Advanced_Solution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ECS7020P_miniproject_advanced.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Author"
      ],
      "metadata": {
        "id": "BceETXxKp70d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Student Name:** Daniel Elston\n",
        "\n",
        "**Student Number:** 210720147"
      ],
      "metadata": {
        "id": "XD7yWlfKp7qF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Problem Formulation\n",
        "\n",
        "A large problem with a lot of data collected is background noise. The same applies to audio recordings. Background noises such as other people talking, a cars engine or even electrical noise caused by having your laptop charging while you record an audio. Removing this background noise improves results and makes analysis more clear. This advanced solution is aimed at identifying background noise. \n",
        "\n",
        "The background noise itself is closely related to silence for audio files such the ones in use here. Silence could be useful in audio recognition, for example matching periods of silence could help classify an audio signal. For this reason, the advanced solutions is aimed at identifying background noise and silence for use in any further research."
      ],
      "metadata": {
        "id": "SY9FgOkGJm0M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "import librosa.display\n",
        "import math\n",
        "import soundfile as sf\n",
        "import matplotlib.style as ms\n",
        "from tqdm import tqdm\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, precision_score, recall_score\n",
        "from xgboost import XGBClassifier\n",
        "import xgboost as xgb\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "\n",
        "import os, sys, re, pickle, glob\n",
        "import urllib.request\n",
        "import zipfile\n",
        "\n",
        "import IPython.display as ipd\n",
        "from tqdm import tqdm\n",
        "import librosa\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "g5zF2O1jwOE6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e66e9ea-3b8f-48d2-d148-b587a8abdee9"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data is loaded in. For this problem, a dataset consisting of many WAV files, each with either a hum or a whistle to the Harry Potter theme song or the Star Wars theme song."
      ],
      "metadata": {
        "id": "6LO-d2AvKcbx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pathall = '/content/drive/MyDrive/Data/MLEndHW_pub/AllAudio'"
      ],
      "metadata": {
        "id": "5WwWfdIMvnsk"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_pathall = '/content/drive/MyDrive/Data/MLEndHW_pub/AllAudio/*.wav'\n",
        "files_all = glob.glob(sample_pathall)\n",
        "\n",
        "len(files_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ClsHj_lSvt65",
        "outputId": "d4a2f211-ea8f-46e3-9e51-72d97f131691"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "747"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Machine Learning Pipeline\n",
        "\n",
        "The machine learning pipeline below consists of audio files as an input with transformations in the form of audio feature extraction. The feature extracted for this solution is soley audio silence. Machine learning models are trained with audio silence and executed. The output is a classification of song recogonition."
      ],
      "metadata": {
        "id": "0cwd-RjBeVbk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Transformation Stage\n",
        "\n",
        "The input for this model is a dataset containing 747 audio files. There are 4 types of file, each file is either a student humming or whistling to either the Harry Potter or Star Wars theme song. The feature extracted for this solution is audio silence, using the function below."
      ],
      "metadata": {
        "id": "E7nQxKIUf2T4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def getXy_sil(files,labels_file, scale_audio=False, onlySingleDigit=False):\n",
        "  X,y =[],[]\n",
        "  for file in tqdm(files):\n",
        "    fileID = file.split('/')[-1]\n",
        "    file_name = file.split('/')[-1]\n",
        "    yi = labels_file.loc[fileID]['song']=='Potter'\n",
        "\n",
        "    fs = None\n",
        "    x, fs = librosa.load(file,sr=fs)\n",
        "    rmse = librosa.feature.rms(x + 0.0001)[0]\n",
        "    \n",
        "    silence = 0\n",
        "    for e in rmse:\n",
        "        if e <= 0.4 * np.mean(rmse):\n",
        "            silence += 1\n",
        "    sil = silence/float(len(rmse))\n",
        "\n",
        "    xi = [sil]\n",
        "    X.append(xi)\n",
        "    y.append(yi)\n",
        "\n",
        "  return np.array(X), np.array(y)"
      ],
      "metadata": {
        "id": "7fyjPj7jOYRA"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each feature extracted was saved as a seperate text file and loaded in from the same filepath as the rest of the data. This saves computing power. Further features extracted in the basic solution have also been loaded as they may be useful in identifying silence."
