Noise_test.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/marcello-grati/capstone_L01_artificial_music_detection/blob/main/Copy_of_functionized_capstone.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vk8Y9s0-nNAx"
      },
      "outputs": [],
      "source": [
        "# import required libraries\n",
        "import numpy as np\n",
        "import librosa.display\n",
        "import soundfile\n",
        "import random\n",
        "import os, glob\n",
        "import math\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import Normalize\n",
        "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score, matthews_corrcoef"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYBTAozjh24k",
        "outputId": "c24e54fb-617e-40a9-83c7-3fca3668cfbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# import dataset from google drive folder\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VEvJWPwRR2II"
      },
      "outputs": [],
      "source": [
        "# Definition of the paths for the datsets\n",
        "\n",
        "# Fake data sets paths\n",
        "pathRiffusionDataset  = '/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion'\n",
        "pathJSFCDataset  = '/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale'\n",
        "pathLADataset = '/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer'\n",
        "pathRNNDataset = '/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC'\n",
        "\n",
        "# Real data sets paths\n",
        "pathMusicnetDataset = '/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet'\n",
        "pathMaestroDataset = '/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vQuR9InpXqvF"
      },
      "outputs": [],
      "source": [
        "# Global variables\n",
        "\n",
        "sr = 16000      # sample rate\n",
        "n_mels = 128    # number of Mel bands to generate\n",
        "\n",
        "# ids for the datasets. Basically, these are prefixes for the files in datasets.\n",
        "# fake datasets start with \"01\"\n",
        "# real datasets start with \"00\"\n",
        "\n",
        "riffusion_id = '01_riffusion_'\n",
        "js_fake_chorale_id = '01_JsFakeChorale_'\n",
        "la_composer_id = '01_LA_composer_'\n",
        "rnn_id = '01_RNN_'\n",
        "\n",
        "musicnet_id = '00_musicnet_'\n",
        "maestro_id = '00_maestro_'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TqrqL7xopzXc"
      },
      "outputs": [],
      "source": [
        "# Given a signal and desired SNR, this gives the required AWGN which should be added to the signal to get the desired SNR\n",
        "# Arguments:\n",
        "# signal - signal which will be mixed with noise\n",
        "# SNR - desired SNR in dB\n",
        "# Return value: noise\n",
        "\n",
        "def get_white_noise(signal,SNR) :\n",
        "  #RMS value of signal\n",
        "  RMS_s=math.sqrt(np.mean(signal**2))\n",
        "  #RMS values of noise\n",
        "  RMS_n=math.sqrt(RMS_s**2/(pow(10,SNR/10)))\n",
        "  #Additive white gausian noise. Thereore mean=0\n",
        "  #Because sample length is large (typically > 40000)\n",
        "  #we can use the population formula for standard deviation.\n",
        "  #because mean=0 STD=RMS\n",
        "  STD_n=RMS_n\n",
        "  noise=np.random.normal(0, STD_n, signal.shape[0])\n",
        "  return noise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EPcTmwWyEW8T"
      },
      "outputs": [],
      "source": [
        "# Extracting specified by <feature_type> audio features\n",
        "# Arguments:\n",
        "# <file> - input audio file\n",
        "# <sample_rate> - parameters used for computing some audio features\n",
        "# <noisy> - True if needed to add noise\n",
        "# <snr> - specify snr if noise is adding\n",
        "# Return value: matrix of features\n",
        "\n",
        "def get_features(file, sample_rate, feature_type, noisy, snr) :\n",
        "    # load an individual soundfile\n",
        "     with soundfile.SoundFile(file) as audio:\n",
        "        waveform = audio.read(dtype=\"float32\")\n",
        "\n",
        "        if noisy :\n",
        "          noise = get_white_noise(waveform, snr)\n",
        "          waveform = waveform + noise\n",
        "\n",
        "        mean = np.mean(waveform, axis=0)\n",
        "        std = np.std(waveform, axis=0)\n",
        "\n",
        "        # add an epsilon term to the standard deviation to avoid division by zero\n",
        "        epsilon = 1e-8\n",
        "        std = np.where(std == 0, epsilon, std)\n",
        "\n",
