Beat-detecttor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pianistprogrammer/anaconda3/lib/python3.7/site-packages/scipy/sparse/lil.py:512: FutureWarning: future versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning.\n",
      "  if not i.flags.writeable or i.dtype not in (np.int32, np.int64):\n",
      "/Users/pianistprogrammer/anaconda3/lib/python3.7/site-packages/scipy/sparse/lil.py:514: FutureWarning: future versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning.\n",
      "  if not j.flags.writeable or j.dtype not in (np.int32, np.int64):\n",
      "/Users/pianistprogrammer/anaconda3/lib/python3.7/site-packages/scipy/sparse/lil.py:518: FutureWarning: future versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning.\n",
      "  if not x.flags.writeable:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [[0.07984149 0.03914155 0.05658015 ... 0.         0.         0.        ]\n",
      " [0.07310947 0.07121469 0.10228164 ... 0.         0.         0.        ]\n",
      " [0.09262447 0.08783106 0.12289134 ... 0.         0.         0.        ]\n",
      " ...\n",
      " [0.         0.         0.         ... 0.16428271 0.17173768 0.17379338]\n",
      " [0.         0.         0.         ... 0.15968731 0.16562451 0.16805796]\n",
      " [0.65398648 0.63834916 0.64790381 ... 0.1653118  0.16187044 0.15556285]]\n",
      "\n",
      "\n",
      "[[1.         0.87545119 0.65408731 ... 0.52092121 0.70431281 0.79001277]\n",
      " [0.87545119 1.         0.91148996 ... 0.7257501  0.82025823 0.75104373]\n",
      " [0.65408731 0.91148996 1.         ... 0.78459335 0.81339756 0.62941686]\n",
      " ...\n",
      " [0.52092121 0.7257501  0.78459335 ... 1.         0.90017203 0.49972442]\n",
      " [0.70431281 0.82025823 0.81339756 ... 0.90017203 1.         0.77786795]\n",
      " [0.79001277 0.75104373 0.62941686 ... 0.49972442 0.77786795 1.        ]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import modules\n",
    "import librosa\n",
    "import librosa.display\n",
    "import IPython.display as ipd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "from scipy import stats\n",
    "from scipy.io import wavfile\n",
    "import numpy as np\n",
    "import math\n",
    "from sklearn.metrics import pairwise_distances\n",
    "\n",
    "\n",
    "# to get the similarity matrix between two frames of the same signal\n",
    "# read audio file\n",
    "y, sr = librosa.load('Data/genres_original/classical/classical.00011.wav', duration=10)\n",
    "#y, sr = librosa.load('mazing.wav', duration=30.0)\n",
    "\n",
    "# print(f\"Sample rate -> {sr}\")\n",
    "# print(f\"Sample size -> {y.size}\")\n",
    "\n",
    "y_comp, sr = librosa.load('Data/genres_original/classical/classical.00011.wav',offset=10, duration=10) #load signal after 10s\n",
    "# print(f\"Sample comparison size -> {y_comp.size}\")\n",
    "\n",
    "hop_length = 512\n",
    "frame_length = 1024\n",
    " \n",
    "chroma_ref = librosa.feature.chroma_cqt(y=y, sr=sr, hop_length=hop_length)\n",
    "chroma_comp = librosa.feature.chroma_cqt(y=y_comp, sr=sr, hop_length=hop_length)\n",
    "\n",
    "# Use time-delay embedding to get a cleaner recurrence matrix\n",
    "x_ref = librosa.feature.stack_memory(chroma_ref, n_steps=10, delay=3)\n",
    "x_comp = librosa.feature.stack_memory(chroma_comp, n_steps=10, delay=3)\n",
    "\n",
    "# mfcc_ref = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length)\n",
    "# mfcc_comp = librosa.feature.mfcc(y=y_comp, sr=sr, hop_length=hop_length)\n",
    "\n",
    "\n",
    "#print(f\"chroma reference -> {x_ref}\")\n",
    "#print(f\"chroma comparison reference -> {x_comp}\")\n",
    "\n",