      ],
      "metadata": {
        "id": "idBbVrrOhZ99"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/Data/MLEndHW_pub/f_og.txt', 'rb') as filepath:\n",
        "  f_og=pickle.load(filepath)"
      ],
      "metadata": {
        "id": "ohrDjh0F2tLB"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/Data/MLEndHW_pub/f_sil.txt', 'rb') as filepath:\n",
        "  f_sil=pickle.load(filepath)"
      ],
      "metadata": {
        "id": "yrdjdGMVg4AA"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each feature is added to a dataframe for easy visualisation of resulting features extracted."
      ],
      "metadata": {
        "id": "_6G1UQkQhp5Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Modelling\n",
        "\n",
        "The SVM and RandomForestClassifier (RFC) from Sklearn will be used to train our model and execute the results over all audio files. Using extracted features, machine learning models are trained and executed. \n",
        "\n",
        "SVM is a support vector machine that seperates the data based on a regression line that fits the data the best. Once this regression has taken place, the SVM classifies each data point based on which side of the line it lies on.\n",
        "\n",
        "The RFC constructs many decision trees tasked with classifying each audio feature. Many iterations of the classification algorithm take place, then the RFC takes an average classification as the result.\n",
        "\n",
        "The XGBClassifier allows the dataset to be treated as a model and therefore enables it to be classified."
      ],
      "metadata": {
        "id": "F3wE4s7CjPx-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Methodology\n",
        "\n",
        "The audio silence extracted will now be passed through various modules in order to train our model. Validation sets of the data extracted will be used to test the model and produce a result of how well the model classifies the data for each song type.\n",
        "\n",
        "Model performances will be validated using metrics such as their accuracy, f1 scores, recall scores, precision scores and finally, their confusion matrices. Model Accuracy, is the most important for this report, as it will tell us how often the classifier is correct in identifying the song name. The f1 score is the mean ratio of the models performance using precision and recall. \n",
        "\n",
        "Confusion matrices are shown to visualise the target classification predicted by the model compared to the actual classification. "
      ],
      "metadata": {
        "id": "T8cbwCaepCQB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. Dataset\n",
        "\n",
        "The dataset being used to train each model is that of the audio files features that have been extracted. In this case, the audio silence feature is used.\n",
        "\n",
        "Two random files are loaded and their paths are shown."
      ],
      "metadata": {
        "id": "NIbgjRPhp1ls"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n=4\n",
        "m=5\n",
        "fs = None # Sampling frequency. If None, fs would be 22050\n",
        "x, fs = librosa.load(files_all[n],sr=fs)\n",
        "x2, fs2 = librosa.load(files_all[m],sr=fs)\n",
        "t = np.arange(len(x))/fs\n",
        "\n",
        "print(files_all[n])\n",
        "print(files_all[m])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c24dcc13-9ec3-4574-df6e-06ca462a63e0",
        "id": "LoCEp_awuP2e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Data/MLEndHW_pub/AllAudio/S5_hum_1_Potter.wav\n",
            "/content/drive/MyDrive/Data/MLEndHW_pub/AllAudio/S4_hum_1_Potter.wav\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is an example of the raw data we are working with. The files shown below are randomly chosen."
      ],
      "metadata": {
        "id": "N91f5LnusrIX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,4))\n",
        "librosa.display.waveplot(x, max_sr=1000, alpha=0.25, color='r')\n",
        "plt.ylabel('Amplitude')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "wnYohpGjs0Qa",
        "outputId": "bea07eb4-2d17-4593-a567-a1e3d432453f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,4))\n",
        "librosa.display.waveplot(x2, max_sr=1000, alpha=0.25, color='b')\n",
        "plt.ylabel('Amplitude')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "mWdpbZqLs2T0",
        "outputId": "85cc823a-1ef1-4f0d-a55b-a4fa627dfcd5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,4))\n",
        "librosa.display.waveplot(x, max_sr=1000, alpha=0.25, color='r')\n",
        "librosa.display.waveplot(x2, max_sr=1000, alpha=0.25, color='b')\n",
        "plt.ylabel('Amplitude')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "20bf280d-fd63-44d6-94b3-d80e5be754d4",
        "id": "uQNWThphuP2p"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code below retreives all files by name."
      ],
      "metadata": {
        "id": "AUN6-Uv7jEbD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_table = [] \n",
        "\n",
        "for file in files_all:\n",
        "  file_name = file.split('/')[-1]\n",
        "  participant_ID = file.split('/')[-1].split('_')[0]\n",
        "  interpretation_type = file.split('/')[-1].split('_')[1]\n",
        "  interpretation_number = file.split('/')[-1].split('_')[2]\n",
        "  song = file.split('/')[-1].split('_')[3].split('.')[0]\n",
        "  all_table.append([file_name,participant_ID,interpretation_type,interpretation_number, song])"
      ],
      "metadata": {
        "id": "ZyWEybG7vv_i"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The files are put into a dataframe to easily see their titles and details."
