Code/Audio_Spectrogram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "760454b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import librosa.display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c8e01ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y: [ 0.00066001  0.00100411  0.00090719 ... -0.00156178  0.00221853\n",
      "  0.        ] \n",
      "\n",
      "y shape: (1103,) \n",
      "\n",
      "Sample Rate (KHz): 22050 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Load the audio file\n",
    "y, sr = librosa.load('../../allwavs/allvowl/extracted/trimmed/2__f__0.00000__0.05000__2__te1__agnes2_mono.wav')\n",
    "\n",
    "print('y:', y, '\\n')\n",
    "print('y shape:', np.shape(y), '\\n')\n",
    "print('Sample Rate (KHz):', sr, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93adcfa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio File: [ 0.00066001  0.00100411  0.00090719 ... -0.00156178  0.00221853\n",
      "  0.        ] \n",
      "\n",
      "Audio File shape: (1103,)\n"
     ]
    }
   ],
   "source": [
    "# Trim leading and trailing silence from an audio signal (silence before and after the actual audio)\n",
    "audio_file, _ = librosa.effects.trim(y)\n",
    "\n",
    "print('Audio File:', audio_file, '\\n')\n",
    "print('Audio File shape:', np.shape(audio_file))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f817738a",
   "metadata": {},
   "source": [
    "Fourier Transform of the audio file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b31e812",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anfasa/.local/lib/python3.10/site-packages/librosa/util/decorators.py:88: UserWarning: n_fft=2048 is too small for input signal of length=1103\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Default FFT window size\n",
    "n_fft = 2048 # FFT window size\n",
    "hop_length = 512 # number audio of frames between STFT columns (looks like a good default)\n",
    "\n",
    "# Short-time Fourier transform (STFT)\n",
    "D = np.abs(librosa.stft(audio_file, n_fft = n_fft, hop_length = hop_length))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e98e037",
   "metadata": {},
   "source": [
