/EfIX7PfqS0EMeoD1ZWVly3bp1Wg9DUccLLqB+fBSEKXpoMXUOmqfUMveIICLSoxChXrXCNyd2wrOzd+CMFxd9HFk7dSi6vbAAAPD4qNZ4ae4u3Nq3CcZ2aoi4yDCrK72kLE9eM67OznAWx/9c0SUNP224eBJfKyoM50rsr+X58LquOHL2Ap6bre/+eK9P6IgHf9gMAJg2pg1u6dsEgONeT+umDUXW8wvs/ix7xhhUVBrQ3KLp831DWuA/C/ciNiIURWWVGNgqGcMy62HqL8ZgsEVKHG7onYFp/9uGLumJ+PnOPubfrapeCQDR4aHmtM9uGbWxNvssUmpFYsqo1th0OB8Pj2yFDtPn24zHch8A8L+7+qBTo0Qcy7+A5FqRqKiUiI7Qz6yPEGK9lDLL5nElAh0hxEgA/wEQCuBjKeWMaj+PBPAFgK4ATgOYKKXMrmmfgRjoZEz5A/+9pjMu6dDQfN8ZfqCTM8L0IuHrxD8Ey3taCGVSPtUMsojIe8HymeaKDU8OQ5fn/rL7s/ioMBQ6CO4A4D+TOuG+7zZ5fOy9L4xCqBDYc/IcRr7pehGBcZ0a4tdNx6weS4gOt0knHt2+Ppqn1MJbCy9WuDzw4miE6KQ5oWqBjhAiFMAeAMMA5ABYC+BqKeUOi23uBNBBSnm7EGISgMullBNr2m+gBjoA8MkNWbjlc+t/Gz8o/Aefq8Ck1Im5GviaI71z5f2j5/cYEblvy/ThiLdYX6QlR4GOEmt0ugPYJ6U8IKUsA/AdgHHVthkH4HPT7VkAhggR+HWs7vp6A/638Sju+nqD1exN9SAH4EmMP+FzFZj0fAKm46FRAHH2rVzTj115/+j5PUb+zdmkgk4mHQLO3mprpvRIiaprqQCOWNzPAdDD0TZSygohRAGAJAD2C6Hr0JEzxTV22K1SYZAY/+4K8/0/th5Xc1hERESKcBaIME4hvapKb3U0+61F+quvZuK1nPFPio3Q6Miu01V5aSHEZACTASA9PV3j0VjrV63mOhERERHph56CcV+NRat/c3S4fgoR1ESJQOcogEYW99NMj9nbJkcIEQYgAcaiBFaklB8C+BAwrtFRYGyKsayb7syE91diTfYZFUdDRERERGrjGkn7/KWBrxKBzloALYQQTWAMaCYBuKbaNr8BuAHASgBXAvhb6rWutQJ+uL0XAOCPLcfRtXFt7M87j0W7TuLjZQc1HhkREZFnfH3CxxNM8pa915C7ryu+Bh07XVSGjLqxWg+jRl4XI5BSVgC4G8CfAHYC+EFKuV0I8awQYqxps08AJAkh9gF4EMAUb4/rD8Z0aID6CVHo07wupl2SaX58wYP9Malboxp+k9zBNYYU6LxZJE6klJpO+NR4DfIEk7xl7zUUyK+rWB/3tWnbMN6nx/OEElXXIKWcI6VsKaVsJqV8wfTYU1LK30y3S6SUV0kpm0spu0spDyhxXH/01S090DylFmZc0UHroQSMQP7QcoUAT3Q9pcXfzZNjSouFtnZ/7ulgdExU+z/pm73XYODXViW6aEJWGnY9N9Lhzwe2Sq7x92eZsoE8lT1jDLY/OxIbnxzm1u89Mbq1zWM392li89hdg5rhh9usxxgRqkgYoSr9jzCAZM8Yg74t6prvj+3YAOM7p7q1D2+/N1z5fX43+ReJwDzR9YUar1Cr9Ebw5rnS6nlW6zMhMiwEs+/pi2t72BafkdX+74l9L4wy3/75zt4IDRH4fnJP7HthlFvrLsl9IR70zOF3T+Cafmmm1f02DRzPBPzzyCB8c2v14r36EhkWgrn39TPfX/LIQLxyZUdEhYdiWGY9u7/z/rVdzbdDq9W7zp4xBl0b17Z67J1ruiC9TgxGtauPMR0aYPqlmfhuck/zzx8d2QofXW9sG3PHwGbmx2tbVEKrExuBK7qk4qquaQCAyzun4l890nFj7wzMvqcvXp/QETdZBDU9mtQBADxV7fkCgKu6NkL3JnVw8KXRyJ4xBtkzxuimWWhNdFV1Ldi8dXUXfL4iGz9vrF67wTFvT3Rc+X3LbfTWldyV8bApHXnK8rVT/TWkt/eCL6n1z76tf1O0S03A85e3R524SKuO257a/+Joq5OIbc+MQEWlAYkxEdj/4miv90+u8eS9EqRvr6CQWjvGfLtX0yTc2CcDt3253vzYwZdG44+tx3H3NxuRnhSD9KQY/P3QAAz+95Ia99uzaR2sOqBc8afbBjRF50aJ+GbNYbxwWXu7FXf3vjAK4aEhMJhe5CPa1kPjpIvrVD66PgsVlQbsOXEeBimR2SAe5QYDIsNC8fDwlnht/h78X7+meGh4S9z9zQY0SIgGAAghsPjhgRj42mIAwKh29TG6fX3zzwBg6d4883HuHNgcxWUVAGxT1h4c1hKd0xPRr8XFWaQb+2QgrXYMEqIvNvhsl5pg/FmvDPzfgKZITYxGpenf1SU9ERsO5+PAi6Nx+EyxeS2Ov7XB5IyOxhJj9NFR1hF3vqzUfO1Xnbe4Mh4GOeQpe6+dqpd1sAY5auqcfvEK5n1DWiiyz+pXSuMiw5AY416vBz1/j3NWnvzBbf2bAgAGt07BpG6NUNt0rrP9mRH45v96oENaAjqkJaBPsyQMa1MPQggMbVMPP1qkbzVNjrPZb9XMRJWnL21rdf/xUa3x1S3uzQbNvqev+XZkWChGtmuAL27ugUZ1YtAixTiGJy3WWYeb0rWqZjMaWQRxVcJCQ5DZMB7tUhMQEiIQGWYMRO4e3AK3D2iKK7umITw0BO9f2xVPW8yeWC7sDwkREEJYBRb14qMAAK9eaVz+EBMRhkdGtMKVXa3Xfd87pIVVkAMAbRsmWAU5lqaPa4vURGPAVfUZ2rZhgnkcei84UBPO6GissKRC6yEoRs0AQ+uTTFb/CV583j3n7H1jmbNePUDxxE939PZ6H4C+L5a4OyvvCD/TSC3LpwxGamI07hrcHPFRxhNrKSW2TB+O2EjjaWeDhGj8dndfXCirNF9YiAoPRbeMOlb7eml8e9SPj8IfW47hxj5N0C41AfvzzmPjkXyEh4QgtXa01fYVBolW9WtZPbb56eGICg9Bq2nz8O+rOqJurUisP3TWPIOcUisSW6YPx4p9p9G9ifXx7xnSAgfzzuOWvk0wPLMeDNU+HJY8MhB14yLd+vtMGdXGfNve7MjlnVPxi4NMnxYpcVj22CCkWQRXdw1q7tbxXVXiJ+WjnWGgozF/vPIWjF+QzqoN+dPfg6l9vufoNRLoz4VEze+P6l/yP9/ZG+PfXeHx8TqmJXj8u8HGn152HdMSsDmnAL2aJmHlAZsWfKQz4aHG93VVkAMY3+uW96tEO6kSdnV34/q9Qa1TzI99dmN35F8os0oXq5IcF2m1z8iwECREh6Oi0gAAaJwUg6yMOhjQMtkqVTY+Khwj29W32d/Yjg3NtxvVsZ25sTcGb70xsRPemNjJ7s+EEFZBjppu6tMESXHuzYbrEVPXNBYb6R+dZS350xdkFTUDSn/6ewhYn1j7Y6DtDrX/fa7u39FrJJCDnCru/BO7WKSyeSLMDyoAqUGLVLv2qb4LKiPCjM9rn+ZJPjsmea5urHszHO5KiAl3HGAIY7pqlc9u7AbA+Nmw+okhVgv+X7mS1W9rktkw3mr2yV8F57eCjtR2M3fcHc1T4vDHvX2db+ghc/lXBXp82NtGye/uIDiftKv637D63yHQ/y5q//v85e+n5zUn1Y3p0MCj3/vguq7ON9Ipb7P2fB0wh4UKdPDh7Nn1vTIA+N8i6GD09a09dFWJy/KtUS8+yuo1VJU6GxvJ5KZAxkBHY+EqXYGMDg9F3+Z1zYvJ1GAu/+rkS9bTnHJXfq+mmvW+5upHuy+/AvzlRJzU5e2JsKMFrJ5w9vp/55ouaFQnGtf3auzWfke0tU078Rdar0F0V6VBotyUCuQLl1qkD5Fjeggv+jSv63wjhVUVDLCnps8uYfqLRYbxVDiQ8dnVWPWFb0pZ9fgQPG6nCVSgiQrXT+qfq+cqfnZOQ+SwL4Qnql7/Q9s43ufse/ph2phMNKoT7XAbS8MVHB9dNK6TbYARExGKwa1SzCeJaouptoZjRFvXn2s9nPj7UrB+tzw+ujWeusSYYpVSy/W0ueRakVj66KCgTXkNFnx2NabGjE72jDFIiAk3lzMEgGt72jbkCxRvXd0ZP93hXUdhtbkzkx9sX86knBt7Z6iy31nrc1TZryMJ0eGICAvB/UNaAjD2kwBsmw5WqZ8Q5bOx+ZKWGUD7XxyN9GqLrxsmRuGfRwfhdQcLpdXwf/2aWt3/4Losl383WE/8g83g1vVwc9+m2PbMCAxomez8FyzYKzBAgYWBjg48fYn9L28l9WwauIs4x3ZsiK6N1ZkZU4o7qSn8ciZPTR/b1vlGfuQKU8+MqpOR4XbS0zqmJeDhEa18Oq4qgbBkpImD/hihIQK9miZhTPsGaG5KDfrpjt6oGxdplQ6067mRWPPEEPx+tzrrQavP6JB+PemDc5maxEWGcR0X2WCgowM39W2C/i3qYtbt+p6V0Js7Bjbz6veFxX9E/q5nU30H+9VVGNxb47Hy8cFomGibyjaodYrdsrW+EAhV8x4fZZ3i/OLl7dHANEPWu3ldvPOvLqhrKjFb1cEdAMLDjJ+cUeGhSImPQtNk/20o6AqlvyeEg9v+bEx7zwqJEKmJpSZ04otbeqi6uPN8SQU2PDkMXZ77S7Vj+NrtA7wLdLQ4R/G3njvkP6J1tF7NFe402asfH2V1kl1lzdQhSIz2/z4PjvjisyIuMgyZDeKx43ghAOCaHumYkGXdfT4sxPaa6B0Dm6OvxcLzEJWupMdF6eM0RennQjq4TUTK4oyOjqhVge35y9piWGY91IkNnBOCNVOH2FRTeW5cO8Tr5EvREX6hkT+KUKEqUbNkx5WSLK16fAiuc1CBLaVWlCpj0wut/mWuLM5OTYzGyHYXr+A7a/zoqSu6pNl9/Kqu9h8nxwJl5sgVlv1yKLgF7jeEn+rTPEnxFJRre2YgyY2rp/4gpZbt4uPrejVGZy8bDhKRrVv7NlF8n2EurrSvnxBldRHo5zt7Ozz5DTg+OjOtugAzbYz95oCd0xPRTKPUNHuVNXc8OwJ3DmqORrVdq8pHRsF4oU1PlVlJGwx0dGbmTd3xza09Pf795y9rp+Bo1OHNd/fk/k2xftpQhz9v3aCWF3sn8l/llZ6fxmhR3Ssy3LOvny7ptfH02EwsenigsgPykJp/Ol+tATKYqqW0qm//8/OBoS3xx739fDMYF8REhKFJ3VgsfWyw1kMhnTpbXAYA5kIaFLwY6OhMeGgIQkIE1k0biolZjdz+fX9omufNd/ddA5vXODv18HD1qy89NKyl6scg3wikdM568Z6XWHZWFTBewYahVby50hofFe6wWpivqRmL+KqRaNX7oNLBAUNChEvP11e39FB0XKS+QE1nC2X1NTJhoKNTdeMiEeXBFU+Dk0uA/d2sMe9v1FrnZOl6N3qVCBG4XySBYN79+rlK7a2nxxpLu9bUiFNPBrVK0XoIBGWDWDWvnl/TvZHbPVLIOSVjaTULKhF5ioFOkHntyg64rX9T5xuSQ9WLINREyuDMi/YX9tZ6+aOYiFBzieVmKc5nOvQQfCe70cE8mGU1rq3qBap2qQl+8Vy8OL4D2qUmaD2MgKDW+z9Uyw63RA4w0NGxq7Ia4cbeGXj5ivaKXXlOiY/CNT3SFdmXFqQGYcOH13U13768c6rPj0/q6hVgzXRd6Snj7ruIpy/aSYqLwFuTOql6jGmXGIsQeHuimhijTT8jco9a36L2+lwRaY2Bjo61S03A9LFtMbFbOlrXj7f62c19PK+C1DhJH7ntnoiN1LZ89OsTOmp6fCJ7LK90q3FVdVwnBvi+4GhZQWJMBLJnjFHtuCm1ovDx9Vno3sS7ip9R4aF48pJMhUblmks7NvTp8cg/cHaJqjDQ8VM39cnQegiacGUNTlUXbyXc3CcDPSyu+AsucPQpX3xXBcJT+takzqrsN6VWJLJnjEH9hCherfcByyWWvn5ZDs2sh8gwfZXizUiK0XoI5KeeujQTr1zZQethkA4w0PEjl3ZogBnj2yN7xhg0qsMvAEemj22r2L7uH9YSCdHh+OmO3niQ1dZ8zhdVp+4Z3EL9g6hMjWaNybUikWFR2ew/CgZTgZYuqAZ/XdsXqWAD1+t7ZTjdJoZ9UsiO3s3qYoIHlWsp8DDQ8SNvX9MFk7pfXF+z4MH+NtvwqitwSQflUxm6Nq6Ne4f4/wkx2erVLAlhodpM6wxomYypY9ogy8su3vFRyqd0fj+5J/5jsTakvhflq6tbeeC0YvtSkt4n91o76HOjJ5d1TlWsOlqEC0HToyPVbylAzt3Qq7HWQyCyi4GOH2ueUssm7UZvqQdE/mDllCF4fUJHPDC0hWoVrv5+aAD+fmiA1WNjOjTA//Vr6lYlP3ssUyrHdfI80O/bvC4AYz+UpslxaJBwcXGxkhdRVj8xRLF9KUmJWRSlg6UL5RdL9r44vr3Ce1deXGQY+jT33YxdUlwk7udFKM09dalymRRESmKg4+e6phuvBCfFRrBBFpGHkmtFYnyXNNw3tCW+uLk7PriuK+rGReDq7sqlPjRNjkPT5DjsfWGUedG3owaN3rAMTtx1VVYaAKBvi7o2P1OyEIg3zU31TulntJ5F6ecUPygDDQBlFcr0U8lwsXDO/Uwr1hwX/5NeMdAJEJ/e2A1f3dpd62Go6pa+nlea81Q087+D0oi29bF26lC8NF75xazhoSHmFKTaMcbCGWUKN9q7Z3BzRfcHAGEKncgMasWmj+4otQga0mrHYNljgzQcjWs6pCUqsh97ATcRkTsY6Pi5dqkJSKsdjY6NEtGrWWB/KTSp6/uy2K5UeaPApGaFvTYNrMvF143z/Er9BxZ9nqqoUZxAKX2aB/bnlNIW7jxhdT+tdgx+vL2XRqNxTf+Wydjz/Cith0FEBG2bkpDXnhjdBg9w2p78WIjwTXU1Pbm6ezqyTxWhq5dFCAAgNsL2Y7yWC01D7emSXhvXqtxQ2JseYMGoa4Ztb5vmyXEajMQ9rhQSUMOodvUxd1uuJscOFEJYlzp3pjaLIJGO8XK1n4sIC/F6IbO/cKeSz5Vd01QcCdDeokEjeSfYgpwqj49ug+Qa1lxokTbZqE4Mnr/c/oL3EC9muMZ0aHBxP8zld8u0MW20HoJfmMhSwopxJ8gBgHS2uyAd8yrQEULUEUL8JYTYa/q/3cuTQoh5Qoh8IcRsb45HyqkX7x+LWi250ztI7avGdw1Sfg0EkaVb+jaxKu+stYiwEPx8Z2+Pfjc10fMCCcHOXlpjaLVy6P5QdpoCV0oAFxch/+ftjM4UAAullC0ALDTdt+dVANd5eSxS0Ii29bUegqoyG8Y734jIR/o5WVTdxU4K271DWmBsx4Zomuz+2rQ0lQKLdg05k6kH8R6mJgay8DBj8Hdj7wxtBxKEOqcnaj0EIoe8DXTGAfjcdPtzAJfZ20hKuRDAOS+PRQp6dGRrrYfg1xonBd9UfaBlHPX14aL46sUHqruup7HZXreM2phlWmgeERYCIQRevdL9ym+DWqfUmBbnKa3WXZCtz28O7Cqb7rp9QDO8968u6NHUdz18yOiOAc20HgKRQ95+a9WTUh433c4FUM/L/ZGPxCnYEyMYtWkQj9/v7qv1MNxima7oSdWmQFtL8+Utrp0odsvwvmBAfJRr77f7h9oWFunauA4mdnN//UGkjoISf+n/4k8GtEzG/+7qo/UwatQu1TrADwsRiAp37XU5Icu9dZZptWMwqn0D5xuS4tSsUEnkLaefOEKIBUKIbXb+G2e5nZRSwsteaUKIyUKIdUKIdXl5ed7sigKMnk7a/FFKrUg0qh18s1A1cfXLeWynVK+PFWOnMpoj8XaKi7x8hfL9fHypVlQY/nlE//1f/E2nRon47zWdNSm9766ujWvjmh7pLNlPRD7l9BNHSjlUStnOzn+/AjghhGgAAKb/n/RmMFLKD6WUWVLKrORkNpUj8kT9+CiM69jQfP/G3hlY9thgXGrxWKaTVCq66Kquafjz/v64a6Dn6RmRLl7FBoCW9Wph+zMjbB6Pj9b/LOztphSW6uskmibHIT0pBtkzxmgwKv/mLBy/pENDPDO2LZ67rJ1PxuOJl69oj+8n98QzY9uaHxtr8XlkT4UXU8gPD2fLBSIy8vbSym8AbjDdvgHAr17uj3QiRmcNB+uxqovLYiJC8Z+rO5vvZzaMR0RYCG7onYEvbu6OxkkxiGXqosuiwkPRqn4tPDKyNbJnjMHIdvXN62iUP5bxI9ne8zP3vv547aqOqhxXCZ0aJZqLLiTFRpgff+HyduhmpxcMucbeDF91KfFRuK5nY93OfE/slo6wUOOas5b1aqFBQhTemNipxt/Zf/K8x8e7e3ALj3+XiAKLt5+KMwAME0LsBTDUdB9CiCwhxMdVGwkhlgL4EcAQIUSOEML2ciX53O0DmprLktaNi3CytbbemKjfEzy9qb4IPcyiikD/lslYEuQpRJ5UMbP0/rVd0alRos3jziqrObPgwQHo3MjxeqDUxGiM7+x9Gp23nh138ar8JIu1Q/+7qw/Ol1bYbF9eYfDJuEh//Uyu75lhM7v3zjVd8MNtvRAaIhATEYqGiVF2A/jLdPBaJyL/51WgI6U8LaUcIqVsYUpxO2N6fJ2U8laL7fpJKZOllNFSyjQp5Z/eDpy8N2VUG8y+R9sF9TufHVnjz6uaJnZt7P4V4S3Th3s0Jn9h2UB16uiLTQVfvfLiScN7/+qCSzrUnCISLHo3S8IH13VVpKu8vfU9L1xmv9Gmq5qnxDltpmnv50qtA/bkYsczFkEPALvNi4vKKj0eE/m3Cd0aYfpY69dI/YQoc0+0L27ujvf+1RW17BTruEnlXmhK8aQqYiDJslMan0hP9DnPTZprn+qbfhmhIQKbnhqGl6+wf5L40vj2GOlhz5+oMO/S72aM9+7E1Zd6NzeWVN39/Eikm0pfZ88Yg1HtG7AksElSXCRGtK2P63o19mq9DWB83a6fNhQHXxptfiy9hpLjavWfyWwQj64KnGjERYZh5eND3P69yLBQq1LrPZrUweanh2NwmxTzYyGsyEQOZGXUQcdGiRjWRvmCreunDVV8n/YMbJXifCMi0gzPgMhs1eNDMOfefph5Uze8+68uGNHW+OVjL03Hm47t66cNxY5njdmLQgCJMRGY2C3d7raXdU7F+9d19eg4EWEhAb34eVhmPXPqYVrtGIzr1BCRXgZ3gPPFz/6qsekqcr8WyXhEgT5SSXGRLldu62jnPeSNrMa1ERsRim8n90RUuHvPub2F2udLKxSphiWEQEJ0ONqysSi5wdlMpieS4nxT0lypIiG+urhIFGwY6JBZ/YQoZDaMx8BWKUiKi8RTl7bFS+Pb44fbeuGGXo1R36IggL3gx1WxkWGIiQjDxieHBWyp0R5N1F18/ciIVri2Z2OEhRpPEBKiw/GfSZ2d/JZrXK115OmFemfnNGoFWvcMaa7Kfh8d2UqV/dakb4u62P7sSLupYvZc1TVN8Z4rtWNcOzb5jruzZ9MvzVRpJMEjMizU6/TR/0zqhPeu7aLMgIjISmCeZZLbTp0vs3ksNTEaV3dPR0RYCJ4Y0wbz7u8HALilbxPUibXO5//97r6YeVM3l45VdQW6drV9VG8Q99UtPVwevxbKDY4XWbtzwuEobc8Vk/s3w12DtOlKLT2s/uqsaqwafUkbJ8UoMttlz619m2KVKe0rLjIM4zqpuyZq+qWZuKKLe80UB7VO8erihD1Vla26ZdRBu4a25crvH8rKV75292D3gvmDp4pUGol32ihU/r5Fivfr8dSSXicGyx4bhPkP9MfYjg2Rxj5nRKpgoEMAgAgnMyuRYaFIjDEGJpYzO4sfHggAaJ+W4HWu8oPDrK+M926W5NX+vOUsVgkPUebt4yhtz1LVyfPGJ4dZPT62Y0M8MsL7NCxv+TLdzbLwgqu+vFm9oDkiLAT1E4zviRWPD8brEzqpdiwAuLFPE/Nibi10N81WVhUvePHy9vj+Ntty2x3SjKk4l3TQd7d6b6vw6cmodu6tZ4x0M+3RV+wVJ/BEvxbq9uN7ZITns7lTRrVGWu0YtKxXy+UUWD3y46FTkGCgE+TCTAFOLxeDivBQYdWFu158lMsByX8mdaqx/0j9hChkzxiD/0zqhA+v66pK3rY7hmfWfNLQPi0Bm5+yrez26Y1Zbh/rsRrSn7pl1DY32gsN1ee3iqNZGAHlg6DRLp44N0yIwtXdjUFkVIRvPurio8IRqvHrtrpLOjRwWgzBnVTL1vXj8fio1ujT3FhOOyIsxG7fn8Gt6yF7xhhNgzJXOHuf+5MwN1OBa8fou62At/q19K7kuzN3DTLOoF3eOdWtmdz7h7QwXzDQu5rW4z44rKX5b0CkV+waSFg/bajLX5A7nx2JsNAQVBokHh7eElHhIfjm/3q69LsptaKQ5ULjwHGdlO2fMKR1ChbuOunW79zQq7FNap09cXauPLZIqeXWsQBjR/m4yDA8+et2m5/NuMK2fOnZIttUQz1SIw0tNTHa7uPJcZHIO19qvj/tkkyMbt8A3645rMIoatYiJQ4ZdWPRrmEC3liwx+fHt/Tfay7m/v9fvyYYllkfr823HtOUUe7NCt42QJt0SSJ3DPJRRbTXTCX9f910zOm2v9zZG53T7VdKvKVvE3yy7KDbxx/fJRWXdUrF9Z+ucft3nWld33Ea4b1DmJ5K+scZHUJSXKTLi5qrAqLQEIG7B7dwOOXeLcP6g3xku/poplGKiCcfxvcPta1MpSYhBK7rlWH12Je3dMfihweiWbJtf5VELgS3sfLxwVoPwez723rh9QkdMah1MiZmNXL+Cz4ydUwmWtV3PxAn8qULftB7aXK/i31+QkIEQkIE2lqsVevu4KKeoyAHAJ68xHlxiEs72s4cvTS+PSJNbQQ6pqlfvS1ToTVURL7AQIcUNb5zKh4Z0Qozb+pu9fj713ZFisXankBRFX9YxiFKpC69968u6NciGRmmNMFaFqlBP9/ZGzdU6zYeLJ4f187hz6rPSkaFG+9/dH0Wkn1UarZKndgI1IoKR4e0RLwc5A0FSb90lmVpVs8Pviv+1bOxzWPvWMye9mthmzZ3S1/nTVB/ubN3jT/v17wuXjHN8u96zrbh9oRu1hdWLBtLLzKtqQXgchEbe99ntw1oigdYbIT8BAMdUtTrEzvhrkHNERsZZs7t/e1uZcvauqtuLfdPcl0vsSxw4MXRVl/M9tYruGNSt0Y1rpnqkl4btaKCb0Zn/gP9cW0v25MLe2bf0xf9TQuRh2XW8+vFvp4anlnf6iSHqLrxblbvo4tKym2rblaV+9//4mibnwHAjS5coOrUKBHPjmtb4zYTujVC9owx5iAk1OLzrWGCdWrv5zdfvOgYFR6CpnVjseu5kXh4eCv888ggp+NplhyLV6pdrOnVNAn3+TjrgchTDHRINQ1Nayk6pCVqOg5HazpqIt2onWyZVvbsuLaoFRmGZimep+k9NLyVucIdXdSynuOUqyGtjbn43TJqo0eTOmiXmuD2wuxA8+QlbfDZja6VfA9mVSk/geLaHs6rOFZJ9uAiEDkWG2G8yOVoVt+VwhxCCEzIauTwebS8kBYeamyKbflZN6h1isPZmvDQEPz98EBEhYdCCIH0pBiM71Lzmtiq8Viu4wvE7AwKXIH1CU+60jAxGs113MdASZkN4tE4KQbX98pASIjAA15c7XKUTtKoTrSiJ2WeBIB6VVXe+ePru+HD692veheIhBCaVy7Uu5k3dQu4NNCnx9Y8G6BXb1+tTMPj6tJ9WPWvdmwEDr5knM0ZmlkPN/fJwI29M/D6hI74brJrRXsAY6+55y+3319teNt6dh9v0zAeD5q+d6JNZcN/usNY5XTTU8a2BJV2mpipXQ6fSGsMdEg1qYnRWPDgAK2HAUD9XPTXJ3TCjxa9ROJdLO5Q3ex7+iLJznoSIQSWPjpY0aaXN7uQL65XvZraT+1LiAl3ubAGGc0Y316xBo3+ZmCrFJvmx3oSGxnqdnn2cBdnMu/WWVngpDh1nofZ9/ZFuApl+TPq2g+gqtJk2zSIx1OXtsX0sW0xvksaejr4zKrJN7fa9v9y9PzGR4Xj3mrrZro2NhZESIyJwNOXZiLJi9d6U9N60V/v0jYVnchdDHQoKDw03PPGbq5IiAm3ms4PDw3BYyPdb+TZLlX9ijl64O1px1VZXFuglEnd0xGl08aRwW77MyNVm5W7sU+GKvvVm/iocCREKxNE1Y+PQlbj2nj5ig6KXnRypHdzz/oARUfYrhO9qU8Th+m8r0/o6HSfw9vWx/4XR6Njo0SPxkSkFQY6FBS0SNP6V0/Xc+W1oFT3cU+421+n+iLe0e1daxpKRPbV9XElQmcaJkSrVjY/QqEZnWWPDcKsO3pjYjf9lIy3Z2K3Rvixhubc1blalEJvzZCJXMFAh4LC6PYN8Me9fX16zHidV0Zzp5O3ltJqR2N6tXUHUeGhTFGjgHbHQPWasv71QH/V9u2pjLqx2PTUcFX2nVbbvXU6PZsmIatxbTROijEHXxOyGmla4GRSt0aYc28/l7aNiwxDNxeacxMFAwY6FBQiwkLY5KwaX6ReKMFegzwAWDdtqI9HQoGqv52eJ1q7ScXUshY1VDAMRI+Nci+N+LvJPfHt5J6YfU9fXNYpFWPaN7ApsexrCdHhyGyo3ndYBztp053TE1U7HpGvMNChoCGEwP1DfNvk7F2LBnKkrPDQEPzPtDA2JsI/gjbSpweG6a8nSHmluwmegaFdanyNfcQ80bVxbXRIsz6R/+oW24X+ADDGlBYbHhqCWlHhePrSTPz3GnUqwunJL3aKDOg9K4HIFQx0KKjc7+IJjVINJkd3aIDaKuWdk7G53te39sBtA9RL8yHyN/68luK3u/riFhUqQlafkelbbRavqulz9d5CQghNGw7PN6UZhqlQOc6SvdeMJwV1iPSGgQ4FrXrxkeard5a++b8ePgtO7h2srxKvemSw0/vBUp/mdXW3sJr8S0aS5w1+9ahZsv/+e9SqMtci5WK6XtXne12Lktbfm/rc1FWpzLWnWtarhff+1QU39MpQ/VjLpwy2up9aO3B6rVHwYqBDQafqJOCG3hl2G5r2blbXZ1fw2NDRufzicq2HEHDeuaaLw15Ewai2jnvpeCIjKdbcNJJsVRUnWDdtGMZ3SUXDxChk1I3FggcH4Ja+TTUena1R7RtYtS9QCwu8UCBioENBJ+9cKQDgzoGcTfn0xiyth+CU2ikbwWhMhwaoHcuTGj2rV8vzWcqXxrfHvPv7oXX94Co6UJMQcXEG3SAvzhI/N64dfrvbWJGzeUocornejyigaNdIg0gjg1qnYHfuOQDa9pLRg+bJPBEi0iNvShknxUUiKS4SndMTscv0WVfl/WuDs0CKEAIPDm+FTumJaGyRqhgbGYbYyOD+Hqjumu7p+GbNYa2HQaQIvrsp6LxyZQcYDMbb1/RIR3xUOB79aYu2gyIiUtmlHRtiZLvgbrY7uHU9rYegW5FhIRjYMhkvXN4OeedLmf5IAYGpaxR0IsNCzekJMRFhuKJrGh53s88CEQWWRnW48JqCW3hoCGbe3B1CCHx0fRYiwniKSP6Pr2IKeqEhguWJiYKcnoozDGyVrPUQiIgCAgMdIpPvTOVFg0kdnZVSJSLgkg4NFdlPvWqVumKYikREQYaBDpFJuBeLf/1VHBfhEunOlV3TFNnPbf2bYckjA8337x/WQpH9EhH5i+A7syMiIqqmvNJYcviOgYGTxhodEWquMBYaItAggeuQiCi4MNAhMmmcFIMRbVmRhygYDW6dgvQ6MXhsJAuTEBEFCuatEJnUjYvEB9fpv4EmESnv0o4NcUmH4C69TEQUaLya0RFC1BFC/CWE2Gv6f20723QSQqwUQmwXQmwRQkz05phEFFxOnS/VeggUJIQQWg8B4zunaj0EIqKA4W3q2hQAC6WULQAsNN2vrhjA9VLKtgBGAnhTCJHo5XGJ/Ea71AQ0rRtr92cGg/TxaPzPjb2baD0EIp+5e3BzxfdZPz4KDROjnG9IRBRgvE1dGwdgoOn25wAWA3jMcgMp5R6L28eEECcBJAPI9/LYRH7hrUmdUV5pwCt/7sas9TlWPyu4UK7RqPxHr2b66W9C5I9m3twNITqYrSIi8jVvZ3TqSSmPm27nAqhxJbcQojuACAD7vTwukd+oHRuBlPgou6WcQ0O0rwcyMauR1kMgIpOkuEjF99m6fjxa1qul+H6JiPTO6YyOEGIBgPp2fjTV8o6UUgohHObhCCEaAPgSwA1SSoODbSYDmAwA6enpzoZGRAoICeGVXiK9SIgO13oIREQBw2mgI6Uc6uhnQogTQogGUsrjpkDmpIPt4gH8AWCqlHJVDcf6EMCHAJCVlcXFC0RERERE5BFv1+j8BuAGADNM//+1+gZCiAgAvwD4Qko5y8vjERGRAka1a4BaUZw9ICKiwOXtAoEZAIYJIfYCGGq6DyFElhDiY9M2EwD0B3CjEGKT6b9OXh6XyO/Y69FRabCbxUmkuks7NsTLV3TQehhERESq8WpGR0p5GsAQO4+vA3Cr6fZXAL7y5jhEgSAro47NY1ERoRqMhIiIiCjwaV/yiSiIRYcz0CEiIiJSAwMdIh8a2iZF6yEQERERBQUGOkQ+9NH1WVoPgYh0qkNagtZDICIKKAx0iHxI6LA7eaM60VoPgYgAdLezjo+IiDzHQIdIQzE6KEbwf/2aaj0EIgKQGMNy30RESmKgQ6Shid3StR4CwkP5MUBk6etbe2g9BCIiUgDPcIh87OlLM823Y3Uwo0NE1vo0r4vuTXyTRrbkkYE+OQ4RUTBioEPkY53Ta2s9BL8x86ZuWg+BglTTurE+OU7jJN8ch4goGDHQISJdenNiJwxsxXLcpI3asRFaD4GIiLzEQIeIdGd851Rc1jlV62FQELtjYDOfHatbhnGWNzoizGfHJCIKBvxUJSIiqiY+Sv0KaO1TjX1zZt7UHesPnUWXxkxrJSJSEmd0iAgAS9sS+dp3k3sCAGIjw9C/ZTLiInntkYhISQx0iMiuW/o20ezYUaxGR0EgKpyvcyIiNfHyERHpyhsTO6JX07paD4NIVbGRoQgNEVoPg4gooHFGh4hsTLfo9eMLP93R23z78s5pqJ8Q5dPjE/naXw8M0HoIREQBj4EOEQGwXnydleGbZolVIkL5UUT6c13Pxqrtu2FitGr7JiIiI55dEBEOvjTaZhalQ1qCz8exZfpwnx+TyJEru6apst+ruzdSZb9ERGSNgQ4RQQjbtQLjOhn72FStI/BFVbYwrlkgHenYKBHbnxmh+H5fGt9B8X0SEZEtBjpE5FCDhCikVkux2fXcSI1GQ+R7YaEMvomI/BWrrhH5WMPEiyliIXZmUvTks5u6IUQIvP7XHrRPTcDCnSdUKYkbE8kyuxT4ru2Zjq9WHdZ6GEREQYOBDpGPpdSKQrvUeGw7WogQnadqta4fDwB4/9quAIC7BjVX/BjX92qM9Doxiu+XSEnR4aG4UF6p9TCIiMgNTF0j0oC/d0BfPmUwbh/Q1Obxq7unu/T7l3c2rv/p0zwJj49qo+jYiPQoKjyEldaIiHzMv8+2iPzU85e1w4nCUq2H4ZCzRoapidGYMqoN3l9ywOrxOBdT0MJCBMJDBR4d0RrREaEorzR4PFYiNVWVPm+cFINduec83s+NvZvg1r5NzUE+ERGpjzM6RBponlILfZrX1XoYVqpimyGtU9CqXi3F9tu2Ybzdx/c8PwodGyUqdhwiNVRVJIyJCMWELM/LTU8Z1RoRYSFokMBZHSIiX2GgQ0QAgAeHtcKjI1vhw+uzFF079NpVHe0+blnSOixE4JUrOiBahUIHRFp7bGRrrYdARBSUGOgQEQCge5M6uHNgc6dpa5ZeGt/e6TYJ0eG4f2gLq8eqF5sTQmBCt0Z2+/kQ+bMbe2fgjoHNtB4GEVFQ4hodIvLY1d3TcVXXNMxan4NT50tRcKEcgG2FqvuHtsSbC/YCAO4a2AyXdGyoyXiJPFUvPsr5RnY8fWmmwiMhIiJXMdAhIq+EhYZgkqna2gt/7AAAvHJlBxw8VYRGdaJR33SC+PIVHVAnNgLDMutpNlYiT8y5tx/qxkVgc04BfliX49Lv3Nq3CT5edpCzlEREGmKgQ0SKu9TOjM3Ebo00GAmR9zJNBTWGZbo+qzOuUyo+WX5QrSEREZELGOgQkWImdktHcq1IrYdBpLn2aQk48OJorYdBRBTUvAp0hBB1AHwPIANANoAJUsqz1bZpDOAXGAsfhAN4W0r5vjfHJSJ9ap4Sh+YpcVoPg0gXmLZGRKQtb6uuTQGwUErZAsBC0/3qjgPoJaXsBKAHgClCCK5EJiIiIiIi1Xgb6IwD8Lnp9ucALqu+gZSyTEpZ1QI+UoFjEhERaapX0yQkRocDACZmcf0ZEZEeeRt01JNSHjfdzgVgt5ySEKKREGILgCMAXpZSHvPyuERERJoZ0CoZDw1vCQB4YnQb3NynCQBjb6lHR7bScmhERGTidI2OEGIBgPp2fjTV8o6UUgohpL19SCmPAOhgSln7nxBilpTyhJ1jTQYwGQDS09NdGD4REZG2EmLC0aKecW3a1d353UVEpBdOZ3SklEOllO3s/PcrgBNCiAYAYPr/SSf7OgZgG4B+Dn7+oZQyS0qZlZyc7P6/hoiIyAfGViuhXt/DhqJERKQeb1PXfgNwg+n2DQB+rb6BECJNCBFtul0bQF8Au708LhERkWYaJkZb3R/YKhk7nh2h0WiIiMgebwOdGQCGCSH2Ahhqug8hRJYQ4mPTNm0ArBZCbAawBMBrUsqtXh6XiIhIN4QQiIlgazoiIj3x6lNZSnkawBA7j68DcKvp9l8AOnhzHCIiIr1IjDFWW0uKY3NcIiI9Y6lnIiIiFyVEh6NhgjFtbWTb+lg7dajGIyIiIkc4z05EROSib/+vJ6LCjdcIQ0IEkmtxVoeISK8Y6BAREbkos2G81kMgIiIXMXWNiIiIiIgCDgMdIiIiIiIKOAx0iIiIiIgo4DDQISIiIiKigMNAh4iIiIiIAg4DHSIiIiIiCjgMdIiIiIiIKOAw0CEiIiIiooDDQIeIiIiIiAIOAx0iIiIiIgo4Qkqp9RjsEkLkATik9Tgs1AVwSutBkBU+J/rD50R/+JzoD58TfeHzoT98TvRH789JYyllcvUHdRvo6I0QYp2UMkvrcdBFfE70h8+J/vA50R8+J/rC50N/+Jzoj78+J0xdIyIiIiKigMNAh4iIiIiIAg4DHdd9qPUAyAafE/3hc6I/fE70h8+JvvD50B8+J/rjl88J1+gQEREREVHA4YwOEREREREFHAY6TgghRgohdgsh9gkhpmg9HgKEEJ8KIU4KIbZpPRYChBCNhBCLhBA7hBDbhRD3aT2mYCeEiBJCrBFCbDY9J89oPSYyEkKECiE2CiFmaz0WAoQQ2UKIrUKITUKIdVqPhwAhRKIQYpYQYpcQYqcQopfWYwpmQohWpvdH1X+FQoj7tR6Xq5i6VgMhRCiAPQCGAcgBsBbA1VLKHZoOLMgJIfoDOA/gCyllO63HE+yEEA0ANJBSbhBC1AKwHsBlfJ9oRwghAMRKKc8LIcIBLANwn5RylcZDC3pCiAcBZAGIl1JeovV4gp0QIhtAlpRSz/1BgooQ4nMAS6WUHwshIgDESCnzNR4WwXxefBRADymlnnpdOsQZnZp1B7BPSnlASlkG4DsA4zQeU9CTUv4D4IzW4yAjKeVxKeUG0+1zAHYCSNV2VMFNGp033Q03/cerWhoTQqQBGAPgY63HQqRHQogEAP0BfAIAUsoyBjm6MgTAfn8JcgAGOs6kAjhicT8HPIEjckgIkQGgM4DVGg8l6JlSpDYBOAngLyklnxPtvQngUQAGjcdBF0kA84UQ64UQk7UeDKEJgDwAn5lSPD8WQsRqPSgymwTgW60H4Q4GOkSkCCFEHICfANwvpSzUejzBTkpZKaXsBCANQHchBNM8NSSEuATASSnleq3HQlb6Sim7ABgF4C5TajRpJwxAFwDvSSk7AygCwPXROmBKIxwL4Eetx+IOBjo1OwqgkcX9NNNjRGTBtA7kJwBfSyl/1no8dJEp7WMRgJEaDyXY9QEw1rQm5DsAg4UQX2k7JJJSHjX9/ySAX2BMWSft5ADIsZiBngVj4EPaGwVgg5TyhNYDcQcDnZqtBdBCCNHEFMlOAvCbxmMi0hXTwvdPAOyUUr6u9XgIEEIkCyESTbejYSyoskvTQQU5KeXjUso0KWUGjN8lf0spr9V4WEFNCBFrKqACU3rUcACs5qkhKWUugCNCiFamh4YAYGEbfbgafpa2BhinCMkBKWWFEOJuAH8CCAXwqZRyu8bDCnpCiG8BDARQVwiRA+BpKeUn2o4qqPUBcB2AraY1IQDwhJRyjnZDCnoNAHxuqpATAuAHKSXLGRNZqwfgF+O1GoQB+EZKOU/bIRGAewB8bbrAfADATRqPJ+iZLgQMA3Cb1mNxF8tLExERERFRwGHqGhERERERBRwGOkREREREFHAY6BARERERUcBhoENERERERAGHgQ4REREREQUcBjpERKQZIUSSEGKT6b9cIcRR0+3zQoh3tR4fERH5L5aXJiIiXRBCTAdwXkr5mtZjISIi/8cZHSIi0h0hxEAhxGzT7elCiM+FEEuFEIeEEOOFEK8IIbYKIeYJIcJN23UVQiwRQqwXQvwphGig7b+CiIi0xECHiIj8QTMAgwGMBfAVgEVSyvYALgAYYwp23gZwpZSyK4BPAbyg1WCJiEh7YVoPgIiIyAVzpZTlQoitAEIBzDM9vhVABoBWANoB+EsIAdM2xzUYJxER6QQDHSIi8gelACClNAghyuXFBaYGGL/LBIDtUspeWg2QiIj0halrREQUCHYDSBZC9AIAIUS4EKKtxmMiIiINMdAhIiK/J6UsA3AlgJeFEJsBbALQW9NBERGRplhemoiIiIiIAg5ndIiIiIiIKOAw0CEiIiIiooDDQIeIiIiIiAIOAx0iIiIiIgo4DHSIiIiIiCjgMNAhIiIiIqKAw0CHiIiIiIgCDgMdIiIiIiIKOP8PAc7dgpDEHfgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 5))\n", + "display.waveshow(y2, sr=sr2)\n", + "plt.savefig('spec1.png')" + ] + }, + { + "cell_type": "markdown", + "id": "d7f4e028", + "metadata": {}, + "source": [ + "#### Menghitung jarak normlisasi antara dua suara\n", + "#### Semakin dekat dengan angka 0, semakin mirip/sama kedua suara tersebut" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "id": "31bc2889", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized distance between the two sounds: 159990.09994506836\n" + ] + } + ], + "source": [ + "from dtw import dtw\n", + "from numpy.linalg import norm\n", + "\n", + "dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T, dist=lambda x, y: norm(x - y, ord=1))\n", + "print('Normalized distance between the two sounds: '+ dist.