    "xsim = librosa.segment.cross_similarity(x_comp, x_ref, metric='cosine', mode='distance')\n",
    "\n",
    "print(' {}\\n\\n'.format(xsim))\n",
    "\n",
    "cross_sm =  1-pairwise_distances(x_ref, metric=\"cosine\")\n",
    "print(cross_sm)\n",
    "\n",
    "# alltruesarray = np.count_nonzero(xsim.astype(int), axis=1)\n",
    "# alltrues = np.count_nonzero(xsim.astype(int))\n",
    "# allfalse = xsim.size - np.count_nonzero(xsim.astype(int))\n",
    "\n",
    "# print(alltruesarray, stats.mode(alltruesarray))\n",
    "# print(alltrues)\n",
    "# print(allfalse)\n",
    "\n",
    "# xsim_aff = librosa.segment.cross_similarity(x_comp, x_ref, metric='cosine', mode='distance')\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "librosa.display.specshow(xsim, sr=sr, x_axis='frames', y_axis='frames', cmap='magma_r')\n",
    "plt.title('SSM Matirx ')\n",
    "\n",
    "# draw a color bar\n",
    "plt.colorbar(format='%+02.0f dB')\n",
    "\n",
    "# Make the figure layout compact\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "measures -> [138 155 174 192 209 225 242 259 276 292 309 326 344 361]\n",
      "tempo -> 151.99908088235293\n",
      "beats -> [ 138  155  174  192  209  225  242  259  276  292  309  326  344  361\n",
      "  380  399  416  432  448  465  482  498  515  532  550  568  586  604\n",
      "  621  638  654  670  687  704  720  736  754  773  790  806  823  840\n",
      "  857  874  891  908  924  940  956  975  992 1009 1025 1042 1058 1075\n",
      " 1092]\n",
      "Downbeat frames: [ 155  225  292  361  432  498  568  638  704  773  840  908  975 1042]\n",
      "Downbeat times in s: [ 3.59909297  5.2244898   6.78022676  8.38240363 10.03102041 11.56353741\n",
      " 13.18893424 14.81433107 16.34684807 17.94902494 19.5047619  21.08371882\n",
      " 22.63945578 24.19519274]\n",
      "\n",
      "beat times: [ 3.20435374  3.59909297  4.04027211  4.45823129  4.85297052  5.2244898\n",
      "  5.61922902  6.01396825  6.40870748  6.78022676  7.17496599  7.56970522\n",
      "  7.9876644   8.38240363  8.82358277  9.2647619   9.65950113 10.03102041\n",
      " 10.40253968 10.79727891 11.19201814 11.56353741 11.95827664 12.35301587\n",
      " 12.77097506 13.18893424 13.60689342 14.02485261 14.41959184 14.81433107\n",
      " 15.18585034 15.55736961 15.95210884 16.34684807 16.71836735 17.08988662\n",
      " 17.5078458  17.94902494 18.34376417 18.71528345 19.11002268 19.5047619\n",
      " 19.89950113 20.29424036 20.68897959 21.08371882 21.4552381  21.82675737\n",
      " 22.19827664 22.63945578 23.03419501 23.42893424 23.80045351 24.19519274\n",
      " 24.56671202 24.96145125 25.35619048]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "onset_env = librosa.onset.onset_strength(y, sr=sr, aggregate=np.median)\n",
    "# get tempo and beats\n",
    "tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env, sr=sr)\n",
    "# we assume 3/4 time\n",
    "meter = 4\n",
    "# calculate number of full measures \n",
    "measures = (len(beats) // meter)\n",
    "print(f\"measures -> {beats[:measures]}\")\n",
    "print(f\"tempo -> {tempo}\")\n",
    "print(f\"beats -> {beats}\")\n",
    "\n",
    "\n",
    "# get onset strengths for the known beat positions\n",
    "# Note: this is somewhat naive, as the main strength may be *around*\n",
    "#       rather than *on* the detected beat position. \n",
    "beat_strengths = onset_env[beats]\n",
    "# make sure we only consider full measures\n",
    "# and convert to 2d array with indices for measure and beatpos\n",
    "measure_beat_strengths = beat_strengths[:measures * meter].reshape(-1, meter)\n",
    "# add up strengths per beat position\n",