    "Spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f671046",
   "metadata": {},
   "outputs": [
    {
     "data": {
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5ZJncPp8DfrzvjaR+4AvAiRGxVdJngHOACyYqfHDGHn7v+XcXWR/rcUOvGkgdgpWI9nqWoBXsSwnKLKirayIR8cwJzq0CVk3meVOW+ETEZmBz/nqnpLvJRluvAF6dX/ZV4FryxEfSfwYeAHbXPUr510xJjwNzgIab3NRG+9n9iLcYsOJM2+y9law4tSc8WN66W3jl5oOTtAx4IdkWakfmSRERsVnSwvyamWQJ0OnAh/fdGxGjkt4L3EGWEN0HnN2wzEowONOblJpZZ4pRT76wbqfJzOrqGFOe+EiaBXwP+EBE7Mj758bzCeBzEbGr/hpJA8B7yRKnB4Avkq3a+KlxyloJrARYOqfC4GEejGrFGX75a1KHYGVSceJjBfubH7S3vKAtXV1Fm9LEJ09avgd8IyK+nx9+VNKivLVnEbBvX4mXAG/Jx/DMBWqS9pK1EhER9+fPvJRsXNDT5IshrQZ44ZFDMbLTm5RacWb97JrUIViJbPnlialDMGuZu7rqKGu2+Qpwd0RcVHfqCuBM4ML8z8sBIuKVdfdeAOyKiC9JWgycKGlBRDxG1hXWcNRybayPvdtnF1UdM2Z13y821sGOeM761CGYtUhduVDwVLb4vAJ4J3CHpFvzYx8jS3gulXQW8BDw1okeEhGbJH0C+IWkUeBBsplhExreO8i6+4859OjNDjC8Z1rqEKxEjjrlntQhmLXGXV37i4h/Z/zlpAFe2+DeCw54/y/AvxQTmdmh8T9UVqTKzOHUIZi1JICaBzd3jmqtj8d2u6vLivP42mNTh2AlMmPuztQhWOnc397iQm7x6STTh4Z5wbFed8WKs/Dld6UOwUokRrrvHwyzA3lwcwfp6x9j9vztqcOwEtHcwdQhWJlsH0kdgVnLPLi5g1T6q8xYsK3xhWZNGjvuhNQhWJnUGm4pZDZJt7W3OHd1dZaoVfA6PlakwSe86a0VqOpNb637uaurg0Stwuju6anDsDK532PGrDjVx/2LmXW3CM/q6igRYmy0tNWzBMZ+50TairPXmyhb4ba3uTwR4cSnc0R3DrqyztW/2JveWnFmzftt6hDMWueurg4iqPTVUkdhZbLg8NQRWIl03z8X1vkeaXuJ3djAUN7Eh+4cdGUd7Mk9qSOwMql6Vpd1uejOf2dLm/hETd5byQpV2+QtBqw4td1DqUMwa0l4jE9nqdUqDO/1DxYrToz2pQ7BSkRDns5uXa5Ns7okfRJYAdSALcBfRMSm/Nx5wFnAGPC+iLiy0fNKm/jsGRni9oe8O7sVR/83UodgJfL49rmpQ7DS+UHbS2zTGJ+/j4iPA0h6H/C3wHsknQicAZwELAaulnRCREzYj1zaxGeof5RnLmj/QC8rr2e87PbUIViJLJvuLSusYL9of5HtGOMTETvq3s4k2xgeslagSyJiGFgvaR1wKvDriZ5X2sRn98gg1/12WeowrESeU7s+dQhWJhW3IFp3m+QYn/mS1tS9Xx0Rq5u9WdIq4F3AE8Af5oePBq6ru2xjfmxCpU18Zg6O8JIlD6YOw0pkZNvs1CFYicTv5qQOwUqnzWtDTW5W19aIWH6wk5KuBo4a59T5EXF5RJwPnJ+P6TkH+DvGXxWi4W8UpU18ACT/RmXF2bFpQeoQrES2PLowdQhWOu1fFLNW0CalEXFak5d+E/ghWeKzEVhad24JsKnRA6Ys8ZF0MfBGYEtEnJwfmwd8G1gGbADeFhHbJJ0OXAgMAiPA30TEzw543hXAsfue1Yi7uqxob176cOoQrESOeY73frOCXdPm8kJtGeMj6fiIuC9/+ybgnvz1FcA3JV1ENrj5eOCGRs+byhaf/wF8Cfha3bFzgWsi4kJJ5+bvPwpsBf4kIjZJOhm4krp+OklvBnZNpvDp/aOcPP/R1mpgVqdv0NOPrTiDh03qR5pZxwnaNqvrQknPIpvO/iDwHoCIWCvpUuAuoAqc3WhGF0xh4hMRv5C07IDDK4BX56+/ClwLfDQibqm7Zi0wTdJQRAxLmgV8CFgJXNps