Audio Datasets and Emotions Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEzv6ly8Dswx"
      },
      "source": [
        "## Data Import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "1JV3_hEQoloW"
      },
      "outputs": [],
      "source": [
        "# Data\n",
        "Emodb =\"/content/DSER-MIDCA/data/EMODB/wav/\"\n",
        "Ravdess = \"/content/DSER-MIDCA/data/RAVDESS/\"\n",
        "Iemocap = \"/content/DSER-MIDCA/data/IEMOCAP_full_release\"\n",
        "Savee = \"/content/DSER-MIDCA/data/SAVEE/AudioData/AudioData\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "lR2rRW-DvYDT"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBTdehtAve_6",
        "outputId": "4e850d5b-7700-48b3-84cf-b048d048f5ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav\n",
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            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav/Ses01M_script02_2\n",
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            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav/Ses01F_impro01\n",
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            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav/Ses01F_impro05\n",
            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav/Ses01M_script01_2\n",
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            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav/Ses01F_impro02\n",
            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav/Ses01F_impro06\n",
            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav/Ses01M_script03_1\n",
            "/content/DSER-MIDCA/data/IEMOCAP/Session1/sentences/wav/Ses01F_script02_1\n",
            "7532\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "import os\n",
        "list_files=[]\n",
        "for dirname, _, filenames in os.walk('/content/DSER-MIDCA/data/IEMOCAP'):\n",
        "    \n",
        "    if \"sentences/wav\" not in dirname:\n",
        "      continue\n",
        "    print(dirname)\n",
        "    for filename in filenames:\n",
        "        if  \".wav\" in filename :\n",
        "          list_files.append(os.path.join(dirname, filename))\n",
        "          # print(os.path.join(dirname, filename))\n",
        "print(len(list_files))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## IEMOCAP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGGlLrgwBF1f",
        "outputId": "ec868d13-28cc-4036-8ff5-8f61ffb01933"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10039, 7)"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "csv_file = '/content/DSER-MIDCA/data/IEMOCAP/Documentation/iemocap_full_dataset.csv'\n",
        "\n",
        "iemocap_df = pd.read_csv(csv_file)\n",
        "\n",
        "iemocap_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XP6qbNypBI-U",
        "outputId": "e13b2c90-c36f-42c8-d5bb-cfdb5caecf0a"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>session</th>\n",
              "      <th>method</th>\n",
              "      <th>gender</th>\n",
              "      <th>emotion</th>\n",
              "      <th>n_annotators</th>\n",
              "      <th>agreement</th>\n",
              "      <th>path</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>script</td>\n",
              "      <td>F</td>\n",
              "      <td>neu</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Session1/sentences/wav/Ses01F_script02_1/Ses01...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>script</td>\n",
              "      <td>F</td>\n",
              "      <td>fru</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Session1/sentences/wav/Ses01F_script02_1/Ses01...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>script</td>\n",
              "      <td>F</td>\n",
              "      <td>xxx</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Session1/sentences/wav/Ses01F_script02_1/Ses01...</td>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>script</td>\n",
              "      <td>F</td>\n",
              "      <td>sur</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Session1/sentences/wav/Ses01F_script02_1/Ses01...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>script</td>\n",
              "      <td>F</td>\n",
              "      <td>neu</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Session1/sentences/wav/Ses01F_script02_1/Ses01...</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e0f18ee-5e93-4818-a9e9-9a21baf51285')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   session  method gender emotion  n_annotators  agreement  \\\n",
