Mandarin_Tone_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Mandarin Tone Classification.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alicex2020/Mandarin-Tone-Classification/blob/master/Mandarin_Tone_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "1jfpf7uSl8eM",
        "colab_type": "code",
        "outputId": "9ef20977-c186-48af-8ffe-c8ab272b9a3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "cell_type": "code",
      "source": [
        "# import the most useful packages\n",
        "import numpy as np \n",
        "import matplotlib\n",
        "import math\n",
        "import os\n",
        "from matplotlib import pyplot as plt\n",
        "import IPython.display as ipd\n",
        "print('finished importing')\n",
        "! pip install librosa\n",
        "import librosa\n",
        "import librosa.display\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "finished importing\n",
            "Requirement already satisfied: librosa in /usr/local/lib/python3.6/dist-packages (0.6.2)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.6)\n",
            "Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.14.6)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.1.0)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.20.2)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.13.0)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.3.0)\n",
            "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.11.0)\n",
            "Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.1)\n",
            "Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.40.1)\n",
            "Requirement already satisfied: llvmlite>=0.25.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.27.0)\n",
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qwmHot5nmUnx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# make a folder for this course and this project. \n",
        "# the -p flag says make any needed parent folders and  don't complain about existing ones.\n",
        "my_dir = '/content/drive/My Drive/IW06/AliceProject'\n",
        "! mkdir -p '$my_dir'\n",
        "# this command is equivalent to \"cd\" that sets a working directory\n",
        "os.chdir(my_dir)\n",
        "!ls '/content/drive/My Drive/IW06/AliceProject/Data'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3ePpyL5FcTyi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# **Different Sound Representations for Sample Files**"
      ]
    },
    {
      "metadata": {
        "id": "XmmOGUvHmUxk",
        "colab_type": "code",
        "outputId": "e016d33e-1de0-4660-c805-62753eca312c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "cell_type": "code",
      "source": [
        "# Get audio\n",
        "file1 = '/content/drive/My Drive/IW06/AliceProject/Data/ma1_FV1_MP3.mp3'\n",
        "audio1, sample_rate1 = librosa.core.load(file1)\n",
        "file2 = '/content/drive/My Drive/IW06/AliceProject/Data/ma2_FV1_MP3.mp3'\n",
        "audio2, sample_rate2 = librosa.core.load(file2)\n",
        "file3 = '/content/drive/My Drive/IW06/AliceProject/Data/ma3_FV1_MP3.mp3'\n",
        "audio3, sample_rate3 = librosa.core.load(file3)\n",
        "file4 = '/content/drive/My Drive/IW06/AliceProject/Data/ma4_FV1_MP3.mp3'\n",
        "audio4, sample_rate4 = librosa.core.load(file4)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "22050 (16728,) 0.758639455782313\n",
            "16728\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "fYq1CjjSbIhN",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# spectrograms\n",
        "X = librosa.stft(audio1)\n",
        "Xdb = librosa.amplitude_to_db(abs(X))\n",
        "plt.figure(figsize=(7, 7))\n",
        "librosa.display.specshow(Xdb)\n",
        "\n",
        "X2 = librosa.stft(audio2)\n",
        "Xdb2 = librosa.amplitude_to_db(abs(X2))\n",
        "plt.figure(figsize=(7,7))\n",
        "librosa.display.specshow(Xdb2)\n",
        "\n",
        "X3 = librosa.stft(audio3)\n",
        "Xdb3 = librosa.amplitude_to_db(abs(X3))\n",
        "plt.figure(figsize=(7,7))\n",
        "librosa.display.specshow(Xdb3)\n",
        "\n",
        "X4 = librosa.stft(audio4)\n",
        "Xdb4 = librosa.amplitude_to_db(abs(X4))\n",
        "plt.figure(figsize=(7,7))\n",
        "print(Xdb4.shape)\n",
        "# librosa.display.specshow(Xdb4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g9ZUtcdf4r7K",