        "        waveform = (waveform - mean) / std\n",
        "\n",
        "        if feature_type == \"mel\" :\n",
        "          return librosa.amplitude_to_db(librosa.feature.melspectrogram(y=waveform, sr=sample_rate, n_mels=n_mels, fmax=sr/2))\n",
        "\n",
        "        elif feature_type == \"mfc\" :\n",
        "          return librosa.amplitude_to_db(librosa.feature.mfcc(y=waveform, sr=sr, n_mfcc=n_mels))\n",
        "\n",
        "        elif feature_type == \"ber\":\n",
        "          return librosa.amplitude_to_db(librosa.feature.spectral_contrast(y=waveform, sr=sample_rate))\n",
        "\n",
        "        elif feature_type == \"stft\":\n",
        "          return librosa.amplitude_to_db(np.abs(librosa.stft(y=waveform)),ref=np.max)\n",
        "\n",
        "        else :\n",
        "          print(\"Wrong feature type\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iA84hPlcIu_r"
      },
      "outputs": [],
      "source": [
        "# Labels\n",
        "# For our specific task we have to define 2 labels: real or fake\n",
        "\n",
        "emotions = {\n",
        "    'real':'Real',\n",
        "    'fake':'Fake'\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OstsKDeEMWIu"
      },
      "outputs": [],
      "source": [
        "# Detect the label from the filename\n",
        "# filename is a real audio if starts with '00'\n",
        "# filename is a fake audio if starts with '01'\n",
        "def parse_string(input_string):\n",
        "  a = None\n",
        "  parts = input_string.split('_')\n",
        "  a = parts[0]\n",
        "\n",
        "  if(a=='00'):\n",
        "    return \"real\"\n",
        "  elif(a=='01'):\n",
        "    return \"fake\"\n",
        "  else:\n",
        "    return None\n",
        "\n",
        "#Function that takes n unrepeated numbers from 0 to Nmax and return an array of them\n",
        "\n",
        "def random_int_array(n, Nmax):\n",
        "  if n > Nmax:\n",
        "    raise ValueError(\"n must be less than or equal to Nmax\")\n",
        "  integers = random.sample(range(Nmax), n)\n",
        "  return np.array(integers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K1lyaEp2iZuS"
      },
      "outputs": [],
      "source": [
        "# Takes audio data from files in datasets and extract their audio features.\n",
        "# Arguments:\n",
        "#  <num_vector> - list of numbers of audio files per each dataset\n",
        "#  <datasets_vector> - paths to datasets\n",
        "#  <feature_type> - desired feature to extract\n",
        "#  <noisy> - if noise should be added\n",
        "#  <snr> - desired snr, if noise is added\n",
        "# Return values:\n",
        "#  <X> - feature matrix\n",
        "#  <y> - labels vector\n",
        "#  <z> - files names vector\n",
        "\n",
        "def load_data_balanced(num_vector, datasets_vector, feature_type, noisy=False, snr=-20):\n",
        "\n",
        "  if (len(num_vector) != len(datasets_vector)) :\n",
        "    print(\"wrong number of parameters\")\n",
        "    return None\n",
        "\n",
        "  X, y, z = [],[],[]\n",
        "\n",
        "  #For all the datasets\n",
        "  for i in range(len(num_vector)):\n",
        "    count = 0\n",
        "    print(datasets_vector[i])\n",
        "\n",
        "    songs_list = os.listdir(datasets_vector[i])\n",
        "    #Count the number of songs into the dataset\n",
        "    songs_count = len(songs_list)\n",
        "\n",
        "    #Select random number of songs in the dataset\n",
        "\n",
        "    print(\"Number of audio files: \"+str(songs_count))\n",
        "    chosen_files = random_int_array(num_vector[i], songs_count)\n",
        "\n",
        "    #For all the file in the dataset\n",
        "\n",
        "    for k in chosen_files :\n",
        "\n",
        "      features = get_features(datasets_vector[i] + '/' + songs_list[k], sr, feature_type, noisy, snr)\n",
        "      emotion = parse_string(songs_list[k])\n",
        "\n",
        "      X.append(features)\n",
        "      y.append(emotion)\n",
        "      z.append(songs_list[k])\n",
        "\n",
        "      count += 1\n",
        "      print('\\r' + f' Processed {count}/{num_vector[i]} audio samples',end=' ')\n",
        "\n",
        "\n",
        "    print(\"\\n-----------------------------------------------------------------------------------------------------------\")\n",
        "\n",
        "  # Return arrays to plug into sklearn's cross-validation algorithms\n",
        "  print(\"DONE!!!\")\n",