              "0        1  script      F     neu             3          3   \n",
              "1        1  script      F     fru             3          2   \n",
              "2        1  script      F     xxx             0          0   \n",
              "3        1  script      F     sur             3          2   \n",
              "4        1  script      F     neu             3          2   \n",
              "\n",
              "                                                path  \n",
              "0  Session1/sentences/wav/Ses01F_script02_1/Ses01...  \n",
              "1  Session1/sentences/wav/Ses01F_script02_1/Ses01...  \n",
              "2  Session1/sentences/wav/Ses01F_script02_1/Ses01...  \n",
              "3  Session1/sentences/wav/Ses01F_script02_1/Ses01...  \n",
              "4  Session1/sentences/wav/Ses01F_script02_1/Ses01...  "
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "iemocap_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
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          "height": 206
        },
        "id": "XrfGHwsD8YQS",
        "outputId": "ba83b3f9-de23-4565-ec8f-84f2b15542e2"
      },
      "outputs": [
        {
          "data": {
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              "      <th>session</th>\n",
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              "      <th>emotion</th>\n",
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            ],
            "text/plain": [
              "   session  method gender emotion  n_annotators  agreement  \\\n",
              "0        1  script      F     neu             3          3   \n",
              "1        1  script      F     fru             3          2   \n",
              "2        1  script      F     xxx             0          0   \n",
              "3        1  script      F     sur             3          2   \n",
              "4        1  script      F     neu             3          2   \n",
              "\n",
              "                                                path  \n",
              "0  Session1/sentences/wav/Ses01F_script02_1/Ses01...  \n",
              "1  Session1/sentences/wav/Ses01F_script02_1/Ses01...  \n",
              "2  Session1/sentences/wav/Ses01F_script02_1/Ses01...  \n",
              "3  Session1/sentences/wav/Ses01F_script02_1/Ses01...  \n",
              "4  Session1/sentences/wav/Ses01F_script02_1/Ses01...  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "iemocap_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "bPxYCJMCBXnN"
      },
      "outputs": [],
      "source": [
        "iemocap_df=iemocap_df[iemocap_df[\"agreement\"]>0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcu2_Qt0BKfe",
        "outputId": "75ced52c-e6a6-4669-8fd2-e148620ccf00"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "fru    1849\n",
              "neu    1708\n",
              "ang    1103\n",
              "sad    1084\n",
              "exc    1041\n",
              "hap     595\n",
              "sur     107\n",
              "fea      40\n",
              "oth       3\n",
              "dis       2\n",
              "Name: emotion, dtype: int64"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "iemocap_df.emotion.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "WmtW2vfiaGxI"
      },
      "outputs": [],
      "source": [
        "emo_dict={\"xxx\":\"xxx\",\"fru\":\"frustration\",\"neu\":\"neutral\",\"ang\":\"angry\",\"sad\":\"sad\",\"exc\":\"excitement\",\"hap\":\"happy\",\"sur\":\"surprise\",\"fea\":\"fear\",\"oth\":\"other\",\"dis\":\"disgust\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "u46C_WPfb9Ev"
      },
      "outputs": [],
      "source": [
        "iemocap_df[\"Emotions\"]=iemocap_df.emotion.map(emo_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wj5NFaAdcIgP",
        "outputId": "a9e1b626-65a3-4456-9ea7-8550de7e4818"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "frustration    1849\n",
              "neutral        1708\n",
              "angry          1103\n",
              "sad            1084\n",
              "excitement     1041\n",
              "happy           595\n",
              "surprise        107\n",
              "fear             40\n",
              "other             3\n",
              "disgust           2\n",
              "Name: Emotions, dtype: int64"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "iemocap_df[\"Emotions\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "uA2CAg6sZ2NE"
      },
      "outputs": [],
      "source": [
        "# dataframe for emotion of files\n",