      ],
      "metadata": {
        "id": "voS7GfH4jL2e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_df = pd.DataFrame(all_table,columns=['file_id','participant','interpretation','number','song']).set_index('file_id') \n",
        "all_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "AkbBRbGTv3hh",
        "outputId": "71306e47-d187-41a3-e883-0726450197c4"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>participant</th>\n",
              "      <th>interpretation</th>\n",
              "      <th>number</th>\n",
              "      <th>song</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>file_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>S1_hum_2_Potter.wav</th>\n",
              "      <td>S1</td>\n",
              "      <td>hum</td>\n",
              "      <td>2</td>\n",
              "      <td>Potter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S2_whistle_2_Potter.wav</th>\n",
              "      <td>S2</td>\n",
              "      <td>whistle</td>\n",
              "      <td>2</td>\n",
              "      <td>Potter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S5_hum_2_Potter.wav</th>\n",
              "      <td>S5</td>\n",
              "      <td>hum</td>\n",
              "      <td>2</td>\n",
              "      <td>Potter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S3_hum_1_Potter.wav</th>\n",
              "      <td>S3</td>\n",
              "      <td>hum</td>\n",
              "      <td>1</td>\n",
              "      <td>Potter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S5_hum_1_Potter.wav</th>\n",
              "      <td>S5</td>\n",
              "      <td>hum</td>\n",
              "      <td>1</td>\n",
              "      <td>Potter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S222_hum_2_StarWars.wav</th>\n",
              "      <td>S222</td>\n",
              "      <td>hum</td>\n",
              "      <td>2</td>\n",
              "      <td>StarWars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S219_hum_3_StarWars.wav</th>\n",
              "      <td>S219</td>\n",
              "      <td>hum</td>\n",
              "      <td>3</td>\n",
              "      <td>StarWars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S218_whistle_2_StarWars.wav</th>\n",
              "      <td>S218</td>\n",
              "      <td>whistle</td>\n",
              "      <td>2</td>\n",
              "      <td>StarWars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S221_hum_1_StarWars.wav</th>\n",
              "      <td>S221</td>\n",
              "      <td>hum</td>\n",
              "      <td>1</td>\n",
              "      <td>StarWars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S222_whistle_2_StarWars.wav</th>\n",
              "      <td>S222</td>\n",
              "      <td>whistle</td>\n",
              "      <td>2</td>\n",
              "      <td>StarWars</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>747 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                            participant interpretation number      song\n",
              "file_id                                                                \n",
              "S1_hum_2_Potter.wav                  S1            hum      2    Potter\n",
              "S2_whistle_2_Potter.wav              S2        whistle      2    Potter\n",
              "S5_hum_2_Potter.wav                  S5            hum      2    Potter\n",
              "S3_hum_1_Potter.wav                  S3            hum      1    Potter\n",
              "S5_hum_1_Potter.wav                  S5            hum      1    Potter\n",
              "...                                 ...            ...    ...       ...\n",
              "S222_hum_2_StarWars.wav            S222            hum      2  StarWars\n",
              "S219_hum_3_StarWars.wav            S219            hum      3  StarWars\n",
              "S218_whistle_2_StarWars.wav        S218        whistle      2  StarWars\n",
              "S221_hum_1_StarWars.wav            S221            hum      1  StarWars\n",
              "S222_whistle_2_StarWars.wav        S222        whistle      2  StarWars\n",
              "\n",
              "[747 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The audio silence and file labels are put into a dataframe. Each file is either a Potter file (True) or a Starwars file (False)."
      ],
      "metadata": {
        "id": "23rVuoaXjP75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = f_sil[0]\n",
        "df_sil = pd.DataFrame(x, columns=['Silence'])"
      ],
      "metadata": {
        "id": "8bLXwlSvrIyN"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z = f_og\n",
        "labels = z[-1]"
      ],
      "metadata": {
        "id": "TBIwhtnvrIyN"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is the resultant audio silence dataframe."