        "  return X, y, z"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kbwZT-_4jLwt"
      },
      "outputs": [],
      "source": [
        "# <num_samples_vector> defines number of audio files per datasets (number of real and fake files must be equal)\n",
        "\n",
        "open_test_dim = 200\n",
        "\n",
        "num_samples_vector = [open_test_dim,\n",
        "                      open_test_dim,\n",
        "                      open_test_dim,\n",
        "                      open_test_dim,\n",
        "                      3 * open_test_dim,\n",
        "                      3 * open_test_dim,]\n",
        "\n",
        "# define path for chosen datasets\n",
        "\n",
        "datasets_vector = [pathRiffusionDataset,\n",
        "                   pathJSFCDataset,\n",
        "                   pathLADataset,\n",
        "                   pathRNNDataset,\n",
        "                   pathMusicnetDataset,\n",
        "                   pathMaestroDataset]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "und9LfbMvYn6"
      },
      "outputs": [],
      "source": [
        "# adjust shape of the songs by truncating or zero-padding, if needed\n",
        "\n",
        "def adjustFeatureArrays(X, y, z, bins_number):\n",
        "  counter = 0\n",
        "\n",
        "  for index in range(len(X)) :\n",
        "    if X[index].shape[1] != bins_number :\n",
        "      # print(f\"index = {index}, shape = {X[index].shape},  {z[index]}\")\n",
        "      counter += 1\n",
        "\n",
        "      if (X[index].shape[1] < bins_number) :\n",
        "        X[index] = np.pad(X[index], [(0,0), (0, bins_number - X[index].shape[1])])\n",
        "      X[index] = X[index][:,:bins_number]\n",
        "\n",
        "  print(f\"\\nnumber out of shape songs = {counter}\")\n",
        "\n",
        "  X = np.array(X)\n",
        "  y = np.array(y)\n",
        "  z = np.array(z)\n",
        "\n",
        "  return (X, y, z)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y9zSUPVf_LNr"
      },
      "outputs": [],
      "source": [
        "# apply PCA to extracted features\n",
        "# Arguments:\n",
        "# <numComponents> - number of PCA components\n",
        "# <X_flattened> - input array of features\n",
        "# <needPlot> - set to True if you want to see the plot\n",
        "# Return values:\n",
        "# <X_pca> - array of PCA components\n",
        "\n",
        "def applyPCA(numComponents, X_flattened, needPlot = False):\n",
        "  pca = PCA(n_components=numComponents)\n",
        "  X_pca = pca.fit_transform(X_flattened)\n",
        "\n",
        "  if needPlot:\n",
        "        exp_var = pca.explained_variance_ratio_ * 100\n",
        "        cum_exp_var = np.cumsum(exp_var)\n",
        "\n",
        "        plt.bar(range(1, numComponents+1), exp_var, align='center',\n",
        "          label='Individual explained variance')\n",
        "\n",
        "        plt.step(range(1, numComponents+1), cum_exp_var, where='mid',\n",
        "          label='Cumulative explained variance', color='red')\n",
        "\n",
        "        plt.ylabel('Explained variance percentage')\n",
        "        plt.xlabel('Principal component index')\n",
        "        plt.xticks(ticks=list(range(0, numComponents+1, 50)))\n",
        "        plt.legend(loc='best')\n",
        "        plt.tight_layout()\n",
        "\n",
        "        plt.savefig(\"Barplot_PCA.png\")\n",
        "\n",
        "  return X_pca"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aKvlpL-F5jsC"
      },
      "outputs": [],
      "source": [
        "# For open test, one real and one fake datasets should be excluded from the trainig process.\n",
        "# After training the model with other datasets, these excluded datasets are going to be used for open test.\n",
        "# This function forms (X_open, y_open), datasets for open test, and (X_ready, y_ready) - for training.\n",
        "# Arguments:\n",
        "# <X_pca> - features array after applying PCA\n",
        "# <y> - labeles\n",
        "# <z> - corresponding filenames\n",
        "# <excluded_fake> - name of excluded fake dataset\n",
        "# <excluded_real> - name of excluded real dataset\n",
        "# Return values:\n",
        "# <X_ready>, <y_ready> - dataset and lables for training/close test\n",
        "# <X_open, y_open> - dataset and lables for open test\n",
        "\n",
        "def split_train_from_open(X_pca, y, z, excluded_fake, excluded_real) :\n",
        "\n",
        "  X_ready = []\n",
        "  X_open = []\n",
        "  y_ready = []\n",
        "  y_open = []\n",
        "\n",