+XvHBrj7cTclW3Hmb9yeOgQrkbleYNUK1/5f9tuxgGFE/NkE51YBqybzvHaP8TkyIjYDRMRmSeNlJn8G3JJPTwP4JPAPwJONHi5pJVmCxILBGRw5Y3cxUZsBfX3eYsCKM+IFVq0Eat6rqzWSTgI+Dbwuf/8C4JkR8cFxWo+eJp8atxrg2bPmxrzpTnysONOme3d2K05Rg0LNkmnTGJ+itTvxeVTSory1ZxHZ0tMASFoCXAa8KyLuzw+/DHiRpA15rAslXRsRr25U0Gitn4d3zi06futhT6ybkToEK5Fu3NXaOl3Dcb2FCrozgW934nMFcCbZDK4zgcsBJM0lm552XkT8ct/FEfHPwD/n1ywDftBM0gP5ys0e3GwFetapt6UOwUrEy21Y4W5uf5HdmMBP5XT2b5ENZJ4vaSPZnPsLgUslnQU8BLw1v/wc4JnAxyV9PD/2uojYwiGKECOjA4d6u9nTjO3xmAwrTm20o0YamE1eiJp3Z39KRLz9IKdeO861nwI+1eB5G4Cm1vAxmwryFgNWoMpANXUIZi0J2rNXV9FK+yuHFEwb9CaAVpzpv+euUytQvxNp637u6uog/X1jHD53e+owrESi2n1Nuta5aju8jo8VbbjxJUUKGPPg5s4xWu3n0a3zU4dhJXLkrumpQ7ASUb/XhbLulu3O7hafjhGRJT9mZp1IHuNjJeDEp4MMj/WzfvsRqcOwElnuwc1WpGpf6gjMWuaVmzvI7Ol7ePXJt6cOw0qkf4E3lbQCOY+2EnCLT4fpxhUlrXNphr+fzMz2iXDi03EqlVrqEKxMav4V3YoTI/5+sm4nz+oyK7VBj8mwAlU9uNm6n1t8Okj/0AhHPPO3qcOwEqkdvSR1CFYiqnk6uxXt/saXFCjw4OaOMjYywBMPHZU6DCuRBdUdqUOwMql0XxeB2X7KPMZH0iuBX0XEWN2xUyIiwV6wzan0jzFz/vbUYViZeMiYFWmkzavsmhVOpW7xuRK4UdLbImLfhkX/CpwyNWG1bmTvEA+uPT51GFYiJ76yY/N860aDg6kjMGtJkK3e3C6SPgz8PbAgIrbmx84DzgLGgPdFxJWNntNs4nNvXti1ks6KiF9BG2t7CAYGRzlq6abUYZiZjSv6B1KHYNayds3qkrQUOB14qO7YicAZwEnAYuBqSSfU906Np9nEJyLiB5LuBb4t6WI6fPmt2liFvbtmpA7DymRkNHUEViazPMbHul1b9+r6HPAR4PK6YyuASyJiGFgvaR1wKvDriR7UbOIjgIi4Lx/v82/A8yYbdbt5AUMr1LSh1BFYmdQ8aMy6W8SkZnXNl7Sm7v3qiFjdzI2S3gQ8HBG3SfuVdzRwXd37jfmxCTWV+ETEC+te7wbeJukZzdybSv+0YRae1N6pfVZutXkLUodgJVLZ8mjji8w63CRafLZGxPKDnZR0NTDeVOzzgY8BrxvvtvFCahTIhImPpC82eMj7GhVwkOduAHaSDUaqRsRySW8FLgCeA5waEWvya08HLgQGgRHgbyLiZ43KiLE+RrbNPpTwzMZVmT0vdQhWIjHdP5+saDe2vcRaQcN9I+K08Y5Lei5wDLCvtWcJcLOkU8laeJbWXb4EaDi4t1GLT32z1CeAv2v0wEn4w32jsnN3Am8GvnzAdVuBP4mITZJOJpth1rApqzJ9lJkne3CzFWfPce9MHYKVSGXw8NQhWOm0N/GJNmxZERF3AAv3vc8bTpZHxFZJVwDflHQR2eDm44EbGj1zwsQnIr5aV9gH6t8XLSLuzss58PgtdW/XAtMkDeWDmQ5uDGo7vcWAFcf/UFmRBgeOSB2CWctSLmAYEWslXQrcBVSBsxvN6ILJrdxc5CyuAH4qKYAvNzvACfgz4JaDJT2SVgIrAZ5xBPQd7sGDVpzom5U6BCuRSsW/mFl3C9q/rmtELDvg/Spg1WSekWrLilfkXVcLgask3RMRv5joBkknAZ9m/AFOAOQJ1GqA5cf2BUNeJ8PMOlPNe3VZtyvjlhWSdvJUS88MSfs2KxLZ2j5zDqXQiNiU/7lF0mVk8+4PmvhIWgJcBrwrIpqaqhW1IHaPHEp4ZuMaGX4kdQhWIjG2J3UIZi0r3ZYVEVH4tANJM4FKROzMX78O+K8TXD8X+CFwXkT8sumCUrTBWanF6M7UIViJaHRX6hDMWtbOLSuKkqKr60jgsnwQcz/wzYj4iaQ/Bb4ILAB+KOnWiHg9cA7wTODjkj6eP+N1EbFlwlJqUNvtPnQrjv+hskLVqqkjMGtJIKo1Jz4NRcQDwPPHOX4ZWXfWgcc/BXxq0gVVoDLNfehWHCc+VqRQqiGWZsVxi08nGRNjO737sRWnsmtr44vMmuVZXdblghKO8elmtZEBnnxwvNWvzQ7NrMc3pg7BzKxzRLZfV7cpbeKjvhoDc3anDsNKpPI7d3VZceLRbalDMGtZUVtWtFNpE58IqI2WtnqWwl4nPlac8Gx263JBCdfx6WYx1sfIE15p14ozs9//UllxKku8SakVbeKdnIonxpz4dJJAfZ7VZQWq+vvJihMzpqcOwawlHtzcYaJWYXj3jNRhWInEFo8Zs+Jontfxse7XjesElzbxGR4ZZP2GZ6QOw0qkf2A0dQhWInOf+0DqEMxa5jE+HaSiYOa0vanDsBLZsW1u6hCsRA4b9ibKVrT2jkOMcFdXR6lUasyY7sGoVpzFJ61LHYKVSGW6N1G27ufBzWYlNrzNs3CsOP1H7EgdgllLunUvcCc+ZmZmdgjkMT6dJGpiz95pqcOwEtm97bDUIViJzBh9JHUIZi1zi49ZiQ16sLwVqDLk6ezW3dq1crOkC4C/Ah7LD30sIn6UnzsPOAsYA94XEVc2el5pE5/BaSM84/j1qcOwEpl5zObUIViJVA7rwt0dzQ5Qa9+38eci4rP1BySdCJwBnAQsBq6WdEJETLjabGXqYjQzM7OyishmdTXzNUVWAJdExHBErAfWAac2uqntLT6SlgJfA44i6x5cHRFfaNCU9Tzgy8Cc/J4XR8SE/Q7qH2Pa/O1TUgfrTX2L/Ru6FajafYNCzQ40iTE+8yWtqXu/OiJWT6KocyS9C1gD/JeI2AYcDVxXd83G/NiEUnR1VcmCvlnSbOAmSVfl58ZryuoHvg68MyJuk3QE4CV0re1G1w+mDsFKRAMe42PdbxJjfLZGxPKDnZR0NVmDyIHOB/4Z+CTZsKJPAv8AvBsYr/CGv6G2PfGJiM3A5vz1Tkl3M3GG9jrg9oi4Lb/n8abKGaswunNmq+Ga/Yf7b3926hCsRObPb+pHmdkkrGl8SYGKXMcnIk5r5jpJ/x34Qf52I7C07vQSYFOjZyQd4yNpGfBC4Pr80DmSbpd0saTD82MnACHpSkk3S/pIiljNzMxsfxHNfbVC0qK6t38K3Jm/vgI4Q9KQpGOA44EbGj0v2awuSbOA7wEfiIgdkg7WlNUP/D7wYuBJ4BpJN0XENeM8cyWwEmDpnD4qQ14S3opz3PPuSR2Clci2jUemDsGsZbVxe5sK9xlJLyDLDzYA/x9ARKyVdClwF9kwmrMbzeiCRImPpAGypOcbEfF9gIh4tO78gU1Z/ycitubnfgScAjwt8ckHSq0GOGXRoEeimpmZTZEAxtqwgmFEvHOCc6uAVZN5XopZXQK+AtwdERfVHV+Uj/+B/ZuyrgQ+ImkGMAK8Cvhco3JqYxVGnphVaOzW22Yd93DqEKxEFi3emjoEK5vvtLtAtavFp1ApWnxeAbwTuEPSrfmxjwFvP0hT1jZJFwE35ud+FBE/bFhKTVT3ehaOFacy07NwrDiaU9r1Y61XFDB+J4UUs7r+nfGnoP1ognu+Tjal3czMzDqAd2fvMH2zhpn3++tSh2FlsnRR42vMmvXYY42vMetwbdyyojClTXzMzMxs6gRM5XYUU6a0iY/6+9ARs1OHYSVSGxxKHYKVyNhDpf3xaz3EY3zMzMysZ3iMj1mJ6cldqUOwEhl70i2I1t0Ct/iYmZlZD3GLTyep1WD3k6mjsDLZtCN1BFYi1Se9ZYV1ufCsLjMzM+sR2ayu1FFMXmkTn6gGtce6sRHOOpW8ELgVqH+aN1G27ucxPmZmZtYTvHJzh4mRfvZunJ86DCuRoSO3pQ7BSmRwmb+frPt5jI+ZmZn1jC7Me5z4mJmZ2eRFwFgX9nWVOvFRpRtzUetUGhhLHYKViA6bnjoEK532D5jvwryn3ImPmZmZTY2gO8f4VFIHYGZmZt0pmvxqlaS/lnSvpLWSPlN3/DxJ6/Jzr2/mWaVt8VH/GAOH70wdhpWITpiXOgQrk2o1dQRmLWtHi4+kPwRWAM+LiGFJC/PjJwJnACcBi4GrJZ0QEROOS0jS4iPpYklbJN1Zd+zvJd0j6XZJl0mamx8fkPRVSXdIulvSeSliNjMzs/1FNPfVovcCF0bEcFZmbMmPrwAuiYjhiFgPrANObfSwVC0+/wP4EvC1umNXAedFRFXSp4HzgI8CbwWGIuK5kmYAd0n6VkRsmLCEvqDvsL1TEbv1KFWnpQ7ByuQJt0hbdwug2nxWM1/Smrr3qyNidZP3ngC8UtIqYC/w4Yi4ETgauK7uuo35sQklSXwi4heSlh1w7Kd1b68D3rLvFDBTUj8wnWzYuneLNDMzS2wSjTlbI2L5wU5Kuho4apxT55PlKocDLwVeDFwq6VhAhxJSp47xeTfw7fz1d8maszYDM4APRsTvxrtJ0kpgJcAzPBzDzMxsykSBu7NHxGkHOyfpvcD3IyKAGyTVgPlkLTxL6y5dAmxqVFbHJT6SzgeqwDfyQ6cCY2QDlw4H/q+kqyPigQPvzZvNVgMsP6YSmj5eMmh2aKpr3XVqxRnbNSd1CFY6e9pcXhDtWbv5fwGvAa6VdAIwCGwFrgC+KekishzheOCGRg/rqMRH0pnAG4HX5pkdwJ8DP4mIUWCLpF8Cy4GnJT5mZmbWHm1cx+di4OJ8QtQIcGaeI6yVdClwF1mDydmNZnRBByU+kt5ANpj5VRHxZN2ph4DXSPo6WVfXS4HPtz9CMzMzq9eOlZsjYgR4x0HOrQJWTeZ5qaazfwv4NfAsSRslnUU2y2s2cJWkWyX9S375PwKzgDuBG4F/i4jbU8RtZmZmmQDGIpr66iSpZnW9fZzDXznItbvIprRPTkUwfXDSt5kdTP8xnfU/r3W32j1ewNC6X3RYUtOMjunqMjMzs+7iTUrNzMysJ2SDm93iY2ZmZj2iTdPZC1XexEcVmDaUOgorkdpcr4ppxenbuDF1CGYtCYKqEx8zMzPrCeGuLjMzM+sh7uoyMzOznhBAzYlPB6nV4EnvrWQFmps6ACuTvkWpIzBrnRMfMzMz6xFt26S0UE58zMzMbNJCUFXDPUE7jhMfMzMzOyTu6uogMSyq6/tSh2El0rdzQ+oQrExmey9B625ZR1f3bVpR2sTHzMzMppZbfMzMzKxn1OQWHzMzM+sBWUeXEx8zMzPrCcEY1dRBTFoldQAHkjRX0ncl3SPpbkkvqzv3YUkhaX7KGM3MzHpdkHV1NfPVCknflnRr/rVB0q11586TtE7SvZJe38zzOrHF5wvATyLiLZIGgRkAkpYCpwMPNfOQsZEBdq5fPHVRWs+ZFZtTh2Al0l95MnUIZi1qT1dXRPw/+15L+gfgifz1icAZwEnAYuBqSSdExISLC3VUi4+kOcAfAF8BiIiRiNien/4c8BHowiHkZmZmJRSMNfVVBEkC3gZ8Kz+0ArgkIoYjYj2wDji10XM6KvEBjgUeA/5N0i2S/lXSTElvAh6OiNsSx2dmZmY8Nbi5mf8K8krg0Yi4L39/NPDbuvMb82MT6rSurn7gFOCvI+J6SV8ALiBrBXpdo5slrQRWAiyZ1WlVMzMzK5NgjNFmL54vaU3d+9URsXrfG0lXA0eNc9/5EXF5/vrtPNXaA6Bxg2qg07KDjcDGiLg+f/9dssTnGOC2rJWLJcDNkk6NiEfqb84/xNUAL5g/PWpjndagZV2t5u8nK05t+0DqEKx0RtpaWsBkWnO2RsTygz4r4rSJbpbUD7wZeFHd4Y3A0rr3S4BNjQLpqJ/keSLzW0nPyg+9Frg5IhZGxLKIWEZW0VMOTHrMzMysvYJaU18FOA24JyI21h27AjhD0pCkY4DjgRsaPajTWnwA/hr4Rj6j6wHgLxPHY2ZmZk8T1AoauNyEM9i/m4uIWCvpUuAuoAqc3WhGF3Rg4hMRtwITNYcta1swZmZmNq6Atm1SGhF/cZDjq4BVk3lWxyU+ZmZm1g2CWuMGlo5T2sRn954Z3HDb81OHYSVy7KMLU4dgJbL4mRtSh2Cls77N5QW15md1dYzSJj5mZmY2dQKohTcpNTMzs54QbRvjUyQnPmZmZjZ5AU1Mouo4pU18Zkzbwykn3pU6DCuRvv7u+x/cOldloJo6BLOW7NuyotuUNvExMzOzqRUe42NmZma9IaiFZ3WZmZlZT/Dg5o7SN1BlzuLHUodhJTJw2K7UIViJVHdNTx2CWUsCD242MzOznhEe42NmZma9w11dZmZm1hsiqEX3LctQ3sRHgfq6r+/ROtfIttmpQ7AS2fnI/NQhWOk80tbSPMbHzMzMekiAu7rMzMysV