Extract-Spectral_Flatness.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import librosa.display\n",
    "import IPython.display as ipd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load audio files\n",
    "\n",
    "pluck1, sr = librosa.load('/Users/adamdeedman/Documents/University/Lectures/Third Year/FYP/Python Stage/IDMT-SMT-BASS/PS/FS/BS_1_EQ_1_FS_NO_1_10.wav')\n",
    "pick1, sr = librosa.load('/Users/adamdeedman/Documents/University/Lectures/Third Year/FYP/Python Stage/IDMT-SMT-BASS/PS/PK/BS_1_EQ_1_PK_NO_1_10.wav')\n",
    "slap1, sr = librosa.load('/Users/adamdeedman/Documents/University/Lectures/Third Year/FYP/Python Stage/IDMT-SMT-BASS/PS/ST/BS_1_EQ_1_ST_NO_1_10.wav')\n",
    "\n",
    "# finds length of each signal array\n",
    "maxlen = max((len(pluck1)), (len(pick1)), (len(slap1)))\n",
    "\n",
    "# pads all signals to max length\n",
    "pluck1_pad = np.pad(pluck1, (0, maxlen - len(pluck1)), 'constant', constant_values=0)\n",
    "pick1_pad = np.pad(pick1, (0, maxlen - len(pick1)), 'constant', constant_values=0)\n",
    "slap1_pad = np.pad(slap1, (0, maxlen - len(slap1)), 'constant', constant_values=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# frame-length and hop-length\n",
    "\n",
    "FL = 1024 # (frame-length)\n",
    "HL = 512 # (hop-length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract spectral flatness\n",
    "\n",
    "flatness_pluck1 = librosa.feature.spectral_flatness(y=pluck1_pad, hop_length=HL, n_fft=FL)[0]\n",
    "flatness_pick1 = librosa.feature.spectral_flatness(y=pick1_pad, hop_length=HL, n_fft=FL)[0]\n",
    "flatness_slap1 = librosa.feature.spectral_flatness(y=slap1_pad, hop_length=HL, n_fft=FL)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate time of samples\n",
    "\n",
    "frames = range (0, flatness_pluck1.size)\n",
    "\n",
    "t = librosa.frames_to_time(frames, hop_length=HL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(t, flatness_pluck1, color=\"b\")\n",
    "plt.plot(t, flatness_pick1, color=\"r\")\n",
    "plt.plot(t, flatness_slap1, color=\"g\")\n",
    "plt.title(\"Spectral flatness of pluck, pick and slap bass-playing styles\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spectral flatness essentially resembles how much a sound resembles a pure tone."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}