        "Iemocap_df = pd.DataFrame(iemocap_df, columns=['Emotions',\"path\"])\n",
        "\n",
        "Iemocap_df.columns=['Emotions',\"Path\"]\n",
        "Iemocap_df[\"Source\"]=\"IEMOCAP\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "wjMP6QZ4Q-L2"
      },
      "outputs": [],
      "source": [
        "Iemocap_df[\"Path\"] =  \"/content/DSER-MIDCA/data/IEMOCAP/\"+Iemocap_df[\"Path\"] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "uxpXwZpkBPtA",
        "outputId": "eaa027a8-9202-4fc8-8f05-fa2cb3bb7408"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 720x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,3))\n",
        "plt.title('Count of Emotions', size=16)\n",
        "\n",
        "sns.countplot(Iemocap_df.Emotions)\n",
        "plt.ylabel('Count', size=12)\n",
        "plt.xlabel('Emotions', size=12)\n",
        "sns.despine(top=True, right=True, left=False, bottom=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8HAbhOGqxEl"
      },
      "source": [
        "## Ravdess"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "jDHRkNBrnfEb"
      },
      "outputs": [],
      "source": [
        "ravdess_directory_list = os.listdir(Ravdess)\n",
        "\n",
        "file_emotion = []\n",
        "file_path = []\n",
        "for dir in ravdess_directory_list:\n",
        "    # as their are 20 different actors in our previous directory we need to extract files for each actor.\n",
        "    actor = os.listdir(Ravdess + dir)\n",
        "    for file in actor:\n",
        "        part = file.split('.')[0]\n",
        "        part = part.split('-')\n",
        "        # third part in each file represents the emotion associated to that file.\n",
        "        file_emotion.append(int(part[2]))\n",
        "        file_path.append(Ravdess + dir + '/' + file)\n",
        "        \n",
        "# dataframe for emotion of files\n",
        "emotion_df = pd.DataFrame(file_emotion, columns=['Emotions'])\n",
        "\n",
        "# dataframe for path of files.\n",
        "path_df = pd.DataFrame(file_path, columns=['Path'])\n",
        "Ravdess_df = pd.concat([emotion_df, path_df], axis=1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Y9Ii-QrLnm-A",
        "outputId": "663bd6c1-f95f-4029-f221-ba87d8c8320a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dbd032ce-7b5f-4524-86c7-ffa3d47cd75c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emotions</th>\n",
              "      <th>Path</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1435</th>\n",
              "      <td>7</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1436</th>\n",
              "      <td>4</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1437</th>\n",
              "      <td>7</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1438</th>\n",
              "      <td>6</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1439</th>\n",
              "      <td>4</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1440 rows × 2 columns</p>\n",
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            "text/plain": [
              "      Emotions                                               Path\n",
              "0            6  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...\n",
              "1            5  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...\n",
              "2            6  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...\n",
              "3            5  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...\n",
              "4            1  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...\n",
              "...        ...                                                ...\n",
              "1435         7  /content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...\n",
              "1436         4  /content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...\n",
              "1437         7  /content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...\n",
              "1438         6  /content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...\n",
              "1439         4  /content/DSER-MIDCA/data/RAVDESS/Actor_04/03-0...\n",
              "\n",
              "[1440 rows x 2 columns]"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Ravdess_df"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "YV_L-QUDnhpw",
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              "      <th></th>\n",
              "      <th>Emotions</th>\n",
              "      <th>Path</th>\n",
              "      <th>Source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>fear</td>\n",
              "      <td>/content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...</td>\n",
              "      <td>RAVDESS</td>\n",
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              "      <th>1</th>\n",
              "      <td>angry</td>\n",
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              "      <td>RAVDESS</td>\n",
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              "      <td>RAVDESS</td>\n",
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              "      <td>RAVDESS</td>\n",
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            "text/plain": [
              "  Emotions                                               Path   Source\n",
              "0     fear  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...  RAVDESS\n",
              "1    angry  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...  RAVDESS\n",
              "2     fear  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...  RAVDESS\n",
              "3    angry  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...  RAVDESS\n",
              "4  neutral  /content/DSER-MIDCA/data/RAVDESS/Actor_02/03-0...  RAVDESS"
            ]
          },
          "execution_count": 39,
          "metadata": {},
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        }
      ],
      "source": [
        "# changing integers to actual emotions.\n",
        "Ravdess_df.Emotions.replace({1:'neutral', 2:'calm', 3:'happy', 4:'sad', 5:'angry', 6:'fear', 7:'disgust', 8:'surprise'}, inplace=True)\n",
        "Ravdess_df[\"Source\"]=\"RAVDESS\"\n",
        "Ravdess_df.head()"
      ]
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      "metadata": {
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        "outputId": "5b9a0a5c-74b8-423f-c964-c0f2a0c2c678"
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title('Count of Emotions', size=16)\n",
        "sns.countplot(Ravdess_df.Emotions)\n",
        "plt.ylabel('Count', size=12)\n",
        "plt.xlabel('Emotions', size=12)\n",
        "sns.despine(top=True, right=True, left=False, bottom=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFluEdxPoLP5",
        "outputId": "adea3000-d1e4-457a-b8ef-2ee2c7ee0d0e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "fear        192\n",
              "angry       192\n",
              "calm        192\n",
              "disgust     192\n",
              "happy       192\n",
              "surprise    192\n",
              "sad         192\n",
              "neutral      96\n",
              "Name: Emotions, dtype: int64"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Ravdess_df.Emotions.value_counts()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "nvG88dh9ovBj"
      },
      "source": [
        "## SAVEE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Mug-ja5HoYKA",
        "outputId": "6a4b5a63-e1f1-4a56-8f31-8aed91ef3bff"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emotions</th>\n",
              "      <th>Path</th>\n",
              "      <th>Source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>happy</td>\n",
              "      <td>/content/DSER-MIDCA/data/SAVEE/AudioData/Audio...</td>\n",
              "      <td>SAVEE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>neutral</td>\n",
              "      <td>/content/DSER-MIDCA/data/SAVEE/AudioData/Audio...</td>\n",
              "      <td>SAVEE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>sad</td>\n",
              "      <td>/content/DSER-MIDCA/data/SAVEE/AudioData/Audio...</td>\n",
              "      <td>SAVEE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>surprise</td>\n",
              "      <td>/content/DSER-MIDCA/data/SAVEE/AudioData/Audio...</td>\n",
              "      <td>SAVEE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>angry</td>\n",
              "      <td>/content/DSER-MIDCA/data/SAVEE/AudioData/Audio...</td>\n",
              "      <td>SAVEE</td>\n",
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              "          const element = document.querySelector('#df-919bfd18-fb24-4286-a92b-00dbf824d301');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ],
            "text/plain": [
              "   Emotions                                               Path Source\n",
              "0     happy  /content/DSER-MIDCA/data/SAVEE/AudioData/Audio...  SAVEE\n",
              "1   neutral  /content/DSER-MIDCA/data/SAVEE/AudioData/Audio...  SAVEE\n",
              "2       sad  /content/DSER-MIDCA/data/SAVEE/AudioData/Audio...  SAVEE\n",