        "colab_type": "code",
        "outputId": "26baaff6-b05d-4c50-8781-d152805325c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1413
        }
      },
      "cell_type": "code",
      "source": [
        "# mel-spectrograms\n",
        "import matplotlib.cm as cm\n",
        "\n",
        "mel1 = librosa.feature.melspectrogram(audio1, sr=sample_rate1, n_fft=1024, hop_length=512, n_mels=80, fmin=75, fmax=3700)\n",
        "plt.imshow(np.log10(mel1 + 1e-10), aspect='auto', cmap=cm.plasma)\n",
        "plt.show()\n",
        "print(mel1.shape)\n",
        "\n",
        "mel2 = librosa.feature.melspectrogram(audio2, sr=sample_rate2, n_fft=1024, hop_length=512, n_mels=80, fmin=75, fmax=3700)\n",
        "plt.imshow(np.log10(mel2 + 1e-10), aspect='auto', cmap=cm.plasma)\n",
        "plt.show()\n",
        "print(mel2.shape)\n",
        "\n",
        "mel3 = librosa.feature.melspectrogram(audio3, sr=sample_rate3, n_fft=1024, hop_length=512, n_mels=80, fmin=75, fmax=3700)\n",
        "plt.imshow(np.log10(mel3 + 1e-10), aspect='auto', cmap=cm.plasma)\n",
        "plt.show()\n",
        "print(mel3.shape)\n",
        "\n",
        "mel4 = librosa.feature.melspectrogram(\n",
        "    audio4, sr=sample_rate4, n_fft=1024, hop_length=512, n_mels=80, fmin=75, fmax=3700)\n",
        "plt.imshow(np.log10(mel4 + 1e-10), aspect='auto', cmap=cm.plasma)\n",
        "plt.show()\n",
        "print(mel4.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RpBiMC/KZA0BQKC8Mj9i7MctKZfnTjxzX5xjW2coiQtkkaO9Ol7q4JffluENIpO6+nVZr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\nEEJchpsvIYQQ4jLcfAkhhBCX+f8BkXYF8i1KFUwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd8606d37b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(80, 33)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L6rvL8j4xlcdOup/vWpX7bjm1f9HcjX5KsosMd7jMbjWKtDRftxQZurKVCPdb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\nYtgXfvzo9jSVPZkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd8602d9b70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(80, 39)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ahg0bZmUyQgghxJuBGX/q7d69G5///Odx5MgRxGIxbN++HV/4whdw6623YuvWrejp6cEV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\nQgghRMho8RVCCCFCRouvEEIIETL/P5DJxnOl+Dk6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd8603262b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(80, 39)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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R45vv3r17JRaLSUtLiyxZskTK5bKEQiHJ5XLi9/slkUhIPK4P2x38/JNK2/L/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\nlUpl93qxMHAgiIWdK+C94PNjc8w0nlNRPtTvw8b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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd86058d7f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(80, 32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WkIPZ10DRyon",
        "colab_type": "code",
        "outputId": "4e5e92e4-517e-4b00-ad7f-6cacad8a4361",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1358
        }
      },
      "cell_type": "code",
      "source": [
        "# MFCC\n",
        "\n",
        "mfcc = librosa.feature.mfcc(y=audio1, sr=sample_rate1, n_mfcc=60)\n",
        "plt.imshow(mfcc, aspect='auto', cmap=cm.viridis)\n",
        "plt.show()\n",
        "\n",
        "mfcc = librosa.feature.mfcc(y=audio2, sr=sample_rate2, n_mfcc=60)\n",
        "plt.imshow(mfcc, aspect='auto', cmap=cm.viridis)\n",
        "plt.show()\n",
        "\n",
        "mfcc = librosa.feature.mfcc(y=audio3, sr=sample_rate3, n_mfcc=60)\n",
        "plt.imshow(mfcc, aspect='auto', cmap=cm.viridis)\n",
        "plt.show()\n",
        "\n",
        "mfcc = librosa.feature.mfcc(y=audio4, sr=sample_rate4, n_mfcc=60)\n",
        "plt.imshow(mfcc, aspect='auto', cmap=cm.viridis)\n",
        "plt.show()\n",
        "print(mfcc.shape)\n",
        "\n",
        "def mp3tomfcc(file_path, max_pad):\n",
        "  audio, sample_rate = librosa.core.load(file_path)\n",
        "  mfcc = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=60)\n",
        "  pad_width = max_pad - mfcc.shape[1]\n",
        "  mfcc = np.pad(mfcc, pad_width=((0, 0), (0, pad_width)), mode='constant')\n",