        "  counter = 0\n",
        "\n",
        "  for index in range(len(X_pca)):\n",
        "\n",
        "    if z[index].startswith(excluded_fake) :\n",
        "      X_open.append(X_pca[index])\n",
        "      y_open.append(y[index])\n",
        "\n",
        "    elif z[index].startswith(excluded_real) :\n",
        "      if counter < open_test_dim :\n",
        "        X_open.append(X_pca[index])\n",
        "        y_open.append(y[index])\n",
        "        counter+=1\n",
        "\n",
        "    else :\n",
        "      X_ready.append(X_pca[index])\n",
        "      y_ready.append(y[index])\n",
        "\n",
        "  return np.array(X_ready), np.array(X_open), np.array(y_ready), np.array(y_open)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "9GWw9gJ07Ozn"
      },
      "outputs": [],
      "source": [
        "# Close Test\n",
        "# We experimented with different models. The most accurate is RandomForestClassifier.\n",
        "# So, here, for experiments with noise, we use only this model which is the most efficient.\n",
        "# Arguments:\n",
        "# <X_train>, <y_train> - dataset and lables for training\n",
        "# <X_test, y_test> - dataset and lables for close test\n",
        "# Return values:\n",
        "# <classification_models> - trained models\n",
        "# <results> - performance of trained models in close test\n",
        "\n",
        "def close_test(X_train, y_train, X_test, y_test) :\n",
        "\n",
        "  classification_models = [\n",
        "      #KNeighborsClassifier(),\n",
        "      #SVC(kernel='linear'),\n",
        "      #SVC(kernel='rbf'),\n",
        "      #DecisionTreeClassifier(),\n",
        "      RandomForestClassifier(), \n",
        "      #AdaBoostClassifier(),\n",
        "      #GaussianNB(), \n",
        "      #QuadraticDiscriminantAnalysis()\n",
        "      ]\n",
        "\n",
        "  scores = []\n",
        "  for model in classification_models:\n",
        "      model.fit(X_train, y_train)\n",
        "      score = model.score(X_test, y_test)\n",
        "      model_name = type(model).__name__\n",
        "      if model_name=='SVC' and model.kernel=='rbf': model_name+=' RBF kernel'\n",
        "      y_pred = model.predict(X_test)\n",
        "      scores.append((\n",
        "        model_name,\n",
        "        (f'{100*f1_score(y_test, y_pred, average=\"macro\"):.2f}%'),\n",
        "        (f'{100*score:.2f}%'),\n",
        "        (f'{100*matthews_corrcoef(y_test, y_pred):.2f}%'),\n",
        "        (f'{100*precision_score(y_test, y_pred, average=\"macro\"):.2f}%'),\n",
        "        (f'{100*recall_score(y_test, y_pred, average=\"macro\"):.2f}%'),\n",
        "        ))\n",
        "  # Make it pretty\n",
        "  scores_df = pd.DataFrame(scores,columns=['Classifier','F1 Score','Accuracy','MCC','Precision','Recall'])\n",
        "  results = scores_df.sort_values(by='F1 Score',axis=0,ascending=False)\n",
        "\n",
        "  return classification_models, results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_QZWFXto5cqu"
      },
      "outputs": [],
      "source": [
        "# Perform open test with the trained models\n",
        "# Arguments:\n",
        "# <X_open>, <y_open> - dataset and lables for open test\n",
        "# trained_models - list of trained models. In this colab we trained only RandomForest classifier.\n",
        "# Return values:\n",
        "# performance of trained models in open test\n",
        "\n",
        "def open_test(X_open, y_open, trained_models) :\n",
        "\n",
        "  open_scores = []\n",
        "\n",
        "  for model in trained_models:\n",
        "    score = model.score(X_open, y_open)\n",
        "    model_name = type(model).__name__\n",
        "    if model_name=='SVC' and model.kernel=='rbf': model_name+=' RBF kernel'\n",
        "    y_pred = model.predict(X_open)\n",
        "    open_scores.append((\n",
        "        model_name,\n",
        "        (f'{100*f1_score(y_open, y_pred, average=\"macro\"):.4}%'),\n",
        "        (f'{100*score:.2f}%'),\n",
        "        (f'{100*matthews_corrcoef(y_open, y_pred):.4}%'),\n",
        "        (f'{100*precision_score(y_open, y_pred, average=\"macro\"):.3f}%'),\n",
        "        (f'{100*recall_score(y_open, y_pred, average=\"macro\"):.3f}%'),\n",
        "        ))\n",
        "\n",
        "  # Make it pretty\n",
        "  open_scores_df = pd.DataFrame(open_scores,columns=['Classifier','F1 Score','Accuracy','MCC','Precision','Recall'])\n",
        "  return open_scores_df.sort_values(by='F1 Score',axis=0,ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AfA0vRKvOp8Y",