              "3  surprise  /content/DSER-MIDCA/data/SAVEE/AudioData/Audio...  SAVEE\n",
              "4     angry  /content/DSER-MIDCA/data/SAVEE/AudioData/Audio...  SAVEE"
            ]
          },
          "execution_count": 162,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "\n",
        "savee_directory_list=os.listdir(Savee)\n",
        "\n",
        "file_emotion = []\n",
        "file_path = []\n",
        "\n",
        "for folder in savee_directory_list:\n",
        "    if \".\" in folder:\n",
        "      continue\n",
        "    file_directory_list = os.listdir(Savee + \"/\"+folder)\n",
        "    for file in file_directory_list:\n",
        "      \n",
        "      \n",
        "      os.rename(Savee + \"/\"+folder+\"/\"+file, Savee + \"/\"+folder+\"/\"+folder+\"_\"+file)\n",
        "      file_path.append(Savee + \"/\"+folder+\"/\"+folder+\"_\"+file)\n",
        "      ele = file[:-6]\n",
        "      if ele=='a':\n",
        "          file_emotion.append('angry')\n",
        "      elif ele=='d':\n",
        "          file_emotion.append('disgust')\n",
        "      elif ele=='f':\n",
        "          file_emotion.append('fear')\n",
        "      elif ele=='h':\n",
        "          file_emotion.append('happy')\n",
        "      elif ele=='n':\n",
        "          file_emotion.append('neutral')\n",
        "      elif ele=='sa':\n",
        "          file_emotion.append('sad')\n",
        "      else:\n",
        "          file_emotion.append('surprise')\n",
        "        \n",
        "# dataframe for emotion of files\n",
        "emotion_df = pd.DataFrame(file_emotion, columns=['Emotions'])\n",
        "\n",
        "# dataframe for path of files.\n",
        "path_df = pd.DataFrame(file_path, columns=['Path'])\n",
        "Savee_df = pd.concat([emotion_df, path_df], axis=1)\n",
        "Savee_df[\"Source\"]=\"SAVEE\"\n",
        "Savee_df.head()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyVvifqKosU4",
        "outputId": "e463f82f-e952-4785-a767-846737eec49d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "neutral     120\n",
              "happy        60\n",
              "sad          60\n",
              "surprise     60\n",
              "angry        60\n",
              "fear         60\n",
              "disgust      60\n",
              "Name: Emotions, dtype: int64"
            ]
          },
          "execution_count": 163,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Savee_df.Emotions.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "SYYmirJVqYJC",
        "outputId": "d6bea3af-eb92-4535-8338-2b308dc97675"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title('Count of Emotions', size=16)\n",
        "sns.countplot(Savee_df.Emotions)\n",
        "plt.ylabel('Count', size=12)\n",
        "plt.xlabel('Emotions', size=12)\n",
        "sns.despine(top=True, right=True, left=False, bottom=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## EMODB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "VD8X_MZ1qqlf",
        "outputId": "23ebc7a6-d75a-4030-c254-3809d57f1947"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emotions</th>\n",
              "      <th>Path</th>\n",
              "      <th>Source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>angry</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/11b03Wa.wav</td>\n",
              "      <td>EMODB</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>angry</td>\n",
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              "      <td>EMODB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>sad</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/16b03Ta.wav</td>\n",
              "      <td>EMODB</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sad</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/10a05Tb.wav</td>\n",
              "      <td>EMODB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fear</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/14b10Ad.wav</td>\n",
              "      <td>EMODB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>happy</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/13b03Fd.wav</td>\n",
              "      <td>EMODB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>angry</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/11b01Wd.wav</td>\n",
              "      <td>EMODB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>happy</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/11a05Fc.wav</td>\n",
              "      <td>EMODB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>sad</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/14b09Lb.wav</td>\n",