__str__())" + ] + }, + { + "cell_type": "markdown", + "id": "38e21fb6", + "metadata": {}, + "source": [ + "#### Menghitung kesamaan/kemiripan dua suara dengan sumus cosine_similarity" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "id": "ccb9aca0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6655466708653589\n" + ] + } + ], + "source": [ + "# def cosine_similarity(a,b):\n", + "# return dot(a,b) / ( (dot(a,a) **.5) * (dot(b,b) ** .5) )\n", + "\n", + "def dot(A,B): \n", + " return (sum(a*b for a,b in zip(A,B)))\n", + "\n", + "def cosine_similarity(a,b):\n", + " return dot(a,b) / ( (dot(a,a) **.5) * (dot(b,b) ** .5) )\n", + " \n", + "# def cosine_similarity1(v1,v2):\n", + "# # \"compute cosine similarity of v1 to v2: (v1 dot v2)/{||v1||*||v2||)\"\n", + "# sumxx, sumxy, sumyy = 0, 0, 0\n", + "# for i in range(len(v1)):\n", + "# x = v1[i]; y = v2[i]\n", + "# sumxx += x*x\n", + "# sumyy += y*y\n", + "# sumxy += x*y\n", + "# return sumxy/math.sqrt(sumxx*sumyy)\n", + "\n", + "array1 = []\n", + "for nums in mfcc1:\n", + " for val in nums:\n", + " array1.append(val)\n", + " \n", + "array2 = []\n", + "for nums in mfcc2:\n", + " for val in nums:\n", + " array2.append(val)\n", + " \n", + "# print(cosine_similarity1(array1, array2))\n", + "print(cosine_similarity(array1, array2))" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "id": "f85a033e", + "metadata": {}, + "outputs": [], + "source": [ + "# hop_length = 1024\n", + "# y_ref, sr = librosa.load(\"t1.wav\")\n", + "# y_comp, sr = librosa.load(\"coba.wav\")\n", + "# chroma_ref = librosa.feature.chroma_cqt(y=y_ref, \n", + "# sr=sr,hop_length=hop_length)\n", + "# chroma_comp = librosa.feature.chroma_cqt(y=y_comp, \n", + "# sr=sr, hop_length=hop_length)\n", + "\n", + "# x_ref = librosa.feature.stack_memory(\n", + "# chroma_ref, n_steps=10, delay=3)\n", + "# x_comp = librosa.feature.stack_memory(\n", + "# chroma_comp, n_steps=10, delay=3)\n", + "# xsim = librosa.segment.cross_similarity(x_comp, x_ref)\n", + "\n", + "# # print(xsim)\n", + "\n", + "# fig, ax = plt.subplots()\n", + "# display.specshow(xsim, x_axis='s', y_axis='time', hop_length=hop_length, ax=ax)\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "id": "d9433586", + "metadata": {}, + "outputs": [], + "source": [ + "# plt.imshow(cost.T, origin='lower', cmap=plt.get_cmap('gray'), interpolation='nearest')\n", + "# plt.plot(path[0], path[1], 'w') #creating plot for DTW\n", + "\n", + "# plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Pengujian Notebook.ipynb b/Pengujian Notebook.ipynb new file mode 100644 index 0000000..9f83eac --- /dev/null +++ b/Pengujian Notebook.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7d2216e0", + "metadata": {}, + "source": [ + "### Load Suara Ke Librosa dan Menghitung nilai MFCC\n", + "#### MFCC (Mel-frequency cepstral coefficients) dihitung dalam bentuk 2D array (List) " + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "55e239c0", + "metadata": {}, + "outputs": [], + "source": [ + "import librosa\n", + "import librosa.display as display\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "y1, sr1 = librosa.load('bacaan/ustazah/1/ustazah/1/1.wav')\n", + "y2, sr2 = librosa.load('1~12.wav')\n", + "\n", + "\n", + "# plt.subplot(1, 2, 1) \n", + "mfcc1 = librosa.feature.mfcc(y1,sr1) #Computing MFCC values\n", + "# # print(mfcc1)\n", + "# display.specshow(mfcc1)\n", + "\n", + "# plt.subplot(1, 2, 2)\n", + "mfcc2 = librosa.feature.mfcc(y2,sr2) \n", + "# # print(mfcc2)\n", + "# display.specshow(mfcc2)" + ] + }, + { + "cell_type": "markdown", + "id": "1b86b52f", + "metadata": {}, + "source": [ + "#### Menampilkan visualisasi suara 1 dalam bentuk waveshow " + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "b181d059", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tOGR6Xu/WaMctb+dhg8KaQ31xrLrF5UKsekOXW2sh/e2rvfhU4SbK6oMVuGKpscW3uz9PIqK+8saIzgwABVLKQimlHsAHAC603kFKWSSl3AXA+fg8Eaniq12uF0kk8rU0F/NmXLEecTFrd1AWdtroAXbb6lq7GwycUBidMZeeuXK0ugV5prk8N7+dh99/uBMA0NTW/frv9p7Ab97bZvnaeoQIAN7ZcNSt0Y4V+8qxrsA+IWrRG1DVyy52c59cjVfXFNptb+voRLXpWN/vO2Ep2evJ+obJG+uK8M7Go3b7bCqswcZC5cTMWemf2ZvrjuDplYdc7kdEBHgn0RkIoNjq6xLTNiIKEnd/uEPtECiEOUo2eis+qnt0RympcaXIqrtbcrTW7vnyBueJQWuH+9+HdTMCc+OEfWUNlm3WoxltHV0Yed+3NnN1PJ2X89AXe20SEkejtp09tisleP9YfhBn//tnAHA6n2j8Q99h9cEKu+3L95ThzXVH7LbXt3TYfD3sr8tQ4aD8z+zfK/Px9Mr+U95HRJ4JqGYEQohbhRB5Qoi8yspKtcMh6rMWvQFLvt2vdhhuYwUJ+ZK5JM26NC3vaK3lcXmj4wSjyUXHtpZedF+rdHIeZ8xr/phbYgNAaV2r5bE5ketL8gUAhi7j683zgrzhwIlGnKg3Jg2rD1Rg8iPfK+43/N5lKKhodHqs/IpG1DQbR7uidbalhNZlbK0dnSg0tfW29tiy/Xj4q31224VC84eGNmPyc/Wrm3DgRIPd875SWNmEz7dzviJRqPFGolMKYLDV14NM23pNSrlUSpkrpcxNS0vzQmhE6njwi7146Sf7EhCi/mL1wQpc+cpGm21pccqlaRHh9h9FzQoJTF8TCV+wLgtzVSKm77QfBSmpbVXY0+jqVzfhqGn06dmV+Sircz7KYdbW0Ynjda24+MV1NtvzKxrRaFU2t7/MNoGoabYdWemN859baylrc6SrDwN6awuqsLvE9Vwgb3l+dQHuNpUYElHo8MYszy0ARgohhsGY4FwB4EovHJcoaO077r87kUSBaOPhaqw3TZJ31T5ZSWdfro574a0N9vNHvMnVKKn1SFTPXdcWVGFUeiwA4KmVhzA8LQYAMOeJHzBhYALChMDjv5yI+Ehj+V2z3oADJxqw+NPdaNV34mB5IyYOTFA8b0VjG857Zg2Klix0Gt9fPt2FlBgd7jl3jGXb4k93IUqnsdtXzV4mhs4uhGuc37Nt1Xda4i6pbUFKTITd99GzhI+IQoPHIzpSSgOAOwF8B2A/gI+klHuFEI8IIS4AACHESUKIEgCXAXhZCLHX0/MSkXed9a8f1Q6BQkiFVZmYeXFOVxeTqw92lyx7MsrgK61WHck6Op1/LwWV7o8+KZV7WY9eHTY9X1zbim/3nMA3u8tsRnneWFeEc59egx3FdThYbixD2+2gM5o7+aOhswsfbinGu5uO2Wz/YEsxlu854foADlQ2tkOa0roPNh+zrCX07e4yvL2+yD5WU7a4s7gOFY32o1ofbD6G2U+sdnnesQ8utyz0OvuJ1W43M/jPjwX4YkefClSIKEB4pW+nlHIZgGU9tj1o9XgLjCVtRP1CRR/nAqjpsMLFFlFvVTS24c73tltGIaw5eo8pzbPpbccwf+jZGc2Zvsx7czUfqS9ctYvuqaCiCSPu+9by9eYjNYrfyz+WH0ByjA4A8NOhSpTWdjd6cPRzOumxlcgdkgQAWPzZblwwOQsA8H/f7LfMeapv7bCUwpmT3QtfWIeLpmTh6Sum2hxvz/F6m+YJUkoIISClxH83HsXVJw+xzAM6VN6Ik4YmA1AuG1SK+YnlB5EeH4ELp7C/ElGw8s0CBUT9XKQ2oPp8EPlNSW0rNh+psRm92eXGuivBLFIbhrZedGTzpzEPLMfIAcYyuPv+txtnjjG21X565SGMMG3/audxy6Kl5qYDZpe/vAGj0+Psjvvij4ctj19be8Qy72ddQRUe/dq+8YBZfWv3SN2XO41t7a070N31wQ7EmNZO6pISq/aXAwCqe8TVU2FlE878108oWrIQrR2deOCLvVgwMRMpptFEbZjzv8m1ViOI+eWNuOHNLQCAuEj77nxEFDyY6BARkddtteqq1tduZ8HC0yQnRqdRbL7gTF2LXrGcS0l+hbEM7t1Nx/D5dmMp1tMr8zE2Mx6AcdTGXPJmzdwWuzcjTSW19guxWo8q9Uyk7F/fPdqyJr8ST3530K3z9uU9Zv6+YiPCbW5OHa1uscShM83/efHHAlQ0tOHhCyb0+jxEpB4mOkRERG7wVde3uEhtrxOduz/cgWiFxgCuWJ/HnIBYJznWc4/M3eJclexZN47QWI2cSADH61oxID7SMiKjVWgc0OwgkfJ1g4Db3slDVxfw/q0n22xvbLedH/bVzuN4bc0RVDfrmegQBRnW1xCRxcNf7vVrS1cKLcU1LahpMl7QJkSx5Mddzfrez80pq2/zeF7dkSr71ytts55zqJT0WCeA9S3dIzY7i+twypIfbPZVen1Dm/L3r7R4qXXpm7W+JEXrD1djQ2G13XbrOUldUuK372+3GdVqN3SipLYFv3t/u6W0jogCE0d0iMjizfVFCBMCEwcpt6YlckRKiUv+s97SSjopWuvwopRsNTq40A9EVU3OS8+O19snJy1WiZyruTbWDC4621kzLzTaF6Pu/9Yyj8lhLFaJ1JvrivD4twcAAFFaDc4am26zb3VTO5rbO5GdEt3nmIjIOziiQ0Q2EqO1ltp8Inf97v3tqGxstyQ3gTo5n/yv3QvvhZ3FdVj47BoAgMbUSc165EXjotmAM3pDl8u5Q+YOc4DySJO1v3y6C3OfdN32moh8j4kOkQ84W/U80K0+WIGxDy5XOwwKMl/tKlM7BAoxZVajQw1tBuw1LcQ8IN7YSa3cRcLhC1uP1qDdagHckroWjLh3GbYerbGU/R6vcx1XV5fEmvxKl/sRkWeY6BCRjTI3PqSJiNxV1+p+uZq7Tv/nahSYusl9srXE0tBg9YFybDpSY7f/gRONuOPdbQCM83n+9tXePq119Mv/bLBZNHXv8QYYuiSufW0zfv3uVgBw2CSiRW/A+5uNi7AWVDbhmtc29z4AIuoVztEhIiKvc1XeQ/2HL8oYi6q621j/6eOdmDMyFQBww5t5SIy2b4Tx+rojlsetHZ14Y11Rn8/daDUfqK7F+LhZ3wkJ+4Ruw+FqPLsqH+/fejK2Ha3DXz/bjUUzsm3WDiIi3+GIDpGXBfuaIfwAJqJgsya/yvLYnHx4g/UaQGYpMRFuvz6vqEaxsxsR+QdHdIi87Ka3tqgdgkcqgjxRIyICgB3Fddh+rM6tfc3zgQ6caECrVXJTVG2/AKoz1o1c3tt0FOUOFnUNszRUkBCmx0TkfUx0iLxsn2nCLBFRfxMfGe5wXRx/+3BLMb7fZ7/OzbEax8nLuU+vQYpVh7XeGvvgcssaUvf+bw9iTPN1Khvboe/sToLM83ikBJjnEPkOEx0iLzP4eDVvf9lTWo9BSVFIjO77hz4R9S/p8ZFoaGtyvaOKlBZFtWa9gGt4mIChS9qUJDe6WLPHelFU86fB3CdXY0Sa87V6iMj7OEeHyMu0mr7fngukG3vnP7cW/15xSO0wiIhsKM2bUVLnYMFaTZjzv7TWzROUblw5upXVorD+mHlbq74Tu0vrnZ6XiLyPiQ6Ryqw/cgNtLOhgeaPaIVAAk1Ki2EkZEPU/J+p9322v1s1mA4ZO5W5vw1JjPDq/9To63tBu6MQfPtrh1WMSkRETHSIv2FJUg3s/2w0AMHT2Ll0JtOTGWpRWeT0IIsDY6WrOP7gCfKDzZJS5txrbA2N+DgDUOxjRaVUYeemNTg/Lkzs6u2xGpRrbDPhsW6lHx1TT59tLIdmtkwIUEx0iL/g4rxjvbT6GlfvKAzpx6a3Wjk68sDpf7TAoQHn7zjb5Rkcvb76ECr2DER1fiwh3fmn1wOd78KuXN/opGt8qqmrG3R/uQEOrfxLcf353EP/6/qBfzkWhgYkOkRd8lFcCALj57TyVI/GuoqoWPPkd5+mQsuom4wRtR3fOidRUXNOqdgiKPthSjOpm+8VFg5F5DpJ1Awdfen51AZ77ocAv56LQwK5rRAEsTACB0MTNXJbA9R7IWpKpDe/xusC8oCRS0qHSSI+Sb3aX4b8bj6odRp+ZO9D1tmSbyF+Y6BB5yJe1yWonOdJUiHft65tRVt+GRScNxtWzhiAinHN3KLC6BBK5y9eLIrvbLAEAfvv+dsvjFr0B0brguixT+zOKyBWWrhF5KJTnYJY3GC8I1uRXoaCiCY9+s59dtogo6MRFBH4CsTa/CgCC8m9sY3vfyld3l9SjotH9Tn2uWoMT9cREh8hDIZznENlZf7jKrkHFR3nFKkVD5J5A6gbnyOqDldAbujDnH6vR4qc5L97S1xt+v3h+LW58c4vb+3va8Y76HyY6RB5ytUq2t6l9P2v1gUpMf3SFylGQt3V2Sfznx8OWUsxrXtuELUU1AIBnV+XjlZ8LAQBPfX/IrkHFG+uK/BorUSiKjwq3lAs3tQVHolPeYByNqW3pe3OF8DBeipLv8N1FFGTUvp/13A/5IdMxKJRIKS2TrI9UNePSl9ZbnvvPjwWoMnVIM3R2WZKZioY27DGt1l7V1I4nlh+wtIxek1+FTYXVAICnVhzCY8v2AwDyjtb65xsi6oeCrRT67g93AACa2/u+NlFTL0bbWLpGvcVEh4h6pcF0p3FLUQ0OnGiwbF+xrxxDF3+jVlj93j+/P4hpjxhH2g6UNSCvqDsheWL5QazYVw4AOO+ZNbjq1U0AgH+vPITzn1sLoHuksMFqhFJvtU5OuMIFBhNeIu95+adCjHlgOQBgxt9X4eCJRpUjcp8nTXm0vUheWLpGvcVEh4j65LKXNuDuD3Zg6OJv8MOB8qCcQBvsrnt9Mz7bZlzDad/xBjS2G/DsynxUWSUgJbXG34tWY/xzn1/RZBnFsV7/pkGhBDNCq7GUZibH6GxWcweAv36224vfDRFZC4a27WMz4gAAzlYeaOvodJoIhWvcuxT1d5k4hQYmOkTUZwdMdxxvfDMPK/cbRwwKKhp71UWH+u6nQ5WWn3uNKbl5auUhPP9Dd7OAez7ZBQA2SYrBdFd02e4Tlm3mG6WdXRI7i+sAAHtK6zHNaj6W+W4zEfneDVaT9HveZFDbmAe+xTsbihATaexm56xl95gHlmPYX5fheF2r4qi/dLMg+/u95X0Llvo1JjpEHupNfXEoK6s3JjfznvoZN7zhfhcdcl9Xl8RKUwlatWnOTUKUcdHOnSX1lv3MbcH1hi4UVTUDMNa2H602Po7SauwuOK58ZSMAoLndgL+b5uPsKqlHh2khwJ4XMgueWeO9b4yIFHV2SXR2SYx5YLnqyc7Qxd9gq2mOXltHF6qb9dAIAQEgItz15WSz1Wfl7e9sxU1vGT8n3C1H0zo5h97QxbI2UsREh8hDhZXNvdrf23MpA2VqZrSuexHR2mY9TlmySrEcivquuLYFN7+dh1Z9J/7z42EAxuTHmQit8fdS1diOv5hGd3rOrbnqlY2oajJue2NdETYdMXZbazc4XkF+X1mDw+eIyDvO+ffP+Nf3BwHYJgr+Zm4hX9XUbrm5p7UqOfvLp7vxi+ec3/wwfx60dXRi+d4TWLW/QnG/bcdqsSa/0m67dXn0hsPVqLX6OzbjsZX45X/W272GiIkOkYeufX1zr/b39k2nQLmHtfd494Vvm6ELx+vacLSqBXUetB0lW+auajVWP9OWjk6Hd3qP1TSjwtT+9ZNtJdhoSmB6NhZYd7ja8vjdTccsj5UaEBCR/xRUNuF70yju/7aX4ocD6pRvfb69FADw2bYS7CqpAwAYOiVqmvWWz6DdpQ3o6OyyzAvsydxG+piL+ZzXv74Z17xm/FwtrmmxNEV58ruDln0WvbIRj3y9z/J1XWsHdphKbomsMdEh8rH+eKloni/yz+8PYsojK/DP7w5aPiipb+54dyvu/mAHAODjvGK8uvYIACA2Ihzf7CpTfM21r21Gs96YBFl3UDO4mW2faOBcKyK1mZuG/N83+3Hjm3mWRMOfZg5LAQDklzdZRla04QJdPZoM3PPJLsx+YrXl67J6+4YKpT2aLOh6NCOwLgef84/VlhGtnhpaO1Bc04L3NxtvzlhXFZgVVjZh6OJvXI58U+gKVzsAomDVqu+EO+uc9ec/r+auQc+vLsDU7ERsKarB/QvHobG9A4ZOiYe+2Iszxw5AmDCWPuz92zmIiej+s7T6QDm6JHC4sgmX5w5GYrROrW9FVVJKm8YBT6/sbjZw8ESD5YO+p+P13YmK0kWAtwn07/c7kS9U9pgf983uMnR0SkwfkuS3GMwdz6YPScLaAuMIcGVDu035GtA96iylhBACN72ZZ3nOvBaXed6gWUltK346VIkBcREYmxmPgYlRKK5ttTS1efnnQlwza4hdTOEagWdW5eOTrcbOky36TuSXN2JkurET3KbCahwsNzbMKW9sQ2ZClGc/BApKTHSIHDhwogFdXcC4rHgAwLHqFkhIFFQ0ISxMhNSEeyF8s1BdQpTW8nj7sTpsP1aHK07KxkUvrrNMHF2xv7sUY/xD3xn/nxWP1o5Om/lPbR1duDx3MDISItHY1oHHlx3AhIEJWFtQiRevmg7AOF9FCOOIUkpshPe/IR+rb+nAPZ/uxMvX5NpsX/LtAYev2XPcvbkyh3s5l6wvmOQQ+d7LPxXi5Z8KMTgpCj/9+QyE+aPE1HSKj01JBaC8eKf5b/ad72/HC1dOsyQaALDZVDp7qNx2faDqZj2ue30ztBqBPX87B8W1xhtkMx5bZdnnkhft599YN1sxO/vfP+Pbu+YAAH61dKNle15RLX77/g/49NezMH1Isuvvl0IGEx3qtzo6u9AlJSLCbe90v7nuCAYmReOWt/McvDIwObub7upOu3WS48278nlHa+22/eL5tS5ft1fh4v2pFYfw1IpDivtf/OI6HK1qQU2LHtnJ0ThW04LN952F+pYOHK5sxmmj0qDVCHRJQOegc09lYzt2FNfh7HHpLuPzlcKqJnzXo4VqfWsHXv650OFr9E4aBhBR6CqubcUZ//wRr12fixED4lDR2Ib6lg7LiIY3NbTaN5apbtbbrMUFdDcp+WZXGSoa1it2QlP6+w4AEwcmYPT9yi3sldpXVzXpEaUwUv3A53vsPns+3GJsprCxsIaJTj/DRIdCjpQSXbL7btPPhyqRGK3FpEGJNvvN/Psq1DTrUbRkIU57cjWGJEfjiUsn4eGv9ikcNTCFie7mBkrJiTlp6U3iEox35bcfq7M8Nk90tb4baG3kgFis+MNpAIzJblObAVOt1opJjNbiljk5uCx3EL7fewKX5Q5GfWsHonXhKKpqxpiMOLR0dCI+Uqt4fE+0dRiTltK6VgxMNJZZBMOigUq8kTBbv7+JyN7Rmhb88aOd+PC2WbjxzS3YU9qAoiULvXb8ri6JWUtWWVrWWzN0dVmWFQCMn7nWic2WIvsbXQBwoKxRcfs2q7/j7thdWo9R6bF222ua7RvgrC2oAgCbTm3UPwhnq9W6fRAhzgXwDAANgFellEt6PB8B4G0A0wFUA/iVlLLI2TFzc3NlXl5w3VEn39tf1oBLX1qPD2+dhUPljfjhQAWev3IapJRoN3RhXUEVbnrL+L754jenYnRGnGWRw//eNBNvbTiCFfuUW1oS9UZqrA5VTXq8e/NMnDoiFceqW7C9uBa1zXrMzElBl5QYn5XQq2NuLKzGFUs3YuGkTLxw5TSU1rXiVy9vQEltcCY7aupLohXIc4z6EhsTxf6paMlCvLPxKB75ai/yH1uAmmY94iPDEa4Jw/ZjtUiNjcDg5GiXx2nr6MT2Y3VY9MpGl/sCxqYC+k7fjTCHhwm3GqlEaTVodbLm0IgBsVjx+7lYW1CFxjYDFkzM9GaYpBIhxFYpZa7ddk8THSGEBsAhAGcDKAGwBcAiKeU+q33uADBJSnm7EOIKABdLKX/l7LhMdKgnKSVueHMLfjxo21//jNFp2FVSb7c2iBp8NdeFgtNPfz4d5z+3Frsemg8huuvZ2zo6EantLrmQUqK0rhWPfr3PrnQtlLh74e2thEPpfK5iMJ/b3f1CmavvUa2fgTfPG+q/x4unDsT/TB0vX7hyGn7z3jYAQOHfF6C2RY91h6txweQsAMCfPt4JKSX+dfkUrNhXHnTl233lzREwUo8vE51ZAB6WUp5j+vqvACClfNxqn+9M+2wQQoQDOAEgTTo5ORMdAowXgCv3V/SbP7iBLtQvCtRw4ZQsfLHjuNph+ESgvV98eSPCnMaq/f06+5n7coTHm0mRr36WgXwjSs1/K89cMQV3mVrX90cb/3oWbnxzC/aVNeCVa3NVnadJfeco0fHGHJ2BAIqtvi4BMNPRPlJKgxCiHkAKgCovnN8v7nh3q017V0/1rGX1NV14mM8mLWclRNq0sTVLj49AZWM7SydCCH+V3tebJMd8MRRoCYQjgRajLy9yzYdW83fj6tyu/hY7SoTc+Z48fb4v+/b2Z93z9692aZ914qXmvxVvJDl9fd8LGH8O5t+DO8mot/+Nnfx493xORzdVBydHobiGJcQ99VwSIhAF1IKhQohbhRB5Qoi8yspK1y/wE72hy6tJDgC/JjmAbzszKSU5AFDewCSH3OOt5qihvjir7PF/Cjxq/m48Pbejv9eB+n7z1ffrL4E6utQXff1WJGx/D+78TNT4sTHJUWZu8hDIvJGGlQIYbPX1INM2pX1KTKVrCTA2JbAhpVwKYClgLF3zQmxeoQsPw7LfzcHmI9UYkhKDw5VNiNBq8NWO49h2rBaGLolJgxLQqu9ERkIkrjl5CD7YUozs5GiU1beipLYVM4elYPiAGGw4XI19xxuw9Nrp2F1aj3c2HEWYEMg7WovEaC3uOWcMdOFhqG5qR2FlM6ZmJ6JF3wldeBj2lNYjIyES+RVNyEmNwdHqFlwxYzCufGUTAOC2uTmYNCgR0ToNDlc2ITFah2M1LTg5JxkDE6NQ2diON9YXYVNhDaqajB1UMhMiccHkLLz8cyEumTYQn20rxR/OHoVZw1Pww4EK7Cmtx6zhKZg0MBFf7izFtbOG4vW1RzB3VBqO17fiSGUz/nzuaBRWNuObXWWYPDgRRVXNSI7R4Remut+1BZVIj4vEsz/ko77VgPjIcNwyJwc1LXqsza/CBZOzIASws7gOz/5QgKcun4x/LD8YFKuyB8ud9UDnrZ8hfxfu4fuWlPjjfWF9DkeP3eXpiIxaIzrmUQu1R5T8ydFITZhpeyD9GK6amY3fnTUSz6zKx3ubjuH+hWNxzvgMzPnHart9x2TE4Y/zR+PjvGLcNW8krnxlE8ZnxeNfl0/Ga2uO4KdDlbhr3kgcKGvEG+uOoFlvbJKw6o+n4ZWfC/HBlmK8cf1JePnnw9hYaFxnaFp2IuIitRiUFAVNmMDYzHgcq2lBQUUTLps+CN/vK4dWE4a9x+sRH6lFpFaDlfvLceXMbAxMjMKT3x3s1febEqODoUvijNFpOF7fZlnv6OScZEtMAPDbM0fguR8KLF/fPHsY5gdBmZ835uiEw9iM4CwYE5otAK6UUu612uc3ACZaNSO4REp5ubPjco4OmR2va8XNb+VZ+vOTevrTB7MvzMpJwZjMOLyxrsjSQWjlH+biqe8PYdke744akz1PL6xdHRsujunJWlee8EbJo6fx2fzse1z0BvLcGbOev99QulmQd/885P7fSrXDUM3OB+fjzfVFePHHAmy+dx4Sor2/dAD5ns+aEZgOvgDA0zC2l35dSvmYEOIRAHlSyi+FEJEA3gEwFUANgCuklI5XwAMTHVL21PcH8azVHQUAeOj8cdhVWm/pLEN9E0of3IFi98Pz8fTKfDxw/jiX+za2deDjvBI88rWxYeXkQQnYWVLv6xD9qucFrSdzQtw5vtJx+D4Pbfz9ArER4WhqNwAAPr59Fi57aQNOzknG07+aapmPYu40VljZhJw041o0qw9UoKy+DVfOzMbhyibc9s5WFFQ0uX3eYPrZLz5vDJZ8ewAAu66FCp8mOr7ARIeUlDe0YelPh3HvwnHYWVKHA2WNuHJmtuX52mY9HvhiD7YU1WDVH09HbEQ4hi7+BgDw85/PwMr9J/DC6sOYNzYdH+YZe2hcNCULn4do16u+CvWRm8FJUSj2cG2aG04Zir1lDfj3r6ZgYGIUGto6UFbXhuqmdozKMK5Mnhob0atjmtfR+d2ZI/CH+aMhpcQDX+zBfzce8yhWUtbzwiyYLtTc4a/vJ9R+bsGuaMlCbD1ag8+2leKxiyfaPFfR2IZoXThi3ZhALqVEVZMeJz3m3miPViPQ0em7d4K7n0uZCZE2C5laG50Rh4unDsTtpw1HRUMb2jq6kJ3iek0hCny+7LpG5Dfp8ZF44BfjAQDTspMwLTvJ5vmkGB2ev3KazbY195yBaJ0GKbERuHF2Dm6cnQMAaNYbcKK+DU9fMdVSD3vbacMtiVEwcFXy0deEJVSTnIQoLa6amY17zh1j2dbW0YmrX92EvKO1AIALJmfh4qkDMTMnGbtL6jEzJwVSSggh0NHZBa1GuYdLfKQW8RlaAHF9jq/L9IO//tRhAAAhBG48dVhQJjreSJaV3t/evKjueZxQe9v76/sJtZ9bsDlzzAC8fv1JuPXtPOSbRmCmD0nG9CHJdvsOiIt0+7hCCKTFReDWuTlY+rN9Ec5FU7KwJr/KsoadO4t5AsDQlGgUVbe4HQdg/HevdPhxmfF2Ze1RVmuUmUWEh6Hd0IU5I1Jx+2nDAQAD4t3/WVDwYqJDIc/RCtDWCdGjF02wPP7otllIidXh/OfWolXfiWGpMZg7MhVvbTjq81h7y9WAbCAmLGeOGYAfDlR4dIyzxgzAKqtjPHrheORXNOHtDUdx8+xheHXtERx5fAGkBMoa2pCVEGmzYKe1SK0Gn/z6FLQbOlFe325zd29mTgoAWF7rKMnxlvgoY214cozOsi0zIcqn5/QVb7z3lN7fAfiWJlLNTbOH4Y7TjRfuz105Fe0+6LB674KxaDd04q31tp+B2vCwHgsf277uz+eMVpwYnxyjU0x0pg5OxI6SOrf/3S+YmIETCiM3YzPjUVjVbLNtzshUrNxfgYwEJjf9DRMdoh5mDDPeBdt871kAgLhI48XnXxeMRZgQKK1rhZQSN765pdd3pQKVr0pPThqahC1FtTbb/jh/FGqa27GjWHn+iRDAH84eha4u4N8rD1m2P7doKuaPT0dEuAadXRLf7T2BpGgd9h6vxzWzhgIAHrnQmLDeb5oTIwQwMNG9RCEiXKN6CcOIAbF4sMd8niidBq9fn4sb31Qu5Z04MAG7S0NrLg8RuXbNyUNs5v9FhGsQEW4/muENGoUbRQlRWsRF2l5Gzh6RirUFVbh3wRjcOnc4nv+hAK0dnTb7nDQsGduO1dkdr7i2BYV/X4Bpj65AbUsH5oxMxZp8Y/tipZGbVn2nTaJlXp/w3oVj8eyiqRh+7zLLczeeOgxbimoxf1xGr793Cm5MdIgcMCc4ZuY/qMNSYwAAP/75DAAIqlI3R3x1l7y6SW95fHJOMq45eSjGZsTjk9tPgRACb6w7gkumDUJrRyf+vmw/nl801Wbk5byJGeiSEpWN7ZgxLNnyIa4JE1gwMRMAMGt4io+i979IrQY3zh5mt/3MMY5bePa80HDEWd06EQWP388bhUUzB/eqDM1T2nDjaPa1s4YgKVqHZ1blK65blmQajb51rnGUybxUBQBcP2so3txQhMFJtjeUBidH4ZPbT0GkVgMhhOmztgMvXT0d4x/6DgDw5Z2nYsR939q8LkwIjBwQi/WHjauVdHZJHHl8geUzZOdD8/HToUr87v3tGJMZj50PzffKz4KCS0AtGEpEoWX2yFRkJUbi5aun4U/zR2PhpEyEhQmEa8KgCRO4eU4OkmN0GJgYhReunGZXXjYqPQ5jMuIxZ2Saz+5UBosHFo7FJVMHAgCeuWIKzD+qISkx+NmUdPc0cWCC5bEmrPfLqUZq+RFBpLaeo9KX5Q7ya5IDdM97+WrncQwxjXwnxejQ0WlbKjc4yTbWF6/qLhG/eJrx71fPcvK02Aikx0ciwVS6W9loXOcvxtQw4aFfjEO4QtmwEAJ/OW8Mvv7tbADGNQ+tP0MSorSYOSwZE7LikcSW0f0WP8WIyOtSYo139RbNyMb6xWfhnAmZyB1qPzGW3HfTnBzcNW8kAODknBTcZrpj2tjW4bDu/MPbTkZWovG5pOjueT9ajXtJj/nCg4jUE2G64bD0mulYfvccZLlZjutNPx6sBABcOn2QJfFqbDPYzVu859wxOPL4AsvXkQqNAXJMVRFmbR22yVKm1d+zwr8vwA2n2o9yA0B2cjSideGYYLqho1eYn5QeH4mvfzfH4RxNCn1MdIg8tPzuOb3avw831oNC7pDuDngRmjDceOpQ5KTFOHkF9Zb5oiIxWgudqZQkLjLc8rin8LAwy53Yi6cOxM2msrieLWDvPGOE5XHPOUJEpJ7rThli+fc5Y1gyxmTEqxKHeS7QxEGJmG76Wz8gLgIJUVoIAWQlRFridJRUdJm6DPS8MdPVo/vAR7fPwrrFZwIAwqw+MK2bBm174Gzcu6C7e+YZY9Jw7awhffreKLQx0SHyUG/vegdiJzRvMM//yE6OxriseDz4i/H9vtzM27ISo5B3/zxEhGvw2zPNyYnxQmC4QlIphPGuK2BsanDracbW6gMToyxNNwDgT+eMxtnjjPOA5oxMxWmj0gAAnU4aON0yR/kuKxF5z98umICLpgxEfGQ4onXqTauePiQJUwYnYsqgREsZ2VFTMx4pgbvmjcSfzhnt9BjmtXu0mjAsPm8MHlg4FoCx9bO1zIQoxSYyqVbdKJNjdDblbG9cP8PSjIbIGhMdIg8p9ewPZN4cUNJZfdBMzU4EAHzzu9l48arpXjwLWTMvQmoe3TlRb1z4VGeVVA411dBrNWGYPSIVgHG9IHNdf3VTOz66bZbNcf9yrvEiJSFKiyd+OQmAsX3r+ZOMTR8yEyJtSt7uW8iRHyJfMpdwhYUJ7Hr4HIcjt/7y+W9OtXSmnD8+HfPHp1vm6DhbKDRMGEeek2N0GJdpHJG6/bThuGlOjsvXWusI1buE5FNMdIj6GW9+VMzMSca07ERsvX8eppoWb42L1Kr+gdxf3LtgDH5/9igAQHay8Q7oK9fm4o/zu++smss9rCcvm38/54zv7uYWZi43Ed2lJenxkXjy0skAgKZ2A/If6669B2CZlExE3vf3iyeqHYJDS6/JxSnDUy0j+amxOof7Fj6+ELsfPgcpsRFYdpd9qbe7jVIWmjptEvUG20sTUa+kxOhQ3azH2r+cgUitxjLCMH1IEs4YnaZydP2LuYUrAJw1Nh2aMIGzx6Xjix2llu3mzkVN7R0AgOtmDbEkOoOs2ryay0qs18toN3QhSmccKepUuJv6m9NH4J5Pd3nr2yHq1zLiIyGlRLmpnf7ojDi1Q3LJnOi4OyqjpFlvcGs/TZjw2ZpvFLqY6BBRr0QoXCQDwJTBiXjjhhlqhEQALs8djMtzBwMATh89AHefNdLy3Ls3z0TuUOOI29+s6tjvXTAWvzvTuJ95AnGs1bo8Wqs7rQaFC5lEtmwl8poLp2bhD2ePwuj7l+P5RVMxIN6/LaT7IishEsfr2yw3SvqiN+XfYaZFQYncxfoSIg/1t7aV95w7BlecNFjtMMiJhCgt7jaVtAHAqSNSFRtDaMIEEkzJSmqsDu/dMtOy3+/njcIvJmcBAN684SS8dI1xPYyzxgzwdfhE/V6wXMqb5+pFeLDmVs8W1c4wyaHe4ogOkYfiPLiTFYwmDIzHRaaFKyl0CCFwyvBUy9fmNXsA4wiR2X0Lx2LRjGyb175z0wxc89pm3wdJFMLqWzospaPW614FsmRTJ7T4yL6N7oYJjgyTb/WvKzQi8lh/G8EiWzlpschJi7XZZp6nRUR9d+XMbIRrwnDk8QVB93e2N6My1rY/OB+RvRgN0rB0jXqJiQ6Rh4Ls86hX0uMjUN7Qjt/PG4mKxnacPynLblVrIiLy3Kh0Y/OBYEpypKnIrq/LLPR2HTomOdRbTHSIPOTLDyW1O8wI06o7d80b5WJP6o940UHBaGBiFErrWn12/KRoLWpbOtza9+vfzsaGw9V4bNl+RAbZmmyA/WKfRIGG71CiAKb2ZaSbyxtQP9XYbmwLm5PGUT6ivhiXGY+LpwXvnMcorfF+uSfNCIh8ie9MIi+44dShAIAVv5+rbiBeNiI9Dm/ecJLaYVCAMk+YVuroRqS24Sol4K5uUN23YCxmj0x1sVdwSDEtFBqt88/fgGcXTcWr1073y7koNLB0jcgLbjhlGEalx2FkuncWeFO7ZM1MI2w7bhFZc7YaOgUO8yK/5B96Q5fT52+Zm4PzJmZg9hOr/RSR76THR2LNPWcgro9d13rrAlPLeyJ3cUSHyAuyU6ItLXe1Gs/rvQIhyQGA1o5OtUOgADY1OwlFSxaqHQa54M8kJzs52vVOfpLooEVzXKRn93i9PXqRHK3DkksmevWY/jQ4gH7nRD0x0SHyMhkoWYqHonUam3VViIhc8cfkdPPaLa44SkgOnGj06PyOmnCEK0xq1LnRdjksTOCKHmtTEZF3sHSNyMsMIdKJav3iMxHbzxZDJSLP+OOvn7vJVF9bHkfrNGjRd1rO1d6jFC1Kp7HbBgC68DAY9Laj4OEaAX0nMDU7EamxEVixr7xPMRFR33BEh8jLlO7qBaPEaB3C+7gIHBH1TwUVTWqHYKF1kBBNGZzo9HUDE6Msj80JTVpc96K4kU6ab4QJIN5qvop5VOmzX5+C62YNtWwPlZF/okDHqxgiLztvQobaIRAR9Xu3zx2OF6+aZrddaZFKc4nZgUfPxe2nDe/zOQsfX4hBScZE6ePbZ+HS6YMB2K+31qw3mLb3+VRE5AYmOkRe9u9fTVE7BI9kxEeqHQIRkceyEiOxYGKmW/ua2yRHajU2yceo9Fi7fQ1dzruqmZ00NBkxLhoX+HLBaSLiHB0ir2O5FxGRf108dSD+t70UqbE6xEdpUVjZ7JXjhikkIg2tBrdfv2BSpmXepjc6chJR7/CKjIiIvI4jg2Tmi/fCNScPscy1+fLOUzF/XDoA4L1bTsYLV9qXq923YCyevHQSACAmQoPnr5za57KxjITu78c8nycnNQZjMuzXURueFovfnz0KADB9SBI+u+MUAM7n+RCR9zDRIfKBpGj/LJ7mC+Oz4jFpUILaYRAROfToRRMwwNQgYNKgRMtI+qj0OAxJsV/XJSFKi8tyTfNlIHD+pL4tPLntgbNx5pjuRZTHZsZjwcQMvHPzTPzn6ukAgLrWDsXXhmvCMC07CQAwNDUGm+89q08xEJH7mOgQ+UBMELdlnjw4EV/eOVvtMCjI3L9wrNohUIjJtBo5GZcZjzvPGAEAKKtvAwAMiI9QfJ0vJcfobEaCUmJ0ePGq6RiYGIV008jVrJwUm85tjgzgqCeRzzHRISIb9Q7uRhI5c/OcHEzPTsLYzHgA3l89PpSFSEd6h6IjPH8vpMTq8KdzRjt8vtPNBgFKFk7MRJaLxKSysd3yeHxWgqUcT6n87eELxmPVH0/rczxE5D1MdIjI4rlFU3HznGFqh0FB6r1bZ+KRC8cDAAqrvDMZvD8I9BHglBid5bH1KIuZdbvmCQPjLY91pnVsIqzmo1ivR+NKVC+S5aRoneudejCvZfPCVdMQHxlut90mFqvFRy+dPgjrFp+JB88fh+tPHWq3ryZMILKPi5USkXcx0SEii19MzkJmguuSCyIlEeEaaEJ4eCLCwQKUnhrQi4t/s+lDknDG6DSPzmuePJ9slciMGGDfTtnVpH1za2YAaO/oHlkZlxmPI48vQFdXd+agUTiYo2YF1gmWmdIaOH11zclDcOWMbLvt4Vbd0cKEwKo/nob4KNtkVBMmcOPsYRiTEd/z5UQUQAL7NhIREVGAGJISjUPlTXbbNWECnV19X+r+cB9aIf/fRRMwKj0Ow+9d1qvXDU6OQnFNKwBYRh3SYiNQ06wHYFtGFx8ZjoY2A6J14QD0Do9pPWLTM48RQqCyqbvsq1NhuMRRIuXrka5HL5qgfF6d7XmHp8Xi0QsnoLaFZb1EwYYjOkRE5HULJ3Uv1DguM7TvenuS5PRVmBBuj56dM97YennJJRMtrZdfunqapczwoqkDcatCyap5McueoxndMRj/r7Ma6Zo4MBGnDE+x2c+67MtVR8qTc5ItraJPHZ6Kn/58utP9zYalxlhGuMxr3/RmdLFF32l5PHFQAq6eaRzpaWgzJjfzx2fgVycNdvt4RBQYPEp0hBDJQogVQoh80/+THOy3XAhRJ4T42pPzEQWLjs6+T4wlCmaj0uNw91kjbeY8KM3rCEZKczcCXdGShZg+xPjRfMWMbAyIM/4uzp2QiUmDEgEYy+DuXTgOADA4KRqTB3e3l9/54HzF+S/PXDEFr11/EgDgL+eMxktXGxOocVnxeO+WkxVjidSG2XQjWzTDmDjEWb1XHr1wAl4ytWk2dEkMSYlBQpQWIwfYr1FjbUB8JN64YYbpPBp8/dvZSLSKu8vFL896bZz0+Ej838UTAbCpBlGw83REZzGAVVLKkQBWmb5W8iSAazw8F1HQSInxf9tTT92oMKmWqLdiI8Jx99mjoDd0X1jqTYn/qHT7+R9A98Wk9UVwTmqMD6PsG+vJ8a5Wue/LBXKsValWVC8ms8dFhNuMqgB9n8uSmRiJL35jbC+vCRNI6DECYx4tuXDKQJwx2riezMj0OJw7IROuHHj0PIw2zWn57I5TcM74DADAU5dPwXs3zwRgLGMLM43EpJrm/qz642m451z7jmvzx2Xg4qkDFc81YWB3sjZiQCymmZK9nNQYTM1OdBkrALx+XS6e+OUkt/YlosDkaQHshQBONz1+C8CPAP7Scycp5SohxOk9txNR4HjwF+PVDoFCSKrVBPXGNoNbrxmVHovSOuP8kXAXiYSnIsLD0G7o3cirTuP+vcFBSVGK83mUDE+LsZunMzi5+/VnjE7D6oOVuGn2MAxLjUFnl8TQ1O5FMa89ZQjuOH0EHlu2H3UteizbfQLZydHYXVpvdy53Gyo8csF4uyTn7xdPQGObAY9/e8CtYzgzLTsJGw5XA7BNSpSkxirfOJo7Kg1zR7luyLDyD92tnr///Vy3S9rOHJvu1n5EFLg8TXTSpZRlpscnAPCvAhGA8yZkYF9Zg9phEKnmoqkDEanV4JlV+S5HP5T0HKHwtitOGoy3Nhz16TmcSY+PQHlDu+Jzf5o/CqV1bThU3oRXrs1FcowWqw9W4oHzxynuLyAQExGOv188EY1tHRieGosfD1Uq7psUo8PB/zvXZXzXnjLUbtuVM4dg+Z4Tdtt9/bvypnAHyWpf2lMTUeBz+ddJCLFSCLFH4b8LrfeTUkoAHlUwCyFuFULkCSHyKiuV/0gTBYM7zhiBl6+ZrnYYRKoZmxmP3589CkD33JYTphXtAdv1VAwKk/kjTZ28rEu4HJW+qWFwcveIykAXi00qjQQ5Ky2788yRSI4xPn/2uPRedR+Li9Tijz0W1pyVk4rzrZpDWHdJmzQoAYOT+95S/sCj57osk7Oeg+OuP549yq3RGm+566yR+OI3p/rtfETkHy7/+kgp5zl6TghRLoTIlFKWCSEyAVR4EoyUcimApQCQm5sbhNM+iYw0YcJSfx4MrMuMiLzNfKEerTO2KwaAIcnRltXmU2N0OG6VBFmzLjNSam4WrdPYdMxyZkxGHA6caOxN6ACAGJ0GzfpONFudJzlah0LYlpuNSo91Wq6mCw+DXqFcTheugVYjkBClRVO7e2V+rswbm25JQCYOSsDzpm5rPX1552yXxzp99ABLgxVzFzIz64Uxz5uQgZnDku1e/8/LJqO+1b41c88KspNzkpEWa2wK8NuzRirGEqkNQ32ry5B7LSlGhySFdXuIKLh5Wrr2JYDrACwx/f8LjyMiIr9bem2u2iFQCAt3c06EqzkzjVYX2UpJhVYj0NFpmw1ZJxfWIwuFVfZr14wcEIv8CseJSkZ8BPaXOXzahnnNleQYHRKjtCisasaItFhLSav1KI1OI5D/2ALM+ccPlm3m7mh9dde8kZC9bBM3IC4C47Ps58vcNHuYpVmJs8VN/3O17Si2+ffuaA5ObI+Rng9uneUyxicvnYxmLyWDRBT6PE10lgD4SAhxE4CjAC4HACFELoDbpZQ3m75eA2AMgFghRAmAm6SU33l4biLykimmNrNEamp2MTIj4DxhGpYaY5f8OHrFzuI6u21hVitXKs2hEY5WtuzhullDEKnT4OWfCvH+LSejS0qc98waY4OBikYYOiXmj8vAkksm4Zynf7a8LtKqpGzRjGycNXaAy3MtPncM5o9Xnh7rbrxm6xef6XAOi/lYp41Kw96/nePyWE9eOgk5afad8y6ckgWdB40m/FnORkTBz6NER0pZDeAshe15AG62+nqOJ+chIt/59q45lnauRGpSGvnxVilXT2My4rG5qMZmm7SaZmqexxImgOiIcDTrOxGpdT6tVRNmfP5vF07A93uNk/ZHZ3Sv//Lnc8bgvgXjMPfJ1UiJ0WF0RhwWTMzAtGxj6+Mlv5xoKefThYdhUFI0XLn99OEu93GXoyTHmhDCrTlDl+UqL645NjMeY00LyPamhTYRUV94OqJDREFubIivWk+kxFzGFq4RMJjK3azbYJuTmlevzUWkToPJgxLx341HsWy3MYGxnoBf3tCGlX84DY98vc9hadvtp+UgOzkauvAwfHf3XEt76Bev6i73mj7Efn5LKMtJi8Xme+3ulRIReU3w9IQkIqJ+SakCq7ndvQYEjpjnAxk6nc9jOXNsOk4ZnoqYiHDccOowy4X5yPQ47H/E2KY5SqfBiAG2HeF6HnXxeWMtbZhHZ8TZdD5zJTEqdCfJD4j3bC4SEZEzTHSIiMinBiX1vX2xI5kJnl0gW5fEKS0gGRdp3zJZFx5muTCXUiJK5zhZUeoy1lcZCZE48vgCrx2PiKi/YKJDREQ+s/pPp+Mv543B7ofn22xvMxhHZCZkxeMkhZbEnjKvueNovKZF353o9FzQ9NNfz8K9C8Y6PX5CtPO1Y8J62QjAld42FiAiIs7RISIiHxqWat95a2hKNIalxmJPaQM+uG0WNELgix3HMSo9DoWVTZbWzM50udk6WWndGkfS4iJQVt9mmSuT/9h5br+WiIgCD0d0iIjILyYMTEBOWgx+/PMZyB1i7DQWGxGOCNPclV+fNhxr/nKmW8dy1Y3NvIio9bovl0wbhLtMC1EmRtvOezny+AK7tWu0bnQhi4sMt5wjy6qczo2XEhGRj3FEh4iI/OJfl092OsISFmZMfACgo8t+xKazFwtgdnTan2dcZjwWzcjGM6vyMSQ5Gh/ccjJy7l0GoPelYebStHdvnmlpLPDX88bilrk5AICzx2Xgpav5EUtEpCb+FSYiIr+wbsnszPxx6Tg5J8VmUc8xGXHITo7G9/vK3TpGuxslaz3Xj/r16cMxa3iKy9f989JJmDvauHDlJKvFdhOitZa5O7ER4Th3QoZbsRIRkW9wcJ2oHxudHud6JyI/W3ptLs4el26zbfndczEsrXu+j6uWy4NNi21aNwUwKIwSWZs+JAk3zR7mMr5LcwfblbkREVHgYaJDRER+NyQlGj27OltXpg1OjsaBR891+PqsRMctl1+7Lhe/OWMEAODehWPx4PnjAADNLub1EBFRaGGiQ9SPXTR1oNohUD91+ugByH/MmKgIAZw1ZgASe7RsjtQ6X1RTCAGlaTtnjU1HVqJxxOWCyVm40ckozYgBsRiXGd/L6ImIKBhwjg5RP3bDqUPVDoH6MfNCnUIIvHb9SX06hlLTAVcitWHISTOus/P2jTMQrrBgKBERBT8mOkT91IyhyS7vmBOpbXBSlCUhsmbudHbT7GEormnBoYomZCfH4FB5k+JxFkzMwCjTnLRNf52H2Ejjx19WYpSPIiciIrUx0SHqpwYnR6sdApFLb9xwEgBjomNuLLD0mukYayo3++uCsZBSQgiBx77Z5/A4L1413fI4Idq97m9ERBTcmOgQ9VMXTMlSOwQil0YM6O4MeNXMbAxNicb88bZtm81r4Fi3ix6dEYcnfjnRP0ESEVFAYqJD1E+dNipN7RCIemVQUjR+dVK2w+evmzXU0jI9IlzjdF8iIgp97LpGREQhISsxCpdMG6R2GEREFCCY6BARERERUchhokNERERERCGHiQ4REREREYUcJjpEIW5UeqzaIRARERH5HRMdohDX2GZQOwQiIiIiv2OiQxTiuqS0PE6J0QEA8u6fp1Y4RERERH7BdXSIQpyAsPn6yOMLLAssEhEREYUqjugQ+dCFU7LUDsEOkxwiIiLqD5joEPmQTuPff2JDUqL9ej4iIiKiQMVEh8iHbp2bg8cvnuiXc/1p/ih8fsepfjkXERERUaBjokPkQyPT47BoZrZfznXznBwkmZoNEBEREfV3bEZAFILS4yNQ3tBus23SoAQMTIxSKSIiIiIi/2KiQxSCEqK0KG9oR+7QJAxLicHHW0vw35tnQhvGQVwiIiLqH5joEIWg3KHJCBMCH982C0erW/Dx1hLER2rVDouIiIjIb5joEIWYD289GVOyExERrgEADE2NQdGShSpHRURERORfTHSIQszMnBS1QyAiIiJSHQv2iYiIiIgo5DDRISIiIiKikONRoiOESBZCrBBC5Jv+n6SwzxQhxAYhxF4hxC4hxK88OScR2Zo5LBm/OWM4IsJ534KIiIjIzNMro8UAVkkpRwJYZfq6pxYA10opxwM4F8DTQohED89LRCbROg3+fM4YCCHUDoWIiIgoYHia6FwI4C3T47cAXNRzBynlISllvunxcQAVANI8PC9RSInSatQOgYiIiCikeJropEspy0yPTwBId7azEGIGAB2Awx6elyiknDKcndKIiIiIvMlle2khxEoAGQpP3Wf9hZRSCiGkk+NkAngHwHVSyi4H+9wK4FYAyM7OdhUaUVDLSYtBYWWz2mEQERERhSSXiY6Ucp6j54QQ5UKITCllmSmRqXCwXzyAbwDcJ6Xc6ORcSwEsBYDc3FyHSRNRKIiN4DJWRERERL7iaenalwCuMz2+DsAXPXcQQugA/A/A21LKTzw8HxERERERkUueJjpLAJwthMgHMM/0NYQQuUKIV037XA5gLoDrhRA7TP9N8fC8RCHpjNG979ORFK3zQSREREREwc2j2hkpZTWAsxS25wG42fT4vwD+68l5iELBwMQolNa1evWYn91xCgYlRnn1mEREREShgCsMEvmJJsy4zs2o9FiH+7QbFPt0ODQtOwkD4iM9iouIiIgoFDHRIfKz8VkJdttaOzoBAAPiIvwdDhEREVFIYqJD5Cc1zXoAQEen/ajN+sPVAIDGNoNfYyIiIiIKVUx0iPzEXLpW39ph95x5wdBhqTF+jYmIiIgoVDHRIfKDy6YPwqIZxkVwU2Lsu6SNGGCct2NOhoiIiIjIM1yxkMgPnrxsMgDgpZ8OQwjHyUxTO0vXiIiIiLyBIzpEfmbokgCAXSX1ds9xRIeIiIjIO5joEPlZUrRW7RCIiIiIQh4THSI/iwg3/rNzN+GZPiTJl+EQERERhSQmOkR+Ni4rHst+N8ft/bOTo30YDREREVFoYqJD5Ec3zR6GGcNSMC4r3qPjnDVmgJciIiIiIgpN7LpG5EcPnD/O7X1TY3WoatL7MBoiIiKi0MURHaIgpO/sUjsEIiIiooDGRIcoCCVEsXMbERERkTNMdIgCUGqsDkNSYhw+r9Xwny4RERGRM7xaIgogXdK4mOiHt83Cs4umqhwNERERUfBiMwIilYQJYbft8tzBSI+PxPC0WADGtXYGxEVYno/WadCi70SUTuO3OImIiIiCERMdIpXct3As6lr0eOTr/ZZtkwYlYtKgRMvX2x+cjzfXHbF8nRyjQ4u+FZdOH4SLpw70Z7hEREREQYWla0QquWTaINw4O6dXr6k2tZuO0YXjpKHJvgiLiIiIKCQw0SEKIuEaY7lbU3uHypEQERERBTYmOkRBqEuqHQERERFRYGOiQ6QyTZjAUCetpImIiIio99iMgEhle/92DjRhAseqW9QOhYiIiChkMNEhUlmk1tgq+uELx6scCREREVHoYOkaUYAzcEIOERERUa8x0SEKcDlpnL9DRERE1FtMdIgC3OmjBmDjX89SOwwiIiKioMJEhyjAhYUJZCREqh0GERERUVBhokNERERERCGHiQ4REREREYUctpcmChJJ0Vqkx0fi2llDMXFggtrhEBEREQU0JjpEQeK9W06GLjwMw9Ni1Q6FiIiIKOAx0SEKEmMz49UOgYiIiChocI4OERERERGFHCY6REREREQUcjxKdIQQyUKIFUKIfNP/kxT2GSKE2CaE2CGE2CuEuN2TcxIREREREbni6YjOYgCrpJQjAawyfd1TGYBZUsopAGYCWCyEyPLwvERERERERA55muhcCOAt0+O3AFzUcwcppV5K2W76MsIL5yQiIiIiInLK06QjXUpZZnp8AkC60k5CiMFCiF0AigE8IaU87uF5iYiIiIiIHHLZXloIsRJAhsJT91l/IaWUQgipdAwpZTGASaaStc+FEJ9IKcsVznUrgFsBIDs7243wiYiIiIiI7LlMdKSU8xw9J4QoF0JkSinLhBCZACpcHOu4EGIPgDkAPlF4fimApQCQm5urmDQRERERERG54mnp2pcArjM9vg7AFz13EEIMEkJEmR4nAZgN4KCH5yUiIiIiInLI00RnCYCzhRD5AOaZvoYQIlcI8appn7EANgkhdgL4CcA/pZS7PTwvERERERGRQ0LKwKwQE0JUAjiqdhxWUgFUqR0EUR/wvUvBiu9dClZ871IwCub37RApZVrPjQGb6AQaIUSelDJX7TiIeovvXQpWfO9SsOJ7l4JRKL5vuaYNERERERGFHCY6REREREQUcpjouG+p2gEQ9RHfuxSs+N6lYMX3LgWjkHvfco4OERERERGFHI7oEBERERFRyGGi44IQ4lwhxEEhRIEQYrHa8RA54uq9KoS4XghRKYTYYfrvZjXiJHJFCPG6EKJCCLFH7ViIHHH1PhVCnC6EqLf6m/ugv2MkcocQYrAQYrUQYp8QYq8Q4i61Y/IWlq45IYTQADgE4GwAJQC2AFgkpdynamBEPbjzXhVCXA8gV0p5pypBErlJCDEXQBOAt6WUE9SOh0iJq/epEOJ0AH+SUp7v59CIekUIkQkgU0q5TQgRB2ArgItC4XqXIzrOzQBQIKUslFLqAXwA4EKVYyJSwvcqhQwp5c8AatSOg8gZvk8pVEgpy6SU20yPGwHsBzBQ3ai8g4mOcwMBFFt9XYIQ+cVTyHH3vfpLIcQuIcQnQojB/gmNiKjfmiWE2CmE+FYIMV7tYIhcEUIMBTAVwCaVQ/EKJjpE/cdXAIZKKScBWAHgLZXjISIKZdsADJFSTgbwHIDP1Q2HyDkhRCyATwHcLaVsUDseb2Ci41wpAOu73oNM24gCjcv3qpSyWkrZbvryVQDT/RQbEVG/I6VskFI2mR4vA6AVQqSqHBaRIiGEFsYk510p5Wdqx+MtTHSc2wJgpBBimBBCB+AKAF+qHBOREpfvVdNkQ7MLYKzBJSIiHxBCZAghhOnxDBivuarVjYrInul9+hqA/VLKp9SOx5vC1Q4gkEkpDUKIOwF8B0AD4HUp5V6VwyKy4+i9KoR4BECelPJLAL8TQlwAwADjBNrrVQuYyAkhxPsATgeQKoQoAfCQlPI1daMisqX0PgWgBQAp5UsALgXwayGEAUArgCskW91SYDoVwDUAdgshdpi23WsaiQxqbC9NREREREQhh6VrREREREQUcpjoEBERERFRyGGiQ0REREREIYeJDhERERERhRwmOkREREREFHKY6BARkWqEEClCiB2m/04IIUpNj5uEEC+qHR8REQUvtpcmIqKAIIR4GECTlPKfasdCRETBjyM6REQUcIQQpwshvjY9flgI8ZYQYo0Q4qgQ4hIhxD+EELuFEMuFEFrTftOFED8JIbYKIb4TQmSq+10QEZGamOgQEVEwGA7gTAAXAPgvgNVSyokwrji/0JTsPAfgUinldACvA3hMrWCJiEh94WoHQERE5IZvpZQdQojdADQAlpu27wYwFMBoABMArBBCwLRPmQpxEhFRgGCiQ0REwaAdAKSUXUKIDtk9wbQLxs8yAWCvlHKWWgESEVFgYekaERGFgoMA0oQQswBACKEVQoxXOSYiIlIREx0iIgp6Uko9gEsBPCGE2AlgB4BTVA2KiIhUxfbSREREREQUcjiiQ0REREREIYeJDhERERERhRwmOkREREREFHKY6BARERERUchhokNERERERCGHiQ4REREREYUcJjpERERERBRymOgQEREREVHI+X/NEjYlFzCpcAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# fig, ax = plt.subplots()\n", + "plt.figure(figsize=(14, 5))\n", + "display.waveshow(y1, sr=sr1)\n", + "plt.savefig('spec.png')" + ] + }, + { + "cell_type": "markdown", + "id": "802547fa", + "metadata": {}, + "source": [ + "#### Menampilkan visualisasi suara 2 dalam bentuk waveshow " + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "137ac26f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 5))\n", + "display.waveshow(y2, sr=sr2)\n", + "plt.savefig('spec1.png')" + ] + }, + { + "cell_type": "markdown", + "id": "5b8fb13c", + "metadata": {}, + "source": [ + "#### Menghitung jarak normlisasi antara dua suara\n", + "#### Semakin dekat dengan angka 0, semakin mirip/sama kedua suara tersebut" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "31bc2889", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized distance between the two sounds: 35539.630615234375\n" + ] + } + ], + "source": [ + "from dtw import dtw\n", + "from numpy.linalg import norm\n", + "\n", + "dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T, dist=lambda x, y: norm(x - y, ord=1))\n", + "print('Normalized distance between the two sounds: '+ dist.__str__())" + ] + }, + { + "cell_type": "markdown", + "id": "00f25b00", + "metadata": {}, + "source": [ + "#### Menghitung kesamaan/kemiripan dua suara dengan sumus cosine_similarity" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "ccb9aca0", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6609852797540173\n" + ] + } + ], + "source": [ + "\n", + "\n", + "def dot(A,B): \n", + " return (sum(a*b for a,b in zip(A,B)))\n", + "\n", + "def cosine_similarity(a,b):\n", + " return dot(a,b) / ( (dot(a,a) **.5) * (dot(b,b) ** .5) )\n", + " \n", + "\n", + "array1 = []\n", + "for nums in mfcc1:\n", + " for val in nums:\n", + " array1.append(val)\n", + " \n", + "array2 = []\n", + "for nums in mfcc2:\n", + " for val in nums:\n", + " array2.append(val)\n", + " \n", + "\n", + "print(cosine_similarity(array1, array2))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/app.py b/app.py new file mode 100644 index 0000000..81d3cec --- /dev/null +++ b/app.py @@ -0,0 +1,124 @@ +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() \ No newline at end of file diff --git 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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 \ No newline at end of file diff --git a/spec.png b/spec.png new file mode 100644 index 0000000..5713fa5 Binary files /dev/null and b/spec.png differ diff --git a/spec1.png b/spec1.png new file mode 100644 index 0000000..45fa22b Binary files /dev/null and b/spec1.png differ diff --git a/test.py b/test.py new file mode 100644 index 0000000..e93b1f8 --- /dev/null +++ b/test.py @@ -0,0 +1,40 @@ +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) \ No newline at end of file