      ],
      "metadata": {
        "id": "VM4GTG-Zjt-Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_sil.assign(Labels = labels)\n",
        "display(df.head())\n",
        "print(df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "6c540896-bbd0-4c95-d86c-b15b282f556b",
        "id": "KsRHD0zRrIyO"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Silence</th>\n",
              "      <th>Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.450210</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.368598</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.373518</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.117468</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.606156</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Silence  Labels\n",
              "0  0.450210    True\n",
              "1  0.368598    True\n",
              "2  0.373518    True\n",
              "3  0.117468    True\n",
              "4  0.606156    True"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(747, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8. Results"
      ],
      "metadata": {
        "id": "Wj5dRcilrp3M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results of the model and analysis are shown below. To start, the audio silence is normalised."
      ],
      "metadata": {
        "id": "AI2WyJpRkUkz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "testy = df['Labels'].to_numpy()\n",
        "xtest2 = df[['Silence']].to_numpy()\n",
        "scaler.fit(xtest2)\n",
        "xte = scaler.transform(xtest2)"
      ],
      "metadata": {
        "id": "tIJ-O2PjUuxf"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset is split into training and validation sets. Each training set will be used to train the model, then the validation sets will be used to test how well the model fits the data."
      ],
      "metadata": {
        "id": "Facw8EFlj9UT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(xte,testy,test_size=0.25)\n",
        "X_train.shape, X_val.shape, y_train.shape, y_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y54imVapwr9m",
        "outputId": "75284c6d-fd50-4c1d-9a9d-cacbad4de63a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((560, 1), (187, 1), (560,), (187,))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the SVM module, the dataset is now trained and executed."
      ],
      "metadata": {
        "id": "MPEUU8zZkEgm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model  = svm.SVC(C=1)\n",
        "model.fit(X_train,y_train)\n",
        "\n",
        "yt_p = model.predict(X_train)\n",
        "yv_p = model.predict(X_val)\n",
        "\n",
        "print('The training data metrics are:')\n",
        "print('Accuracy Score:', accuracy_score(y_true=y_train, y_pred=model.predict(X_train)))\n",
        "print('f1 Score:', f1_score(y_true=y_train, y_pred=model.predict(X_train)))\n",
        "print('Recall Score:', recall_score(y_true=y_train, y_pred=model.predict(X_train)))\n",
        "print('Precision Score:', precision_score(y_true=y_train, y_pred=model.predict(X_train)))\n",
        "print('\\n')\n",
        "print('The validation data metrics are:')\n",
        "print('Accuracy Score:', accuracy_score(y_true=y_val, y_pred=model.predict(X_val)))\n",
        "print('f1 Score:', f1_score(y_true=y_val, y_pred=model.predict(X_val)))\n",
        "print('Recall Score:', recall_score(y_true=y_val, y_pred=model.predict(X_val)))\n",
        "print('Precision Score:', precision_score(y_true=y_val, y_pred=model.predict(X_val)))\n",
        "print('\\n')\n",
        "print('The support vectors are', model.support_vectors_.shape, ('\\n'))\n",
        "\n",
        "tcm = confusion_matrix(y_true=y_train, y_pred=model.predict(X_train))\n",
        "tcm_n = tcm.astype('float') / tcm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "vcm = confusion_matrix(y_true=y_val, y_pred=model.predict(X_val))\n",
        "vcm_n = vcm.astype('float') / vcm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "print('Training confusion matrix:\\n {}\\n'.format(tcm))\n",
        "print('Validation confusion matrix:\\n {}\\n'.format(vcm))\n",
        "\n",
        "print('Normalised training confusion matrix:\\n {}\\n'.format(tcm_n))\n",
        "print('Normalised validation confusion matrix:\\n {}\\n'.format(vcm_n))"
      ],
      "metadata": {
        "id": "fWlYIcpWwuon",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d6871bc-f95f-4355-df3b-926cf3274713"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The training data metrics are:\n",
            "Accuracy Score: 0.5535714285714286\n",
            "f1 Score: 0.31693989071038253\n",
            "Recall Score: 0.21481481481481482\n",
            "Precision Score: 0.6041666666666666\n",
            "\n",
            "\n",
            "The validation data metrics are:\n",
            "Accuracy Score: 0.47593582887700536\n",
            "f1 Score: 0.2794117647058823\n",
            "Recall Score: 0.1958762886597938\n",
            "Precision Score: 0.48717948717948717\n",
            "\n",
            "\n",
            "The support vectors are (517, 1) \n",
            "\n",
            "Training confusion matrix:\n",
            " [[252  38]\n",
            " [212  58]]\n",
            "\n",
            "Validation confusion matrix:\n",
            " [[70 20]\n",
            " [78 19]]\n",
            "\n",
            "Normalised training confusion matrix:\n",
            " [[0.86896552 0.13103448]\n",
            " [0.78518519 0.21481481]]\n",
            "\n",
            "Normalised validation confusion matrix:\n",
            " [[0.77777778 0.22222222]\n",
            " [0.80412371 0.19587629]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results of the SVM classifier on accuracy of the training dataset are poor. Around 53% of the time it returns the correct label. The results of accuracy on the validation  are even worse, only returning the correct label approximately 51% of the time. All metrics follow a similar pattern. In deployment, this alogrithm would not work.\n",
        "\n",
        "The results are poor again showing great uncertainty in our models decisions."