              "      <td>EMODB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>sad</td>\n",
              "      <td>/content/DSER-MIDCA/data/EMODB/wav/08a02Tb.wav</td>\n",
              "      <td>EMODB</td>\n",
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            ],
            "text/plain": [
              "  Emotions                                            Path Source\n",
              "0    angry  /content/DSER-MIDCA/data/EMODB/wav/11b03Wa.wav  EMODB\n",
              "1    angry  /content/DSER-MIDCA/data/EMODB/wav/16a04Wb.wav  EMODB\n",
              "2      sad  /content/DSER-MIDCA/data/EMODB/wav/16b03Ta.wav  EMODB\n",
              "3      sad  /content/DSER-MIDCA/data/EMODB/wav/10a05Tb.wav  EMODB\n",
              "4     fear  /content/DSER-MIDCA/data/EMODB/wav/14b10Ad.wav  EMODB\n",
              "5    happy  /content/DSER-MIDCA/data/EMODB/wav/13b03Fd.wav  EMODB\n",
              "6    angry  /content/DSER-MIDCA/data/EMODB/wav/11b01Wd.wav  EMODB\n",
              "7    happy  /content/DSER-MIDCA/data/EMODB/wav/11a05Fc.wav  EMODB\n",
              "8      sad  /content/DSER-MIDCA/data/EMODB/wav/14b09Lb.wav  EMODB\n",
              "9      sad  /content/DSER-MIDCA/data/EMODB/wav/08a02Tb.wav  EMODB"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "berlin_directory_list = os.listdir(Emodb)\n",
        "\n",
        "file_emotion = []\n",
        "file_path = []\n",
        "\n",
        "for file in berlin_directory_list:\n",
        "    file_path.append(Emodb + file)\n",
        "    \n",
        "    ele = file[5]\n",
        "    if ele=='W':\n",
        "        file_emotion.append('angry')\n",
        "    elif ele=='E':\n",
        "        file_emotion.append('disgust')\n",
        "    elif ele=='A':\n",
        "        file_emotion.append('fear')\n",
        "    elif ele=='F':\n",
        "        file_emotion.append('happy')\n",
        "    elif ele=='N':\n",
        "        file_emotion.append('neutral')\n",
        "    elif ele=='L'or ele=='T':\n",
        "        file_emotion.append('sad')\n",
        "    else:\n",
        "        file_emotion.append('surprise')\n",
        "        \n",
        "# dataframe for emotion of files\n",
        "emotion_df = pd.DataFrame(file_emotion, columns=['Emotions'])\n",
        "\n",
        "# dataframe for path of files.\n",
        "path_df = pd.DataFrame(file_path, columns=['Path'])\n",
        "Berlin_df = pd.concat([emotion_df, path_df], axis=1)\n",
        "Berlin_df[\"Source\"]=\"EMODB\"\n",
        "Berlin_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCj4jIOgss1Z",
        "outputId": "eeaf5f4d-3794-4fe5-86bb-c68687510c6a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sad        143\n",
              "angry      127\n",
              "neutral     79\n",
              "happy       71\n",
              "fear        69\n",
              "disgust     46\n",
              "Name: Emotions, dtype: int64"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Berlin_df.Emotions.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "7SW3J0VKszTm",
        "outputId": "e8dc9be8-d5d3-4c0f-e891-a4a465dc3555"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title('Count of Emotions', size=16)\n",
        "sns.countplot(Berlin_df.Emotions)\n",
        "plt.ylabel('Count', size=12)\n",
        "plt.xlabel('Emotions', size=12)\n",
        "sns.despine(top=True, right=True, left=False, bottom=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndQvJ-uMtvc3",
        "outputId": "4993e1e2-b9ec-4dea-a14a-983e51824bf1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "neutral        2003\n",
              "frustration    1849\n",
              "angry          1482\n",
              "sad            1479\n",
              "excitement     1041\n",
              "happy           918\n",
              "fear            361\n",
              "surprise        359\n",
              "disgust         300\n",
              "calm            192\n",
              "other             3\n",
              "Name: Emotions, dtype: int64"
            ]
          },
          "execution_count": 164,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_df =pd.concat([Iemocap_df,\n",
        "Ravdess_df,\n",
        "Savee_df,\n",
        "Berlin_df])\n",
        "final_df.Emotions.value_counts()\n"
      ]
    },
    {
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      "execution_count": 165,
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        },
        "id": "o-DiHTBrdzIj",
        "outputId": "dbdd6b0f-a6ef-4e64-8153-49f45410f7da"