        "outputId": "b9a5fd0f-4125-47df-eeab-384241c596b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 12\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  -25\n"
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              "      <td>52.49%</td>\n",
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              "               Classifier F1 Score Accuracy    MCC Precision  Recall\n",
              "0  RandomForestClassifier   52.49%   52.50%  4.97%    52.49%  52.49%\n",
              "1              GaussianNB   45.66%   51.25%  4.78%    53.17%  51.80%"
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            "Open test results for SNR =  -25\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   51.47%   51.50%  3.004%   51.504%  51.500%\n",
              "1              GaussianNB   44.77%   51.25%  3.432%   52.355%  51.250%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 14\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  -22\n"
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              "                                                     [key], {});\n",
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   61.22%   61.25%  22.69%    61.38%  61.32%\n",
              "1              GaussianNB   55.83%   55.83%  11.73%    55.87%  55.86%"
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            "Open test results for SNR =  -22\n"
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              "      <th></th>\n",
              "      <th>Classifier</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>MCC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>56.2%</td>\n",
              "      <td>56.25%</td>\n",
              "      <td>12.53%</td>\n",
              "      <td>56.277%</td>\n",
              "      <td>56.250%</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>54.17%</td>\n",
              "      <td>54.50%</td>\n",
              "      <td>9.133%</td>\n",
              "      <td>54.634%</td>\n",
              "      <td>54.500%</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "1              GaussianNB    56.2%   56.25%  12.53%   56.277%  56.250%\n",
              "0  RandomForestClassifier   54.17%   54.50%  9.133%   54.634%  54.500%"
            ]
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 15\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  -20\n"
          ]
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              "      <td>72.75%</td>\n",
              "      <td>72.92%</td>\n",
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   72.75%   72.92%  46.85%    73.79%  73.07%\n",
              "1              GaussianNB   44.27%   53.75%  16.49%    65.27%  54.45%"
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          "text": [
            "Open test results for SNR =  -20\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   56.32%   59.50%  22.57%   63.411%  59.500%\n",
              "1              GaussianNB   43.58%   54.50%  18.95%   69.947%  54.500%"
            ]
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 14\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  -17\n"
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   82.91%   82.92%  65.97%    83.01%  82.96%\n",
              "1              GaussianNB   78.03%   78.33%  58.96%    80.44%  78.55%"
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            "Open test results for SNR =  -17\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   52.79%   57.25%  18.39%   61.660%  57.250%\n",
              "1              GaussianNB    47.9%   54.75%  13.79%   60.013%  54.750%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 16\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  -15\n"
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   92.08%   92.08%  84.20%    92.09%  92.10%\n",
              "1              GaussianNB   73.17%   74.58%  56.78%    82.26%  74.99%"
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Classifier</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>MCC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
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              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>55.39%</td>\n",
              "      <td>59.75%</td>\n",
              "      <td>24.98%</td>\n",
              "      <td>66.000%</td>\n",
              "      <td>59.750%</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>38.48%</td>\n",
              "      <td>51.50%</td>\n",
              "      <td>7.655%</td>\n",
