Caracteristiques acoustiques.ipynb

{
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  "nbformat_minor": 0,
  "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": "3.8.1"
    },
    "colab": {
      "name": "Caracteristiques acoustiques.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gabilodeau/INF8770/blob/master/Caracteristiques%20acoustiques.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xRPmSeX9fCDE"
      },
      "source": [
        "INF8770 Technologies multimédias\n",
        "\n",
        "Polytechnique Montréal\n",
        "\n",
        "Exemple de calculs de caractéristiques acoustiques"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnI9xDKnfEz7"
      },
      "source": [
        "Exécuter les 2 lignes suivantes pour colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gb1VkzerfFXG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6bad886-420b-4033-dcf5-2833c23c7cdd"
      },
      "source": [
        "!git clone https://github.com/gabilodeau/INF8770\n",
        "%cd INF8770"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'INF8770' already exists and is not an empty directory.\n",
            "/content/INF8770\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OET-NTpAfCDG"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.io import wavfile\n",
        "from scipy.signal import find_peaks_cwt\n",
        "from math import gcd\n",
        "from detect_peaks import detect_peaks  #ne fait pas partie d'une librairie standard"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-dxoVeafCDR"
      },
      "source": [
        "Lecture d'un fichier audio et affichage "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g11wn5TpfCDU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "33431d22-f989-4cb6-fe4d-e4b84c00e481"
      },
      "source": [
        "[fs, signal] = wavfile.read('000.wav')\n",
        "fig1 = plt.figure(figsize = (10,5))\n",
        "plt.plot(range(len(signal)), signal)\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B56pi9ZcfCDc"
      },
      "source": [
        "Première caractéristique: Intensité"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KviCaOCRfCDe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1628d7e-5792-4910-c7f6-627b36552703"
      },
      "source": [
        "Intensite = np.sqrt(np.sum(signal)**2);\n",
        "print(\"Intensité = \",Intensite)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Intensité =  10061.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCwcNoWDfCDk"
      },
      "source": [
        "Transformée de Fourier discrète du signal pour calculer les caractéristiques dans le domaine fréquentiel."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TeDL27a0fCDl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "12569928-9004-410a-b8f9-095de45c34fc"
      },
      "source": [
        "signalfreq = np.fft.fft(signal)\n",
        "fig2 = plt.figure(figsize = (10,5))\n",
        "plt.plot(range(len(signalfreq)), signalfreq.real)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JVFI9fWfCDq"
      },
      "source": [
        "Détection des fréquences les plus importantes du signal. Noter que pour la détection de pics, celle-ci dépend beaucoup des paramètres. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AOAtDblfCDr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "d6cf40ea-5464-4719-b1fb-2546801b84d9"
      },
      "source": [
        "peaks = detect_peaks(signalfreq[range(0,int(len(signalfreq)/2))].real, mph=900000, mpd=30, show=True)\n",
        "print(\"Pics :\", peaks)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Pics : [ 441  617  659  791  822  884 1031 1098 1235]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bu2k1B4QfCDv"
      },
      "source": [
        "Deuxième caractéristique: Hauteur tonale, le pic avec la plus grande amplitude."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enV18p3bfCDw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07ca1ba1-0e0c-4e66-fc3d-c7a04e4ca125"
      },
      "source": [
        "hauteurtonale = 0\n",
        "max = 0\n",
        "for i in range(1,len(peaks)):\n",
        "    if signalfreq[peaks[i]]>max : \n",
        "      hauteurtonale = peaks[i]\n",
        "      max = signalfreq[peaks[i]]\n",
        "    \n",
        "print('Hauteur tonale= ', hauteurtonale)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hauteur tonale=  822\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4wmHoLnkfCDz"
      },
      "source": [
        "Troisième caractéristique: Brillance, la moyenne des pics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABMMufQAfCD0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1420397f-6e75-4cf7-ae33-6630ae55a87a"
      },
      "source": [
        "brillance = np.mean(peaks)\n",
        "print('Brillance= ', brillance)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Brillance=  842.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1n64_sufCD3"
      },
      "source": [
        "Quatrième caractéristique: Largeur de bande, la moyenne des pics auxquels on soustrait la brillance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2c_j3OUjfCD3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "307dbe41-3c5d-4225-b9b5-23d4d0b5e1fc"
      },
      "source": [
        "largeurdebande = np.mean(abs(peaks - brillance))\n",
        "print('Largeur de bande= ', largeurdebande)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Largeur de bande=  195.55555555555554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oe6CRMICfCD6"
      },
      "source": [
        ""
      ],
      "execution_count": 9,
      "outputs": []
    }
  ]
}