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      "outputs": [
        {
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            "text/plain": [
              "        Emotions                                               Path   Source\n",
              "0        neutral  /content/DSER-MIDCA/data/IEMOCAP/Session1/sent...  IEMOCAP\n",
              "1    frustration  /content/DSER-MIDCA/data/IEMOCAP/Session1/sent...  IEMOCAP\n",
              "3       surprise  /content/DSER-MIDCA/data/IEMOCAP/Session1/sent...  IEMOCAP\n",
              "4        neutral  /content/DSER-MIDCA/data/IEMOCAP/Session1/sent...  IEMOCAP\n",
              "6          angry  /content/DSER-MIDCA/data/IEMOCAP/Session1/sent...  IEMOCAP\n",
              "..           ...                                                ...      ...\n",
              "530        angry     /content/DSER-MIDCA/data/EMODB/wav/15a02Wb.wav    EMODB\n",
              "531          sad     /content/DSER-MIDCA/data/EMODB/wav/09a05Lc.wav    EMODB\n",
              "532          sad     /content/DSER-MIDCA/data/EMODB/wav/15b09La.wav    EMODB\n",
              "533        happy     /content/DSER-MIDCA/data/EMODB/wav/14a02Fd.wav    EMODB\n",
              "534         fear     /content/DSER-MIDCA/data/EMODB/wav/12b10Ac.wav    EMODB\n",
              "\n",
              "[9987 rows x 3 columns]"
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          "metadata": {},
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        }
      ],
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        "final_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
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          "height": 302
        },
        "id": "yxGBEbGMDtV_",
        "outputId": "c19a9289-6bfd-4733-ab0d-8d96e7dae329"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
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",
            "text/plain": [
              "<Figure size 720x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,3))\n",
        "plt.title('Count of Emotions', size=16)\n",
        "sns.countplot(final_df.Emotions)\n",
        "plt.ylabel('Count', size=12)\n",
        "plt.xlabel('Emotions', size=12)\n",
        "sns.despine(top=True, right=True, left=False, bottom=False,)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "-bHdwaeynNYB"
      },
      "outputs": [],
      "source": [
        "common_emotions=list(set(Iemocap_df.Emotions)&set(Ravdess_df.Emotions)&set(Savee_df.Emotions)&set(Berlin_df.Emotions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "hIR-QPcSepqr"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "LQWQx-8umE51"
      },
      "outputs": [],
      "source": [
        "df_common=final_df[final_df[\"Emotions\"].isin(common_emotions)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "4LAxkkszm_0n"
      },
      "outputs": [],
      "source": [
        "df_count=df_common[[\"Source\",\"Emotions\"]].value_counts().reset_index()\n",
        "df_count.columns=[\"Source\",\"Emotions\",\"EmotionCount\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "7hsKsVPvmGYx",
        "outputId": "70155fa9-7371-4b7d-b527-51b8cc3f8207"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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53je0bk/a642t/FqWfOLChq1sonJJkqR5byoDET4APB/YF9iivT4XeP803/tEYGwE6J7ACQPlr2mjSLcDbm/dqN8Adk6yVhuAsHMrkyRJmvemck3b7sAWVXVLW74kybnAecCbl7ZhkmPoBhKsk+QaulGg7wOOS/Ja4Crg5a36ycCudDfzvQvYG6CqFid5F3BOq3fY2KAESZKk+W4qSVumWP6AqnrlBKueN6RuAftNsJ8jgSOX9X6SJEnzzVS6R78IfC3JC5L8cZJdgK+2ckmSJM2iqZxp+xfgbcDHgMfTDQI4Bnj3LMQlaRY88yPPHHUIvfC9N3xv1CFI0oMs80xbkmcmeX9V3VtV76iqJ1XVo9qTDB4ObLWsfUiSJGn5TKZ79F+BMydY9x3grTMXjiRJkoaZTNK2JXDKBOu+BWw9c+FIkiRpmMkkbY8GJrqB7qrAGjMXjiRJkoaZTNL2M7ob2Q6zc1svSZKkWTSZ0aP/AfxXkpWBr1bV/UlWAnajG0l64GwGKEmSpEkkbVX1+STrA0cBD09yM7AOcA9wSFUdM8sxSpIkPeRN6j5tVfWhJJ8GtgceC9wCfL+qfjWbwUmSJKkz6ZvrtgTNB7RLkiSNwFQeYyVJkqQRMWmTJEnqAZM2SZKkHjBpkyRJ6gGTNkmSpB4waZMkSeoBkzZJkqQeMGmTJEnqAZM2SZKkHjBpkyRJ6gGTNkmSpB4waZMkSeoBkzZJkqQeMGmTJEnqAZM2SZKkHjBpkyRJ6gGTNkmSpB4waZMkSeoBkzZJkqQeGHnSluTKJBck+UmSha1s7SSnJrmsva7VypPkw0kWJTk/yVajjV6SJGlujDxpa55TVVtW1TZt+SDgtKraHDitLQO8ENi8TfsAn5jzSCVJkkZgRUnaxnsJcFSbPwrYbaD8s9U5G1gzyeNGEaAkSdJcWhGStgK+meRHSfZpZetV1XVt/npgvTa/AXD1wLbXtLIlJNknycIkC2+66abZiluSJGnOrDLqAIAdquraJP8HODXJzwZXVlUlqanssKqOAI4A2Gabbaa0rSRJ0opo5Gfaqura9nojcDywLXDDWLdne72xVb8W2Ghg8w1bmSRJ0rw20qQtyWpJ1hibB3YGLgROBPZs1fYETmjzJwKvaaNItwNuH+hGlSRJmrdG3T26HnB8krFYPl9VpyQ5BzguyWuBq4CXt/onA7sCi4C7gL3nPmRJkqS5N9KkraouB7YYUn4L8Lwh5QXsNwehSZIkrVBGfk2bJEmSls2kTZIkqQdM2iRJknrApE2SJKkHTNokSZJ6YNS3/JAkaUZ99J++NuoQemH/D7541CFoijzTJkmS1AMmbZIkST1g0iZJktQDJm2SJEk