      ],
      "metadata": {
        "id": "1IjpCtWxuf5h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the RFC, the dataset is now trained and executed."
      ],
      "metadata": {
        "id": "uFx0kkFctZbI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.datasets import make_classification\n",
        "clf = RandomForestClassifier(n_estimators=1000, min_samples_split=300)\n",
        "\n",
        "clf.fit(X_train, y_train)\n",
        "y_pred=clf.predict(X_val)\n",
        "\n",
        "print('The training data metrics are:')\n",
        "print('Accuracy Score:', accuracy_score(y_true=y_train, y_pred=clf.predict(X_train)))\n",
        "print('f1 Score:', f1_score(y_true=y_train, y_pred=clf.predict(X_train)))\n",
        "print('Recall Score:', recall_score(y_true=y_train, y_pred=clf.predict(X_train)))\n",
        "print('Precision Score:', precision_score(y_true=y_train, y_pred=clf.predict(X_train)))\n",
        "print('\\n')\n",
        "print('The validation data metrics are:')\n",
        "print('Accuracy Score:', accuracy_score(y_true=y_val, y_pred=clf.predict(X_val)))\n",
        "print('f1 Score:', f1_score(y_true=y_val, y_pred=clf.predict(X_val)))\n",
        "print('Recall Score:', recall_score(y_true=y_val, y_pred=clf.predict(X_val)))\n",
        "print('Precision Score:', precision_score(y_true=y_val, y_pred=clf.predict(X_val)))\n",
        "print('\\n')\n",
        "print('The support vectors are', model.support_vectors_.shape, ('\\n'))\n",
        "\n",
        "tcm = confusion_matrix(y_true=y_train, y_pred=clf.predict(X_train))\n",
        "tcm_n = tcm.astype('float') / tcm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "vcm = confusion_matrix(y_true=y_val, y_pred=clf.predict(X_val))\n",
        "vcm_n = vcm.astype('float') / vcm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "print('Training confusion matrix:\\n {}\\n'.format(tcm))\n",
        "print('Validation confusion matrix:\\n {}\\n'.format(vcm))\n",
        "\n",
        "print('Normalised training confusion matrix:\\n {}\\n'.format(tcm_n))\n",
        "print('Normalised validation confusion matrix:\\n {}\\n'.format(vcm_n))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEy-mmh5ne49",
        "outputId": "14679944-549c-482b-d6c2-b451f1295a60"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The training data metrics are:\n",
            "Accuracy Score: 0.5678571428571428\n",
            "f1 Score: 0.3730569948186528\n",
            "Recall Score: 0.26666666666666666\n",
            "Precision Score: 0.6206896551724138\n",
            "\n",
            "\n",
            "The validation data metrics are:\n",
            "Accuracy Score: 0.47593582887700536\n",
            "f1 Score: 0.3194444444444444\n",
            "Recall Score: 0.23711340206185566\n",
            "Precision Score: 0.48936170212765956\n",
            "\n",
            "\n",
            "The support vectors are (517, 1) \n",
            "\n",
            "Training confusion matrix:\n",
            " [[246  44]\n",
            " [198  72]]\n",
            "\n",
            "Validation confusion matrix:\n",
            " [[66 24]\n",
            " [74 23]]\n",
            "\n",
            "Normalised training confusion matrix:\n",
            " [[0.84827586 0.15172414]\n",
            " [0.73333333 0.26666667]]\n",
            "\n",
            "Normalised validation confusion matrix:\n",
            " [[0.73333333 0.26666667]\n",
            " [0.7628866  0.2371134 ]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results of the RFC on accuracy of the training dataset are poor once again. Around 58% of the time it returns the correct label. The results of accuracy on the validation  are even worse, only returning the correct label approximately 51% of the time. All metrics follow a similar pattern. In deployment, this alogrithm would not work.\n",
        "\n",
        "The results of the matrices are poor again showing great uncertainty in the models decisions."