        "  return mfcc\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fUT0+zobKXUlUTIp6r6hCLkeqfL75jmltdS+xqLyf1SVd43DlCwBAYEy+AAAExuQLAEBg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\nZqYF8tQ+9B3nGqtqqO5sGFN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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd8605fa0f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LFSRSQjpqFS9l6Mk0Ky6pFdnKQlOq1fjQqYt2ncXPGy01NwSmGmxM6ujw5AsAQGB0vgAA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\nAIDA6HwBAAiMzhcAgMD+L+RhVn0BhvEbAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd8603e5da0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ERucLAEBgdL4AAAQWJHDlG94pOk+tQqWoV8Uq17xoNajjXXet0gzS+KrQJKEMoaeEHrIM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\nAAKj8wUAIDA6XwAAAvu/xBIKdQZCFKkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd860345ac8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CSbGKaKEWjmqLv3xW3O9qmCmltnRqhbbk59KOMTXsmBerxMW2Sf9Q8rGinkNq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\nAGdMvgAAOPv/Ndq/tySNtMkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd8604c0390>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(60, 32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rYsR7T7vU-yv",
        "colab_type": "code",
        "outputId": "117eee03-e020-4b1d-ef22-305570d105c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1353
        }
      },
      "cell_type": "code",
      "source": [
        "# Pitch\n",
        "\n",
        "pitch, mag = librosa.core.piptrack(audio1, sr=sample_rate1, n_fft=512)\n",
        "plt.imshow(pitch, aspect='auto')\n",
        "plt.ylim([20,100])\n",
        "plt.show()\n",
        "\n",
        "pitch, mag = librosa.core.piptrack(audio2[0:sample_rate2*2], sr=sample_rate2, n_fft=512)\n",
        "plt.imshow(pitch, aspect='auto')\n",
        "plt.ylim([20,100])\n",
        "plt.show()\n",
        "\n",
        "pitch, mag = librosa.core.piptrack(audio3[0:sample_rate3*2], sr=sample_rate3, n_fft=512)\n",
        "plt.imshow(pitch, aspect='auto')\n",
        "plt.ylim([20,100])\n",
        "plt.show()\n",
        "\n",
        "pitch, mag = librosa.core.piptrack(audio4[0:sample_rate4*2], sr=sample_rate4, n_fft=512)\n",
        "plt.imshow(pitch, aspect='auto')\n",
        "plt.ylim([20,100])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd86eb4f518>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0l7WnfoY0daabrAj89FWkTy26Zs0a9e/fX2eeeWbA30dKPyXp4MGDWrx4sXbv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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd8604085f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zf/3rX6MZoqfmzp1rDRs2zFq9enVM5vnVV19ZN910k/X1119bRUVF1rRp02Iy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\njAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd85e1fb748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd85e1b7438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Gk_F6KPdcnfl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Deep Learning on Full Tone Perfect Dataset"
      ]
    },
    {
      "metadata": {
        "id": "Z_Y5KBe7ZCsC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Compile MFCCs and extract labels: https://github.com/adhishthite/sound-mnist/blob/master/utils/wav2mfcc.py\n",
        "\n",
        "file_path1 = '/content/drive/My Drive/IW06/AliceProject/Complete_Data/FV1'\n",
        "file_path2 = '/content/drive/My Drive/IW06/AliceProject/Complete_Data/FV2'\n",
        "file_path3 = '/content/drive/My Drive/IW06/AliceProject/Complete_Data/FV3'\n",
        "file_path4 = '/content/drive/My Drive/IW06/AliceProject/Complete_Data/MV1'\n",
        "file_path5 = '/content/drive/My Drive/IW06/AliceProject/Complete_Data/MV2'\n",
        "file_path6 = '/content/drive/My Drive/IW06/AliceProject/Complete_Data/MV3'\n",
        "\n",
        "\n",
        "for f in os.listdir(file_path1):\n",
        "  if f.endswith('.mp3'):\n",
        "    mfccs.append(mp3tomfcc(file_path1 + '/' + f, 60)) \n",
        "\n",
        "    \n",
        "for f in os.listdir(file_path2):\n",
        "  if f.endswith('.mp3'):\n",
        "    mfccs.append(mp3tomfcc(file_path2 + '/' + f, 60))\n",
        "\n",
        "for f in os.listdir(file_path3):\n",
        "  if f.endswith('.mp3'):\n",
        "    mfccs.append(mp3tomfcc(file_path3 + '/' + f, 60))\n",
        "    \n",
        "for f in os.listdir(file_path4):\n",
        "  if f.endswith('.mp3'):\n",