3hws5mZmfWI7lzHp5I6gGZJeoOkeyWtk3Ru6njMzMys1uRX5+iKFh9JfcA/AqcDG4EbJV0REQffhTREjPW1KULrBY/8ZlnqEKxE/vtNp6QOwUrn1jaXF0QXzurqlhafU4F1EfFARIwAlwArEsdkZmbW46a+xUfSCyRdJ+lWSWsknVp37ry8J+heSa9v5nndkvgcDfy27v3G/Nh+JK3MP5Q1W/d0VtOamZlZ6UQ099WazwCfiIgXAH+bv0fSicAZwEnAG4B/ynuIJtQtiY/GOfa0TzIiVkfE8ohYPn96t1TNzMysG0XT/7VcEMzJXx8GbMpfrwAuiYjhiFgPrCPrIZpQV4zxIWvhWVr3fglPVXxctzw6unX2Zx5+sIlnzwe2thBbt3P9m67/w1MaSCL++09W/39PU+z+ev3vH8r1Gfxem8u7EqrNrsQ5TdKauverI2J1k/d+ALhS0mfJGmxenh8/Griu7rpxe4MO1C2Jz43A8ZKOIfvX5wzgzye6ISIWNPNgSWsiYnnrIXYn19/1d/1d/9RxpOTP4NBFxBuKepakq4Gjxjl1PvBa4IMR8T1JbwO+ApxGk71BB+qKxCciqpLOAa4E+oCLI2Jt4rDMzMysABFx2sHOSfoa8P787XeAf81fT7o3CLpnjA8R8aOIOCEijouIVanjMTMzs7bYBLwqf/0a4L789RXAGZKG8h6h44EbGj2sK1p8plizfYxl5fr3Nte/t/V6/cGfQTf4K+ALkvqBvcBKgIhYK+lS4C6gCpwdTeyaqmh9mpmZmZlZV+iari4zMzOzVjnxMTMzs55RqsRHmf+WL199u6RxN8ORdIyk6yXdJ+nbkgbz48+W9GtJw5I+fMA9Hb9JaqMYJ/p8DnavpHmSrso/q6skHd6u+kxWi/W/WNIWSXcecE/X1B8O/TOQtFTSzyXdLWmtpPfX3dM1n0EL9Z8m6QZJt+X1/0TdPaWvf935Pkm3SPpB3bGeqL+kDZLuUL4tQt3xrqm/NSkiSvMF/BHwY7K5/S8Frj/IdZcCZ+Sv/wV4b/56IfBiYBXw4brr+4D7gWOBQeA24MTU9T2gTg1jPNjnM9G9ZEuDn5u/Phf4dOq6Fl3//NwfAKcAdx5wT1fUv4DvgUXAKfnr2cBveul7IH8/K389AFwPvLRX6l93/kPAN4Ef1B3rifoDG4D54zy3K+rvr+a/StXiQ7Z89dcicx0wV9Ki+gskiWw63HfzQ18F/jNARGyJiBuB0QOe2w2bpDYT48E+n4nuXUH2GUHdZ9WBWqk/EfEL4HfjPLdb6g8tfAYRsTkibgaIiJ3A3Ty1Amq3fAat1D8iYld+zUD+FXX3lLr+AJKWAH/MU2uk1N9T+vpPoFvqb00qW+LTzGamRwDbI6I6wTWH8tzUmonxYNdMdO+REbEZIP9zYYExF6mV+k+kW+oPBX0GkpYBLyRr9YDu+Qxaqn/ezXMrsAW4KiJ6qv7A54GP8PSttHul/gH8VNJNklbWXdMt9bcmlS3xaWb56kNZ4vqQlsVus1bq3g31a2Sq/u67ScufgaRZwPeAD0TEjgJja4eW6h8RY5Ht/rwEOFXSycWGN+UOuf6S3ghsiYibig+rbVr9/n9FRJwC/CfgbEl/UGRw1jm6PvGRdHY+GO1WstUdGy1fvZWsebN/gmsOdEjLYrdZMzEe7JqJ7n20ril8Edlvw52olfpPpFvqDy1+BpIGyJKeb0TE9+uu6ZbPoJDvgYjYDlwL7NuHqBfq/wrgTZI2kHURvUbS1/NreqH+RMS+P7cAl/HULt/dUn9rUtcnPhHxjxHxgvw3tf8FvCsfuf9S4Il9TZR11wfwc+At+aEzgcsbFPMfm6QqmwF2BtlS2Z2kmRivYPzPZ6J7ryD7jKC5zyqVVuo/kW6pP7TwGeRj374C3B0RF41zTzd8Bq3Uf4GkuQCSppNtgHhP3T2lrn9EnBcRSyJiWX7fzyLiHXX3lLr+kmZKmg0gaSbwOuDOunu6of7WrHaNom7HF1kz5j+Sjey/A1hed+5HwOL89bFk+3msI9vwbCg/fhTZbwQ7gO356zn5uT8im+lyP3B+6roepP5PixF4D/CeJj6fcetHNibqGrK9Ua4B5qWu5xTV/1vAZrKB7RuBs7qt/q18BsDvkzX53w7cmn/9Ubd9Bi3U/3nALXn97wT+tu6Zpa//Ac94NfvP6ip9/cn+Tbgt/1pLl/4M9FdzX96ywszMzHpG13d1mZmZmTXLiY+ZmZn1DCc+ZmZm1jOc+JiZmVnPcOJjZmZmPcOJj5lNSNIR+xYJlfSIpIfz17sk/VPq+MzMJsPT2c2saZIuAHZFxGdTx2Jmdijc4mNmh0TSqyX9IH99gaSvSvqppA2S3izpM5LukPSTfDsMJL1I0v/JN4K8somdsc3MCuXEx8yKchzwx8AK4OvAzyPiucAe4I/z5OeLwFsi4kXAxcCqVMGaWW/qb3yJmVlTfhwRo5LuAPqAn+TH7wCWAc8CTgauyrYGo49smxAzs7Zx4mNmRRkGiIiapNF4agBhjexnjYC1EfGyVAGambmry8za5V5ggaSXAUgakHRS4pjMrMc48TGztoiIEeAtwKcl3Ua2A/zLkwZlZj3H09nNzMysZ7jFx8zMzHqGEx8zMzPrGU58zMzMrGc48TEzM7Oe4cTHzMzMeoYTHzMzM+sZTnzMzMysZ/z/DCetrMYdeKUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert an amplitude spectrogram to Decibels-scaled spectrogram.\n",
    "DB = librosa.amplitude_to_db(D, ref = np.max)\n",
    "\n",
    "# Creating the Spectogram\n",
    "plt.figure(figsize = (10, 4))\n",
    "librosa.display.specshow(DB, sr = sr, hop_length = hop_length, x_axis = 'time', y_axis = 'log',\n",
    "                        cmap = 'inferno')\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe31c25b",
   "metadata": {},
   "source": [
    "Mel Spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a0304f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4541/2778306494.py:4: FutureWarning: Pass y=[ 0.00066001  0.00100411  0.00090719 ... -0.00156178  0.00221853\n",
      "  0.        ] as keyword args. From version 0.10 passing these as positional arguments will result in an error\n",
      "  S = librosa.feature.melspectrogram(y, sr=sr)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y, sr = librosa.load('../../allwavs/allvowl/extracted/trimmed/2__f__0.00000__0.05000__2__te1__agnes2_mono.wav')\n",
    "y, _ = librosa.effects.trim(y)\n",
    "\n",
    "S = librosa.feature.melspectrogram(y, sr=sr)\n",
    "S_DB = librosa.amplitude_to_db(S, ref=np.max)\n",
    "plt.figure(figsize = (10, 4))\n",
    "librosa.display.specshow(S_DB, sr=sr, hop_length=hop_length, x_axis = 'time', y_axis = 'log',\n",
    "                        cmap = 'cool');\n",
    "plt.colorbar();\n",
    "plt.title(\"Audio Mel Spectrogram\", fontsize = 23);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b952bb7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8712836a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}