    "beat_pos_strength = np.sum(measure_beat_strengths, axis=0)\n",
    "# find the beat position with max strength\n",
    "downbeat_pos = np.argmax(beat_pos_strength)\n",
    "# convert the beat positions to the same 2d measure format\n",
    "full_measure_beats = beats[:measures * meter].reshape(-1, meter)\n",
    "# and select the beat position we want: downbeat_pos\n",
    "downbeat_frames = full_measure_beats[:, downbeat_pos]\n",
    "print('Downbeat frames: {}'.format(downbeat_frames))\n",
    "# print times\n",
    "downbeat_times = librosa.frames_to_time(downbeat_frames, sr=sr)\n",
    "print('Downbeat times in s: {}\\n'.format(downbeat_times))\n",
    "\n",
    "tempo, beat_times = librosa.beat.beat_track(y, sr=sr, units='time')\n",
    "print('beat times: {}\\n'.format(beat_times))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xsim_aff' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-3a4603a9a389>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m                          hop_length=hop_length, ax=ax[0])\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Binary recurrence (symmetric)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m imgaff = librosa.display.specshow(xsim_aff, x_axis='s', y_axis='s',\n\u001b[0m\u001b[1;32m      6\u001b[0m                          cmap='magma_r', hop_length=hop_length, ax=ax[1])\n\u001b[1;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Affinity recurrence'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'xsim_aff' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(13,5), sharex=True, sharey=True)\n",
    "imgsim = librosa.display.specshow(xsim, x_axis='s', y_axis='s',\n",
    "                         hop_length=hop_length, ax=ax[0])\n",
    "ax[0].set(title='Binary recurrence (symmetric)')\n",
    "imgaff = librosa.display.specshow(xsim_aff, x_axis='s', y_axis='s',\n",
    "                         cmap='magma_r', hop_length=hop_length, ax=ax[1])\n",
    "ax[1].set(title='Affinity recurrence')\n",
    "ax[1].label_outer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the number of onset notes detectable\n",
    "onsets = librosa.onset.onset_detect(y=y, sr=sr, units='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temporal = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length)\n",
    "#chroma_stack = librosa.feature.stack_memory(chroma, n_steps=10, delay=3)\n",
    "R = librosa.segment.recurrence_matrix(onsets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=2, figsize=(10,10), sharex=True, sharey=True)\n",
    "imgsim = librosa.display.specshow(R, x_axis='s', y_axis='s',\n",
    "                         hop_length=hop_length, ax=ax[0])\n",
    "ax[0].set(title='Binary recurrence (symmetric)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)\n",
    "tempogram = librosa.feature.tempogram(onset_envelope=oenv, sr=sr,hop_length=hop_length)\n",
    "# Compute global onset autocorrelation\n",
    "ac_global = librosa.autocorrelate(oenv, max_size=tempogram.shape[0])\n",
    "ac_global = librosa.util.normalize(ac_global)\n",
    "# Estimate the global tempo for display purposes\n",
    "tempo = librosa.beat.tempo(onset_envelope=oenv, sr=sr,hop_length=hop_length)[0]\n",
    "print(tempo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequencies, times, spectrogram = signal.spectrogram(y, sr)\n",
    "ipd.Audio(y, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(nrows=2, figsize=(10, 10))\n",
    "times = librosa.times_like(oenv, sr=sr, hop_length=hop_length)\n",
    "ax[0].plot(times, oenv, label='Onset strength')\n",
    "ax[0].label_outer()\n",
    "ax[0].legend(frameon=True)\n",
    "librosa.display.specshow(tempogram, sr=sr, hop_length=hop_length,\n",