        "    mfccs.append(mp3tomfcc(file_path4 + '/' + f, 60)) \n",
        "\n",
        "    \n",
        "for f in os.listdir(file_path5):\n",
        "  if f.endswith('.mp3'):\n",
        "    mfccs.append(mp3tomfcc(file_path5 + '/' + f, 60))\n",
        "\n",
        "for f in os.listdir(file_path6):\n",
        "  if f.endswith('.mp3'):\n",
        "    mfccs.append(mp3tomfcc(file_path6 + '/' + f, 60))\n",
        "\n",
        "\n",
        "\n",
        "mfccs = np.asarray(mfccs)\n",
        "print(mfccs.shape)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dkKD9-G83TSt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Save all MFCCs so that don't have to recreate MFCC array every run\n",
        "np.save('mfccs_all_male_female.npy', mfccs)\n",
        "mfccs_loaded = np.load('mfccs_all_male_female.npy')\n",
        "print(mfccs_loaded[0])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HeXM96SX5AkN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Gather all labels for male and female speakers (1-4 for each of the four tones)\n",
        "labels = []\n",
        "\n",
        "for f in os.listdir(file_path1):\n",
        "  if f.endswith('.mp3'):    \n",
        "    label = f.split('_')[0][-1] # label is the last character before the first '_'\n",
        "    labels.append(label)\n",
        "\n",
        "for f in os.listdir(file_path2):\n",
        "  if f.endswith('.mp3'):    \n",
        "    label = f.split('_')[0][-1] # label is the last character before the first '_'\n",
        "    labels.append(label)\n",
        "\n",
        "for f in os.listdir(file_path3):\n",
        "  if f.endswith('.mp3'):    \n",
        "    label = f.split('_')[0][-1] # label is the last character before the first '_'\n",
        "    labels.append(label)\n",
        "\n",
        "for f in os.listdir(file_path4):\n",
        "  if f.endswith('.mp3'):    \n",
        "    label = f.split('_')[0][-1] # label is the last character before the first '_'\n",
        "    labels.append(label)\n",
        "\n",
        "for f in os.listdir(file_path5):\n",
        "  if f.endswith('.mp3'):    \n",
        "    label = f.split('_')[0][-1] # label is the last character before the first '_'\n",
        "    labels.append(label)\n",
        "\n",
        "for f in os.listdir(file_path6):\n",
        "  if f.endswith('.mp3'):    \n",
        "    label = f.split('_')[0][-1] # label is the last character before the first '_'\n",
        "    labels.append(label)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "i2wwhl2_5dWu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "labels = to_categorical(labels, num_classes=None)\n",
        "print(labels.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "m2uHzOdd5YT3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# load labels into drive to avoid having to recreate labels every run\n",
        "np.save('mfccs_all_male_labels.npy', labels)\n",
        "labels_loaded = np.load('mfccs_all_male_labels.npy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EplVGj-mnwns",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## Classifier  ##\n",
        "\n",
        "import tensorflow as tf\n",
        "import IPython.display as ipd\n",
        "import keras\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "from keras.utils import to_categorical\n",
        "import numpy as np \n",
        "import matplotlib\n",
        "import math\n",
        "import os\n",
        "from matplotlib import pyplot as plt\n",
        "import IPython.display as ipd\n",
        "print('finished importing')\n",
        "! pip install librosa\n",
        "import librosa\n",
        "import librosa.display\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "my_dir = '/content/drive/My Drive/IW06/AliceProject'\n",
        "! mkdir -p '$my_dir'\n",
        "os.chdir(my_dir)\n",
        "\n",
        "\n",
        "def get_cnn_model(input_shape, num_classes):\n",
        "    model = Sequential()\n",
        "    model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', input_shape=input_shape))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Conv2D(48, kernel_size=(2, 2), activation='relu'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Conv2D(120, kernel_size=(2, 2), activation='relu'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(128, activation='relu'))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Dense(64, activation='relu'))\n",
        "    model.add(Dropout(0.4))\n",
        "    model.add(Dense(num_classes, activation='softmax'))\n",
        "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
        "                  optimizer=keras.optimizers.Adadelta(),\n",
        "                  metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "mfccs = np.load('mfccs_all_male_female.npy').tolist()\n",
        "mfccs = np.asarray(mfccs)\n",
        "labels = np.load('mfccs_all_male_labels.npy')\n",
        "\n",
        "dim_1 = mfccs.shape[1]\n",
        "dim_2 = mfccs.shape[2]\n",
        "channels = 1\n",
        "classes = 5\n",
        "\n",
        "X = mfccs\n",
        "print(X.shape)\n",
        "X = X.reshape((mfccs.shape[0], dim_1, dim_2, channels))\n",
        "print(X.shape)\n",
        "y = labels\n",
        "input_shape = (dim_1, dim_2, channels)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Zq9NcVMIeWeJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training and Testing \n",
        "*Note: Results shown are from training/testing only male data, but code also works for the complete dataset*"
      ]
    },
    {
      "metadata": {
        "id": "B6OWQYCH7vGb",
        "colab_type": "code",
        "outputId": "e6b19668-6e76-4b32-b1cf-85bdbbf2baec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
        "model = get_cnn_model(input_shape, classes)\n",
        "\n",
        "from sklearn.utils import class_weight\n",
        "y_ints = [y.argmax() for y in y_train]\n",
        "class_weights = class_weight.compute_class_weight('balanced', np.unique(y_ints), y_ints)\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=20, epochs=15, verbose=1, validation_split=0.2, class_weight=class_weights)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2755 samples, validate on 689 samples\n",
            "Epoch 1/15\n",
            "2755/2755 [==============================] - 201s 73ms/step - loss: 5.1846 - acc: 0.6309 - val_loss: 0.6340 - val_acc: 0.9550\n",
            "Epoch 2/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 1.0883 - acc: 0.9103 - val_loss: 0.2074 - val_acc: 0.9855\n",
            "Epoch 3/15\n",
            "2755/2755 [==============================] - 201s 73ms/step - loss: 0.6172 - acc: 0.9459 - val_loss: 0.2284 - val_acc: 0.9840\n",
            "Epoch 4/15\n",
            "2755/2755 [==============================] - 201s 73ms/step - loss: 0.4860 - acc: 0.9546 - val_loss: 0.1929 - val_acc: 0.9869\n",
            "Epoch 5/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 0.3982 - acc: 0.9677 - val_loss: 0.1506 - val_acc: 0.9869\n",
            "Epoch 6/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 0.3423 - acc: 0.9721 - val_loss: 0.2133 - val_acc: 0.9855\n",
            "Epoch 7/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 0.4135 - acc: 0.9662 - val_loss: 0.1339 - val_acc: 0.9898\n",
            "Epoch 8/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 0.2756 - acc: 0.9746 - val_loss: 0.1577 - val_acc: 0.9884\n",
            "Epoch 9/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 0.1833 - acc: 0.9840 - val_loss: 0.1171 - val_acc: 0.9927\n",
            "Epoch 10/15\n",
            "2755/2755 [==============================] - 200s 73ms/step - loss: 0.1836 - acc: 0.9837 - val_loss: 0.1232 - val_acc: 0.9913\n",
            "Epoch 11/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 0.1646 - acc: 0.9858 - val_loss: 0.1295 - val_acc: 0.9884\n",
            "Epoch 12/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 0.1606 - acc: 0.9844 - val_loss: 0.1280 - val_acc: 0.9913\n",
            "Epoch 13/15\n",
            "2755/2755 [==============================] - 200s 72ms/step - loss: 0.1653 - acc: 0.9862 - val_loss: 0.1412 - val_acc: 0.9913\n",
            "Epoch 14/15\n",
            "2755/2755 [==============================] - 199s 72ms/step - loss: 0.1542 - acc: 0.9869 - val_loss: 0.1871 - val_acc: 0.9884\n",
            "Epoch 15/15\n",
            "2755/2755 [==============================] - 200s 73ms/step - loss: 0.1079 - acc: 0.9917 - val_loss: 0.0962 - val_acc: 0.9927\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7KOHT-d-eiX4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Evaluation of Model Performance"
      ]
    },
    {