94EAESZK0XA5/1ctGHUIvvPXoLy3X9p5pkyRJ6gGTNkmSpB4waZMkSeoBkzZJkqQeMGmTJEnqAZM2SZKkHjBpkyRJ6gGTNkmSpB4waZMkSeoBkzZJkqQeMGmTJEnqAZM2SZKkHjBpkyRJ6gGTNkmSpB4waZMkSeoBkzZJkqQeMGmTJEnqAZM2SZKkHuhl0pZklySXJFmU5KBRxyNJkjTbepe0JVkZ+BjwQuDJwCuTPHm0UUmSJM2u3iVtwLbAoqq6vKruBY4FXjLimCRJkmZVH5O2DYCrB5avaWWSJEnzVqpq1DFMSZKXAbtU1eva8quBp1fV/gN19gH2aYt/CFwy54HOjHWAm0cdxEOMbT73bPO5Z5vPPdt87vW1zW+uql2GrVhlriOZAdcCGw0sb9jKHlBVRwBHzGVQsyHJwqraZtRxPJTY5nPPNp97tvncs83n3nxs8z52j54DbJ5k0yQPA14BnDjimCRJkmZV7860VdV9SfYHvgGsDBxZVReNOCxJkqRZ1bukDaCqTgZOHnUcc6D3Xbw9ZJvPPdt87tnmc882n3vzrs17NxBBkiTpoaiP17RJkiQ95Ji0reCSbJLkb6e57R0zHY80TPs7vXDUcWj5+D1OT5I3Jrk4yedGHct8lOTQJP+c5LAkz5+D99ttRX3Skknbim8TYGjSlqSX1yT2le0taQL/CPx5Ve0x3R14fFm2qnpHVX1rDt5qN7rHZK5wTNpmSfsf68VJPpXkoiTfTPLIJE9MckqSHyX5bpI/avUXtBsHj20/dpbsfcCzkvwkyZuT7JXkxCTfBk5LsnqS05Kcm+SCJD7Sq0ny1dbOF7UbLpPkjiSHJzkvydlJ1mvlT2zLFyR591j7J9mpfU8nAj9t/9M7YOA9Dk/yppF8wBXPykP+3l+f5JzW3l9O8ih44O/9k0kWJrk0yYta+V5JTkhyepLLkhzSym33KUiyWpKvt3a/MMnfJHlH+y4uTHJEkrS6W7d65wH7jTj03knySWAz4H+TvDXJkUl+mOTHY8fj9nvw3XacPjfJM1r5EseXEX6MFU5ry0uTnEV3k/wlfieTvC/JT5Ocn+TfW9nSjuMnDez7o0n2Graf9t38JfBv7Xf3iXP7yZehqpxmYaI7Q3YfsGVbPg54FXAasHkrezrw7Ta/AHjZwPZ3tNedgJMGyveie3TX2m15FeDRbX4dYBG/H2Byx6jbYcTfwVgbPRK4EHgsUMCLW/kHgLe1+ZOAV7b5fce1/53ApgPf67ltfiXg58BjR/1ZRz0t5e/9sQN13g28oc0vAE5pbbh5+5t+RPv7vq59V2Pf2za2+5S/j78GPjWw/Jixfw9t+X8G/h2cDzy7zf8bcOGo4+/bBFzZjr/vAV7VytYELgVWAx4FPKKVbw4sbPNLHF+cHmjPrYELWrs9uv2u/XM7brysHR8uGfitW7O9Lu04Pvg7+tF2rJloPwsY+D1ekSbPtM2uK6rqJ23+R3Q/PM8AvpjkJ8B/AY+bxn5PrarFbT7Ae5KcD3yL7jms6y1X1PPHG9vZg7PpnqKxOXAv3T9s+P13ArA98MU2//lx+/lhVV0BUFVXArckeSqwM/Djqrpltj5Azwz7e/+TdibhAmAP4CkD9Y+rqvur6jLgcuCPWvmpVXVLVf0G+Aqwg+0+ZRcAf57k/UmeVVW3A89J8oP2XTwXeEqSNel+qM5s2/3PqAKeJ3YGDmrH99Pp/iOyMbAq8KnW9l9kya63B44vesCzgOOr6q6q+hUPvoH+7cDdwGeS/BVwVytf2nF8mIn2s8KyD3123TMw/zu6ZOq2qtpySN37aN3VSVYCHraU/d45ML8HsC6wdVX9NsmVdAeKh7QkOwHPB7avqruSnE7XLr+t9l8puu9kMv8G7hy3/Gm6/6WtDxw5E/HOE+P/3h9J9z/W3arqvNYdsdNAnfH3G6pllNvuk1RVlybZCtgVeHeS0+i6PrepqquTHIrHidkQ4K+raonnXbf2vgHYgu44f/fA6vHHFy1DdTfZ3xZ4Ht2Zt/3p/iMykQd+X5tHTHM/I+eZtrn1K+CKJLsDpLNFW3cl3Slh6PrTV23zvwbWWMo+HwPc2BK25wBPmPGo++kxwK0tYfsjYLtl1D+brksJukejLc3xwC7A0+iezKGJrQFcl2RVuv9gDNo9yUrtmpHN6LopoDtDtHaSR9JdEPy9Vm67T1KSxwN3VdXRdF2eW7VVNydZne4Hiqq6DbgtyQ5t/bQvpBfQ/V2+YeB6wae28scA11XV/cCr6Z7mo4mdCezWrotdA3jx4Mr2N/yY6m60/2a6ZBgmPo5fBTw5ycPb2eXnLWM/y/rdHRnPtM29PYBPJHkbXWJ2LHAe8CnghNaddwq//9/X+cDvWvkC4NZx+/sc8LV22n0h8LNZ/wT9cAqwb5KL6ZKBs5dR/wDg6CRvbdvePlHFqro3yXfozpr+bqYCnqfeDvwAuKm9Dh4IfwH8kO6alX2r6u72W/dD4MvAhsDRVbUQbPcp+lO6C6nvB34L/ANdAnwhcD3dM5zH7A0cmaSAb851oPPMu4D/BM5vPSZXAC8CPg58OclrWPL4riGq6twkX6D7bbyRJf9eoTuOnJDkEXRnNw9s5UOP4+3s8nF0f/9XAD9exn6OpevOfiPdtW0/n4WPOS0+EUEC0o1q/E1VVZJX0F3MOnQkbjsYnwvs3q7H0hQlWUB3YfCXxpXvRdeFt/+QbWx3SROaynG8rzzTJnW2Bj7aujVuA/5uWKV0N1w8ie4iWROHOWK7S5qESR3H+8wzbZIkST3gQARJkqQeMGmTJEnqAZM2SZKkHjBpk6QZkO5Zqm8fdRyS5i8HIkiaV9pTQdajeyrDmAXDbiOyHO+xF/C6qtphWXUlaaZ4yw9J89GLq+pbow5CkmaS3aOSHhKS7JXke0n+I8ltSS5P8oxWfnWSG5PsOVD/MUk+m+SmJFcleVt77NYfA58Etk9yR5LbWv0FSd49sP3rkyxKsjjJie3RUmPrKsm+SS5rsXxs4NFHT0pyRpLbk9zc7gwvSSZtkh5Snk73aLjHAp+ne1zN04AnAa+iuzHn6q3uR+ieGbkZsCPwGmDvqroY2Bf4flWtXlVrjn+TJM8F3gu8HHgc3bMPjx1X7UXtvf+s1XtBK38X3eOk1qJ7lNdHlvtTS5oXTNokzUdfbWewxqbXt/Irquq/27NLvwBsBBxWVfdU1TeBe4EnJVmZ7oHTB1fVr6vqSuCDdA/7now9gCOr6tyqugc4mO7M3CYDdd5XVbdV1S+A7wBbtvLfAk8AHl9Vd1fVWdNsA0nzjEmbpPlot6pac2D6VCu/YaDObwCqanzZ6sA6wKp0Z8jGXAVsMMn3f/zgtlV1B3DLuO2vH5i/q70vwL/QPbz6h0kuSjLvHsUjaXociCBJD3Yzvz/j9dNWtjFwbZtf1rD7X7ZtAUiyGl2X7LUTbjG246rrgde37XYAvpXkzKpaNJUPIGn+8UybJI3Tuk+PAw5PskaSJwAHAke3KjcAGyZ52AS7OAbYO8mWSR4OvAf4QetmXaokuyfZsC3eSpcg3j/9TyNpvjBpkzQffa2N7Bybjp/GPt4A3AlcDpxFN3DhyLbu28BFwPVJbh6/YbvdyNuBLwPXAU+ku0ZuMp4G/CDJHcCJwJuq6vJpxC9pnvHmupIkST3gmTZJkqQeMGmTJEnqAZM2SZKkHjBpkyRJ6gGTNkmSpB4waZMkSeoBkzZJkqQeMGmTJEnqAZM2SZKkHvj/EhwQ4suiz04AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 720x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,3))\n",
        "plt.title('Count of Emotions', size=16)\n",
        "sns.countplot(df_common.Emotions)\n",
        "plt.ylabel('Count', size=12)\n",
        "plt.xlabel('Emotions', size=12)\n",
        "sns.despine(top=True, right=True, left=False, bottom=False,)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "_i_utZPVmeus",
        "outputId": "b20ba789-e9b5-4429-8796-fb7d06ebe1d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f698695dd90>"
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(data=df_count,x=\"Emotions\",y=\"EmotionCount\",hue=\"Source\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
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      "language": "python",
      "name": "python3"
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