    "                         x_axis='time', y_axis='tempo', cmap='magma',\n",
    "                         ax=ax[1])\n",
    "ax[1].axhline(tempo, color='w', linestyle='--', alpha=1,\n",
    "            label='Estimated tempo={:g}'.format(tempo))\n",
    "ax[1].legend(loc='upper right')\n",
    "ax[1].set(title='Tempogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(14, 5))\n",
    "#plotting the sampled signal\n",
    "librosa.display.waveplot(y, sr=44100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# approach 1 - onset detection and dynamic programming\n",
    "tempo, beat_times = librosa.beat.beat_track(y, sr=sr, units='time')\n",
    "duration = librosa.get_duration(y=y, sr=sr)\n",
    "#tempo, beats = librosa.beat.beat_track(y, sr=sr)\n",
    "beat_times = librosa.frames_to_time(beats, sr=sr)\n",
    "clicks = librosa.clicks(beat_times, sr=sr, length=len(y))\n",
    "#print(beat_times, beat_times.size)\n",
    "#print(tempo, duration)\n",
    "print(f\"Time signature -> {(round(tempo/beat_times.size)) * (math.floor(beat_times.size /duration))}\")\n",
    "\n",
    "#beat_times\n",
    "ipd.Audio(y + clicks, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strip(x, frame_length, hop_length):\n",
    "\n",
    "    # Compute RMSE.\n",
    "    rmse = librosa.feature.rms(beat_times, frame_length=frame_length, hop_length=hop_length, center=True)\n",
    "    print(rmse.shape)\n",
    "    # Identify the first frame index where RMSE exceeds a threshold.\n",
    "    thresh = 0.01\n",
    "    frame_index = 0\n",
    "    while rmse[0][frame_index] < thresh:\n",
    "        frame_index += 1\n",
    "        \n",
    "    # Convert units of frames to samples.\n",
    "    start_sample_index = librosa.frames_to_samples(frame_index, hop_length=hop_length)\n",
    "    \n",
    "    # Return the trimmed signal.\n",
    "    return x[start_sample_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig = strip(beat_times, frame_length, hop_length)\n",
    "print(sig, sig.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_plot(is_draw_plots: bool, yData, title, xAxis=\"Time\", yAxis=\"Amplituda\", xData=0):\n",
    "    if is_draw_plots:\n",
    "        if xData is 0:\n",
    "            plt.plot(yData)\n",
    "        else:\n",
    "            plt.plot(yData, xData)\n",
    "        plt.title(title)\n",
    "        plt.xlabel(xAxis)\n",
    "        plt.ylabel(yAxis)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def __four_forth(tempo: int, sr: int):\n",
    "        fil = np.zeros(int(4 * sr * (60 / tempo)))\n",
    "        nstep = np.floor(60 / tempo * sr)\n",
    "\n",
    "        value = 1\n",
    "        fil[int(1 * nstep)] = 1 * value\n",
    "        fil[int(3 * nstep)] = 1 * value\n",
    "        \n",
    "        draw_plot('false', fil, \"4\\\\4 Meter\", \"Sample/Time\", \"Amplitude\")\n",
    "        print(fil)\n",
    "        return \"4\\\\4\", fil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "__four_forth(tempo, sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pip\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/31/b88ef447d595963c01060998cb329251648acf4a067721b0452c45527eb8/pip-21.2.4-py3-none-any.whl (1.6MB)\n",
      "\u001b[K    100% |████████████████████████████████| 1.6MB 12.1MB/s \n",
      "\u001b[?25hInstalling collected packages: pip\n",
      "  Found existing installation: pip 18.1\n",
      "    Uninstalling pip-18.1:\n",
      "      Successfully uninstalled pip-18.1\n",
      "Successfully installed pip-21.2.4\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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