      "metadata": {
        "id": "jtROhFC5gXDG",
        "colab_type": "code",
        "outputId": "58dc9625-045a-4668-9b7b-efec5c129361",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# evaluate model\n",
        "model.evaluate(X_test, y_test, batch_size = 3, verbose = 1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1476/1476 [==============================] - 27s 18ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.18211926559780078, 0.9884823855103516]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "MSMlIgj8lHtB",
        "colab_type": "code",
        "outputId": "d2288f78-fe05-4510-b04a-2b93e4fcfb88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# evaluate model on training\n",
        "model.evaluate(X_train, y_train, batch_size = 3, verbose = 1)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3444/3444 [==============================] - 63s 18ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08363087464991183, 0.994192799416984]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "eSEDj_Ck3pPA",
        "colab_type": "code",
        "outputId": "e7fef6e1-da9f-4d0d-d27b-2a826c9fa5c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Evaluation metrics from scikit learn\n",
        "\n",
        "import sklearn.metrics as metrics\n",
        "\n",
        "y_pred = model.predict(X_test).ravel()\n",
        "y_pred_ohe = model.predict(X_test)\n",
        "y_pred_labels = np.argmax(y_pred_ohe, axis=1)  # only necessary if output has one-hot-encoding, shape=(n_samples)\n",
        "\n",
        "y_true_labels = np.argmax(y_test, axis=1)\n",
        "\n",
        "confusion_matrix = metrics.confusion_matrix(y_true=y_true_labels, y_pred=y_pred_labels)\n",
        "print(confusion_matrix)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[377   2   0   0]\n",
            " [  7 336   4   0]\n",
            " [  0   2 367   0]\n",
            " [  0   0   2 379]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "V86-Dvtg8eFb",
        "colab_type": "code",
        "outputId": "3b29fa6b-a8b4-4be6-b71c-79452dd4a1b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        }
      },
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_test).ravel()\n",
        "\n",
        "y_true_labels = np.argmax(y_test, axis=1)\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "cm = confusion_matrix(y_true_labels, y_pred_labels)\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cm, classes=[1, 2, 3, 4],\n",
        "                      title='Confusion Matrix')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[377   2   0   0]\n",
            " [  7 336   4   0]\n",
            " [  0   2 367   0]\n",
            " [  0   0   2 379]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ro7Pgu3r1KkJCQpCamgozMzNER0dj/fr1sLGx0dUuiYhIDxh8FXB2dkZYWJiuhiciIrFI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\nVHq+mwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f11dcf15f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "I1lqwLamhyEs",
        "colab_type": "code",
        "outputId": "a70b660c-f87b-450e-c3c7-b2df000ce5fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        }
      },
      "cell_type": "code",
      "source": [
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BH/QhcNgw4YnSyNXI0mRhmD6/I6w76zN09razNFlQJ8jwqlwmR5pMjjRF74tkRSoQCqDd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\nA7hNdGqAVbjxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f11bf409710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tglc++AZ/e+1rxBNJtYtFRETdgOGsYb0K7bhnxvdxWq98fLTlMB5f9m+EG7lo\nBhGR3jGcNS7fbsId00ZiZP8ibNsXwNwln8PHRTOIiHSN4dwDmI0S/vMnwzD++71x0NeABxd/hm8O\n1aldLCIiUoguw9kXqcV23261i5FVoijgZ+P642fj+qO+IYp5Sz/Hv3f61C4WEREpQLElI9X0yu7X\n8Xn1JlzS5yL8+PRJkERJ7SJlzfjv90ZRvgV/XbUVC1Zugre+CYNOcaLcY4coCGoXj4iIskCX4XzZ\naZficOQI3t73HqrqDuD6odfCYcpTu1hZM3KAB3deeyaeWLEJ/1y7HQBgtxjQ/xQXBvROffQpyYNB\n0mXHCBGR7gmyRgbQer31WT2e3WXAH99fhC+9W+AyO/GLoZU4zdknq+dQW104ij2HQ/jsq8PYXhWA\nL9iYec9slNCvPB/9e7swsLcLp/XKh8nYc3sQPB5H1n9GtIj11BfWU1+yXU+Px9Hue7psOQOAzWjF\nDUMr8ebe9Vi1ZzXmf/4XXD3gxxhddg4EnXT/5ttMGH9OBUacXgAAqK1rxI79AeyoCmJHVQBbv/Vj\n67d+AIBBEnBar3wMSId133InrGbdfvuJiHo0Xf91FgQBl556MXrnl+PvW5fin9tX4tu6KkwZcAWM\nklHt4mVdQb4F555RinPPKAWQalnvTAf1jqoAdh0IYuf+IF7bsBeCAFSUODLd4P1PccJhM6lcAyIi\nAnQezs0GFwzAnd+/Bc9ueQ4bDv0fDoQO4hdDZ6DQ6la7aIrKt5lw1kAPzhroAQCEG+PpgA5ge1UA