      ],
      "metadata": {
        "id": "wBpgDlnSmISj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgbc = XGBClassifier()\n",
        "XGBClassifier(base_score=1, objective='multi:softprob') \n",
        "\n",
        "xgbc.fit(X_train, y_train)\n",
        "\n",
        "print('The training data metrics are:')\n",
        "print('Accuracy Score:', accuracy_score(y_true=y_train, y_pred=xgbc.predict(X_train)))\n",
        "print('f1 Score:', f1_score(y_true=y_train, y_pred=xgbc.predict(X_train)))\n",
        "print('Recall Score:', recall_score(y_true=y_train, y_pred=xgbc.predict(X_train)))\n",
        "print('Precision Score:', precision_score(y_true=y_train, y_pred=xgbc.predict(X_train)))\n",
        "print('\\n')\n",
        "print('The validation data metrics are:')\n",
        "print('Accuracy Score:', accuracy_score(y_true=y_val, y_pred=xgbc.predict(X_val)))\n",
        "print('f1 Score:', f1_score(y_true=y_val, y_pred=xgbc.predict(X_val)))\n",
        "print('Recall Score:', recall_score(y_true=y_val, y_pred=xgbc.predict(X_val)))\n",
        "print('Precision Score:', precision_score(y_true=y_val, y_pred=xgbc.predict(X_val)))\n",
        "print('\\n')\n",
        "print('The support vectors are', model.support_vectors_.shape, ('\\n'))\n",
        "\n",
        "tcm = confusion_matrix(y_true=y_train, y_pred=xgbc.predict(X_train))\n",
        "tcm_n = tcm.astype('float') / tcm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "vcm = confusion_matrix(y_true=y_val, y_pred=xgbc.predict(X_val))\n",
        "vcm_n = vcm.astype('float') / vcm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "print('Training confusion matrix:\\n {}\\n'.format(tcm))\n",
        "print('Validation confusion matrix:\\n {}\\n'.format(vcm))\n",
        "\n",
        "print('Normalised training confusion matrix:\\n {}\\n'.format(tcm_n))\n",
        "print('Normalised validation confusion matrix:\\n {}\\n'.format(vcm_n))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0zAi370L17Qb",
        "outputId": "1d209867-1c46-4c9c-8a44-8e05258202b2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The training data metrics are:\n",
            "Accuracy Score: 0.6732142857142858\n",
            "f1 Score: 0.5924276169265034\n",
            "Recall Score: 0.4925925925925926\n",
            "Precision Score: 0.7430167597765364\n",
            "\n",
            "\n",
            "The validation data metrics are:\n",
            "Accuracy Score: 0.48128342245989303\n",
            "f1 Score: 0.37419354838709673\n",
            "Recall Score: 0.29896907216494845\n",
            "Precision Score: 0.5\n",
            "\n",
            "\n",
            "The support vectors are (517, 1) \n",
            "\n",
            "Training confusion matrix:\n",
            " [[244  46]\n",
            " [137 133]]\n",
            "\n",
            "Validation confusion matrix:\n",
            " [[61 29]\n",
            " [68 29]]\n",
            "\n",
            "Normalised training confusion matrix:\n",
            " [[0.84137931 0.15862069]\n",
            " [0.50740741 0.49259259]]\n",
            "\n",
            "Normalised validation confusion matrix:\n",
            " [[0.67777778 0.32222222]\n",
            " [0.70103093 0.29896907]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results of the RFC classifier on accuracy of the training dataset are poor. Around 69% of the time it returns the correct label. The results of accuracy on the validation are worse again, returning the correct label approximately 50% of the time. In deployment, again, this alogrithm would not work.\n",
        "\n",
        "The confusion matrices show a similar story. The model has struggled to classify the audio files.\n"
      ],
      "metadata": {
        "id": "pf1SsGrKyzeX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 9. Conclusion\n",
        "\n",
        "The aim of this machine learning pipeline was to classify audio files based on their audio silence. In theory, this would provide an accurate way of classifying audio files. The audio silence seemed to be an important feature. There would likely be a great difference in the amount of silence each song type had and thus would allow for easy classification. However the result was far from expected and the algorithm did not work as intended, the reasons for which I can only assume were down to human error. \n",
        "\n",
        "All classification models failed to provide any reliable results. At the very least, this pipeline could be used to eliminate low volume background noise in areas of further research. "
      ],
      "metadata": {
        "id": "LGUJEpD6xIX6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2lD__k_I2aTC"
      },
      "execution_count": 20,
      "outputs": []
    }
  ]
}