\n3xysw7eH67H2/6oAAOVF9kxYD+jtgtthVrP4REQ5KyfCGQAKrW7cduaNeGHHS/j40EbM2/gErh9y\nLQYV9Fe7aN3GZjFgeN9CDO9bCACIxhLYc7AOO6pSYb37QBAHfA1Y98UBAECxy5pqVfd2YmBvFzwu\nq24uCRARaVnOhDMAGCUjpg+6Gqfm98GLO17Bk/9+Fj86fSLGV4zJydAxGSUMqnBjUEWqByGeSGLv\n4fpMN/iO/UF8sPkQPkivjOXKM2FAbxf6ljlRVmRHWZEdrjxTTn7tiIiUlFPhDKSuQ19Yfi5OySvD\ns1sW45U9b+Db+ipUDr4GVoNF7eKpyiCJ6FvuRN9yJyadW4FkUsZ+b+hoWFcF8OnX1fj06+rM51hM\nEnoV2lFWZENZoR29iuwoK7ShyGmFKDK0iYg6I+fCudlpzj648+ybsWjL8/jSuwWHG45g5rAZKLWX\nqF00zRBFAX1KHOhT4sC47/eGLMs4XBvG3iP1OOQL41BNAw7WhLHvSP0x04kaDSJKC2zoVWhLtbIL\n7ehVaENJgY3jr4mITiBnwxl10XXoAAAW+ElEQVQA8k0O/HrEDXhl9xt4u+o9PLxxASoHT8HI4mFq\nF02TBEFAr0I7ehXaW22PJ5LwBiI4mAnshlR41zagqjrUal9REFDstqKsKBXWZYWp7vHSAhvMpp47\nDpuIKJtyOpwBQBIlXNn/MlTk98aSbS/i2S2LMa7PD/Cj0yfqatpPJRkksUVoezLbk7KM2mAjDtaE\ncdDXgEM1DTiUfn64NnzMcYqclvRxWrS2i2ywW/Q37A1I3ZDnCzbCF4zAG2iENxCBM9+C3kU29C93\n8Z8VohyW8+Hc7KyS76GXvQTPbH4Ob+17F/vqD+D6IdN0Ne1ndxMFAUUuK4pc1swd4gAgyzLqGqKt\nQjv1GMbmPTXYvKem1XGcdhPKi/OQZzHAlWdOfThMcOeZ4XKkXps1OPtZUpYRqG+CL5gK3tRHI7zB\nCHyBCAKhaLufK4kCTivLx6A+bgzuk5o0pifP8EZEJ0e303d2dpq1SDyC575ajk2+rXCbXbhhWCUq\n8ntntWzZpLdp8xoaYzjkC+Ngi8A+6GuAv74RyeP8pNrMBrgcZrjzTOnwNmeC3O0ww5VngjPPBEnM\n7vXucGM83fI9GrzeQAS+QKpFHE8cW2hREFCQb4bHZUWR05J6dFngcVphMBvxyeaD2LbXj71H6tH8\n22mQRPQty8egCjcGV7hxWq98GA0999q93n5u28N66kt3Tt/JcG5DUk5i7d71eHXPGkiCiGsGXIHR\n5edktXzZkiu/FAUFduzeW4tAqAn++iYEQk0tnkcRSG9raIy3ewwBQL7d1Cqwm0PcnQlzE/Ksxszw\nsHgiidq6xky3s7dFF7QvEGn3fHlWIzyudPA6rfC4LChyWeFxWVHgMLd7U1zL72e4MY4dVQFs2+fH\ntr1+VFWH0PzLajKI6HeKE4P6pIbCnVrq6FE32uXKzy3rqS+cW1tloiBi4qlj0cdRjn9s/SeWbv8X\nvq2rwjUDfqzLaT97AkkS4XakQvS0Xu3vF40l0sEdbTfED9U0YO+R9n/BDJIAV54ZsgzU1jeirX9f\njQYRRU4L+pY74XGmW74tWsLZmLfcZjFgRP8ijOhfBAAIRWLYvi8d1vv8+Orb1AeQWuikf28nBqfD\nuk9JXtZ7CYio+zCcj+OMwoG48+yb8czm5/DRoU+xP3QQNwyrRIFF39N+9mQmo4Ritw3Fblu7+8iy\njEhTPBPYbYZ4qAkA0L/cme52trZqCTvzTN2+fnae1dhqOta6hii2VwWwbW8qrLfsqcWWPbUAAKtZ\nwoBTXKlJZvq40bskj+t9E/Ug7NbugGgihmXbX8LHhzciz2jHz4dM08y0n+xO0peu1DMQakp3gada\n19X+SOY9u8WAAb1TYT24jxtlHruqYc3vp76wnp0/XnvYcu4Ak2TE9MFX41Rnb7y4YxWe/Pez+HHf\nSRjX5wecupI0w5VnbrUqWW1dY6uw/mKnD1/s9AFItcIH9UmFdb9yJ3oV2nv0DWZEesNw7qDUtJ/n\noTyvDM9uXoyXd7+Ob+uqUDn4alhyfNpP0qaCfAvOH9oL5w9NXaT3BiKZLvBt+wLYuN2Ljdu9AI5O\nDlPusaO8yI5yTx7KiuwocVt71I1mRHrBcD5JpzsrMHvULfjbliX4t3czDjccwQ3DZqDUXqx20YiO\ny5O+W/zC75VBlmVU+yP4ep8f+w7XY7+vAQe8qclhPksHNpAab908KUy5Jy8d3HZ4OHc6kaIYzp2Q\nb3Lg5hEz8fLu1/FO1ft4ZOMCVA6+BiM6OO1nUk4imoghlowhmogi2vyYiCGajCGW3hZLv261T/P2\n9HOTyQBD0gib0QqrwQpb84cx9Wht8dwsmdkNTwBSPUElBam5zpvJsoxAKIoD3hAOpMP6gC813ny/\ntwFoseCJ0SCirNDeoqWdmoa1MN/CnzGiLGA4d5IkSriq/+WoyO+N579+Ec9sWYzvFQ2BJEotgjeG\naDJ6TMjGk+2PxVWSKIjpwLbAZrAdDXBji1D/7uv0c4vBAlHQV/dmJN4If2MA/qZA6rExAH9TELWN\nfvibggCAYmsRPLYiFNuKUGwtQrHNgwKLS3dfCyAV2M3D1YaefnRGt+ZpWPeng/qAN5QJ7u8OSbOY\npFQru+ho93i5xw6nnUuLEp0MhnMXfb9kBMrspXhm83P40re11XsGQYJRMsEkGmGUjLAZrDClX5sk\nY+a9lttMoglGyZj5HFMb+xhFU2pfyYTCQjv2Ha5GOBZBJB5BOB5BOJZ+jKe3xVpvj8Qj8DcFT+qf\nBAECLAYLbAYL7EYb7EY78tIfdqMddqMNeSY78lq8ZzfaYBDV+RGLJ+MINNW1Ct/aViEcQCTe2O7n\n55sckGUZX9VuB2q3t3rPIEgoshYeE9rFtiI4Tfm6C6GW07CO6FeU2Z5MyqgORNJBHUoHdwP2Hq7H\nnoOtVymzWwwoL7KjLN013qfMibq6yHdP1Uq2x5EIggCTUYTJIMJokFKPRhEmgwSjoXm7qLvvX08m\nyzIi8QjqoyHUxxoQioZQHwuhPhqCRTKjl70UpfZiuMxO3X3fOJQqSxLJBPxNwXTAGmEUjd2ycEZX\n6hlNxBCOh9PB3ph5ngn17wR787aGWBixZKxD57BIllRgm1qGue3o88x2Wybk22qVtqynLMuoj4Xa\nbO2mtvlRFw1BRts/2hbJggKLCy6LEwVmF9wWF9xmFwosqedOsxPG9D8VkXgjvGEfqsNeVEd8qA7X\noDriRXXYh0j82HAxSSZ4rIWZwPbYilBiK4LHWoQ8o/2Ef0D0MCQlnkjiSG24Vdf4AV8Dqv3hrAeu\nEloGdXNwZ7YZpdbvfSfsv7tPgduG+rpGCIIAQUj9gyCmH5tfC0CL91psF9D6dYv9jnkv81qALMuQ\n5dTvSTL9PNlqGzL7JFvsk9mWlFP7Ib1/Esc9ZlKW4cy3ItzQBEkSIIkiJEmAQRQgSSIkUUh9SCIk\nEUgghkgigsZkGJFEGOFEA8LxBjTEGlAfCyEUbX5MBXJSTp7we2Y1WFBqK0Evewl65aUf7SVZ/2eZ\n03dmgR7+yHWEWvWMJqJoiIURijUgFGtAQ7QBofTrhvS2UCyceh5NbYvLiRMeV4AAm8EKu8mWaZXn\nGe0wmSUcCnhTreDjtPolQYLb7EwFbjp0U49OFFjccFucsBqsXa6/LMtoiIXTge2FN+zDkYgvE+TR\nNv55sRqs6dBOdZWXtOgyby7T8b6fqXsVomjK3HMQRVOiCU2JaHp7G4/JKJriUTQlo+n7Gpo/J5Z5\nLQhCujck/WGwp77+Bnvr7cajrzvTIxKLJ3CoJowD3gZAEhFqaDrh53T0z2pH/gAnEklE40nE0h/R\neCLzOhpLpLclEWveHkvtc3T7iUMit8iAkATEJAQpDhijEAxRCOlHGJuOvjZGgebnYge+jgkDEDdB\nSJghJswQk2ZISQsk2QIpaYERFhiMCcBaj4SpDo1CAA1yEKl/KY6yGqzpoC5GL3tpJrTzTY5OhTbD\nOQsYztoiyzKaEtFjgzsT7K23pUI+fMx/zQ5THgrM7nT4plq+Lku61Wt2wWHKU/16sCzLCEbrUB32\npUPbC2+4BtVhL3yRmjb/SXEY8+CxFcFldyAUDmfCtGXQdrS34kSaL5OYJRNMkglJWUZD+uvdXm/D\nd1kkc5uhnbnEYfjudlurGxKz+XObas0lkYQMWU6mnssykkgefU9OQoacbikmU/tBzuybep1s/X76\nGJn35SRiiQRiiUQ6tBOZ17FEAvFEArFEEvHm58kkjEYJ0aY4ZCD1lZWby9zitZzZ3Gp75rHF56Qe\nBQBy631b7iPIqZASUh+ykASQepSFBGQc3ZZMvwaSSCKR2idVa8hIpLah+WvT8nWiwz8rACDKEgyw\nwCBbYUhaIMpmSEkzxIQFSJggxM0Q4mYgYUIyakIyISKRTCKRlJFIJBFPykgk5MzrRDL1vBUhCcHS\nANEagmANQbSGINpCEMxhQGi9rxFmuI1FKLJ4UGYvQR9nL5xWUA635fgtbYZzFvSU0OoqPdczdb2p\nEaFYCIWFDiQbDJnu5p4qKSdR2xhoEdo+VId9qI74UBOpzfzBM4iGVHiKR0PU3CJQj742p/dpDlxz\nq+Bt9TnpexXa++clKSfRGG9M/5MUzgR2Qzyc6SVp+O57J3GJwyBImQC3ms2IxuLpIGwO15ZBKKfD\nNHl0HyS/E6Zyh7o86eQIEGAQDTCIEiRBSj0XJEhtbpNgECU4bXkwyRbkGe1wmPLgMOVlnucZ82CW\nsn9DYFM0gfpwFPWRGOoaoqgLR1EfTj2vD0dRF46lHiONCCUCkM31mdAWrPUQLGEcU6S4EVIsH5ak\nCw6xAG5jEYotxSjKc8JhM2H0yFMQjbS/1OvJYjjrGOupH/FkHM4CC+pqm7rlfoVsiSZi6R6P1qGd\nCvbWr5sDPiHHAQgQIUIUBIhC6kas1Gsxff1UhIj0Y3qfo6/T+6D1vq0+t8U2ocW+rY9/ovOLmWu5\nbZ1fhHjc8jmdNgSD4aNtTFlON5aP/tk9+ic4vTX9uv39Utu/u2/zVlGQjgZnc5CKEiThO+F6zPPU\nY2d6nrT++9k8n35di/AONERwqKEavkYvAvEahORaNIlBJI2hY66nyDEjkpE8VBiH464f/jBr5eL0\nnUQ9gEE0wGa0okFUZ6hdZ6VGDqSu7XeU1v+YZ4vH44DXpP96ap0gCLBZjLBZjCgtaLkoTsUx+0YT\nMRwOVeObwAFU1R3C4YYj8DX5UG/0o8gT7LYyM5yJiIjSTJIRfZzl6OMsb7U9loyjV7ELPl+oW8qh\naDjPnTsXX375JQRBwN13343hw4creToiIiJFGEVDt46lViycP/30U+zduxfLli3D7t27cffdd2PZ\nsmVKnY6IiEg3FBtzsmHDBowbNw4A0LdvXwSDQYRC3dMdQERE1JMp1nL2+XwYMmRI5nVBQQG8Xi/y\n8vLa3N/ttsFgyO4dqse7E05PWE99YT31hfXUl+6qZ7fdEHaiEVt+fzir58upu0FZT91gPfWF9dSX\n7pyERLFu7eLiYvh8vszr6upqeDwepU5HRESkG4qF8+jRo7FmzRoAwNatW1FcXNxulzYREREdpVi3\n9plnnokhQ4Zg6tSpEAQB999/v1KnIiIi0hVFrznffvvtSh6eiIhIl9RdvoeIiIiOwXAmIiLSGIYz\nERGRxmhmyUgiIiJKYcuZiIhIYxjOREREGsNwJiIi0hiGMxERkcYwnImIiDSG4UxERKQxugznuXPn\nYsqUKZg6dSo2bdqkdnEU8/DDD2PKlCm46qqrsHbtWrWLo6jGxkaMGzcOK1euVLsoilm1ahV+9KMf\n4corr8T69evVLo4iGhoacNNNN6GyshJTp07F+++/r3aRsmrHjh0YN24clixZAgA4dOgQKisrMW3a\nNNxyyy2IRqMqlzA72qrnddddh+nTp+O6666D1+tVuYTZ8d16Nnv//fcxcOBARc+tu3D+9NNPsXfv\nXixbtgwPPfQQHnroIbWLpIiPP/4YO3fuxLJly/Dss89i7ty5ahdJUX/5y1/gdDrVLoZi/H4/nnrq\nKSxduhQLFy7E22+/rXaRFPHSSy/htNNOw+LFi/HEE0/o6vczHA5jzpw5OO+88zLb/vSnP2HatGlY\nunQpKioqsGLFChVLmB1t1XP+/Pm45pprsGTJEowfPx5///vfVSxhdrRVTwBoamrC008/rfgSyLoL\n5w0bNmDcuHEAgL59+yIYDCIUCqlcquw7++yz8cQTTwAA8vPzEYlEkEgkVC6VMnbv3o1du3ZhzJgx\nahdFMRs2bMB5552HvLw8FBcXY86cOWoXSRFutxuBQAAAUFdXB7fbrXKJssdkMuGZZ55BcXFxZtsn\nn3yCSy65BABw8cUXY8OGDWoVL2vaquf999+PCRMmAGj9Pe7J2qonACxcuBDTpk2DyWRS9Py6C2ef\nz9fqF76goEA3XSwtSZIEm80GAFixYgUuuugiSJKkcqmUMW/ePMyePVvtYihq//79aGxsxK9+9StM\nmzZNF3/E2/LDH/4QBw8exPjx4zF9+nTceeedahcpawwGAywWS6ttkUgk80e8sLBQF3+L2qqnzWaD\nJElIJBJYunQpLr/8cpVKlz1t1fObb77Btm3bMGnSJOXPr/gZVKb32UnfeustrFixAosWLVK7KIp4\n+eWXMWLECPTu3VvtoiguEAjgySefxMGDBzFjxgysW7cOgiCoXayseuWVV1BWVoa//e1v2LZtG+6+\n+25d30fQkt7/FiUSCdxxxx0499xzj+kK1ovf//73uOeee7rlXLoL5+LiYvh8vszr6upqxa8NqOX9\n99/HwoUL8eyzz8LhcKhdHEWsX78eVVVVWL9+PQ4fPgyTyYTS0lKcf/75ahctqwoLCzFy5EgYDAb0\n6dMHdrsdtbW1KCwsVLtoWfX555/jggsuAAAMGjQI1dXVSCQSuu31sdlsaGxshMViwZEjR47pItWT\nu+66CxUVFbjpppvULooijhw5gj179uD2228HkMqW6dOnH3OzWLborlt79OjRWLNmDQBg69atKC4u\nRl5ensqlyr76+no8/PDD+Otf/wqXy6V2cRQzf/58/Otf/8Ly5ctx9dVX48Ybb9RdMAPABRdcgI8/\n/hjJZBJ+vx/hcFhX12ObVVRU4MsvvwQAHDhwAHa7XbfBDADnn39+5u/R2rVrceGFF6pcImWsWrUK\nRqMRN998s9pFUUxJSQneeustLF++HMuXL0dxcbFiwQzosOV85plnYsiQIZg6dSoEQcD999+vdpEU\n8frrr8Pv92PWrFmZbfPmzUNZWZmKpaLOKikpwYQJE3DNNdcAAO655x6Iou7+d8aUKVNw9913Y/r0\n6YjH43jggQfULlLWbNmyBfPmzcOBAwdgMBiwZs0aPProo5g9ezaWLVuGsrIyXHHFFWoXs8vaqmdN\nTQ3MZjMqKysBpG7G7enf27bquWDBgm5rDHHJSCIiIo3R37/mREREPRzDmYiISGMYzkRERBrDcCYi\nItIYhjMREZHGMJyJ6IRWrlyZmXyBiJTHcCYiItIY3U1CQpTLFi9ejDfeeAOJRAKnn346fvGLX+CX\nv/wlLrroImzbtg0A8Mc//hElJSVYv349nnrqKVgsFlitVsyZMwclJSX48ssvMXfuXBiNRjidTsyb\nNw8AEAqFcPvtt2P37t0oKyvDk08+qbu5v4m0gi1nIp3YtGkT3nzzTTz//PNYtmwZHA4HPvroI1RV\nVeHKK6/E0qVLMWrUKCxatAiRSAT33HMPFixYgMWLF+Oiiy7C/PnzAQC/+c1vMGfOHCxZsgRnn302\n3n33XQDArl27MGfOHKxcuRI7d+7E1q1b1awuka6x5UykE5988gn27duHGTNmAEgtFn/kyBG4XC4M\nHToUQGp62//5n//Bt99+i8LCQpSWlgIARo0ahRdeeAG1tbWoq6vDgAEDAADXXXcdgNQ152HDhsFq\ntQJITTdaX1/fzTUkyh0MZyKdMJlMGDt2LO67777Mtv379+PKK6/MvJZlGYIgHNMd3XJ7ezP6fneB\nCs78S6QcdmsT6cSZZ56J9957Dw0NDQCA559/Hl6vF8FgEF999RWA1JKNAwcOxKmnnoqamhocPHgQ\nALBhwwZ873vfg9vthsvlwqZNmwAAixYtwvPPP69OhYhyGFvORDoxbNgwXHvttaisrITZbEZxcTHO\nOecclJSUYOXKlfjDH/4AWZbx+OOPw2Kx4KGHHsKtt94Kk8kEm82Ghx56CADwyCOPYO7cuTAYDHA4\nHHjkkUewdu1alWtHlFu4KhWRju3fvx/Tpk3De++9p3ZRiOgksFubiIhIY9hyJiIi0hi2nImIiDSG\n4UxERKQxDGciIiKNYTgTERFpDMOZiIhIYxjOREREGvP/AV0P01dnBga2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f11bd34f780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}