              "      <td>59.766%</td>\n",
              "      <td>51.500%</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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            "text/plain": [
              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   55.39%   59.75%  24.98%   66.000%  59.750%\n",
              "1              GaussianNB   38.48%   51.50%  7.655%   59.766%  51.500%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 12\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  -10\n"
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   95.42%   95.42%  90.86%    95.43%  95.44%\n",
              "1              GaussianNB   88.69%   88.75%  78.77%    89.88%  88.89%"
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            "Open test results for SNR =  -10\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   55.01%   61.00%  32.19%   73.550%  61.000%\n",
              "1              GaussianNB   51.06%   58.50%  27.17%   71.706%  58.500%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 13\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  -5\n"
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              "      <td>RandomForestClassifier</td>\n",
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   99.17%   99.17%  98.35%    99.17%  99.18%\n",
              "1              GaussianNB   95.83%   95.83%  91.88%    95.99%  95.89%"
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            "Open test results for SNR =  -5\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   53.66%   60.00%  29.74%   72.104%  60.000%\n",
              "1              GaussianNB   47.96%   56.50%  22.17%   68.901%  56.500%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 13\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  0\n"
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              "               Classifier F1 Score Accuracy      MCC Precision   Recall\n",
              "1              GaussianNB   98.75%   98.75%   97.50%    98.76%   98.74%\n",
              "0  RandomForestClassifier  100.00%  100.00%  100.00%   100.00%  100.00%"
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            "Open test results for SNR =  0\n"
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              "      <th>Accuracy</th>\n",
              "      <th>MCC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>81.19%</td>\n",
              "      <td>81.75%</td>\n",
              "      <td>67.65%</td>\n",
              "      <td>86.040%</td>\n",
              "      <td>81.750%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>74.49%</td>\n",
              "      <td>75.25%</td>\n",
              "      <td>53.8%</td>\n",
              "      <td>78.661%</td>\n",
              "      <td>75.250%</td>\n",
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              "  </tbody>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   81.19%   81.75%  67.65%   86.040%  81.750%\n",
              "1              GaussianNB   74.49%   75.25%   53.8%   78.661%  75.250%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 17\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  2\n"
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   99.17%   99.17%  98.35%    99.19%  99.15%\n",
              "1              GaussianNB   98.33%   98.33%  96.68%    98.36%  98.32%"
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          "text": [
            "Open test results for SNR =  2\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "1              GaussianNB   67.64%   69.00%  41.66%   72.839%  69.000%\n",
              "0  RandomForestClassifier   65.27%   68.50%  46.69%   79.463%  68.500%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 10\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  4\n"
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              "      <th></th>\n",
              "      <th>Classifier</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>MCC</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>99.17%</td>\n",
              "      <td>99.17%</td>\n",
              "      <td>98.35%</td>\n",
              "      <td>99.17%</td>\n",
              "      <td>99.18%</td>\n",
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              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>98.33%</td>\n",
              "      <td>98.33%</td>\n",
              "      <td>96.72%</td>\n",
              "      <td>98.41%</td>\n",
              "      <td>98.31%</td>\n",
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              "  "
            ],
            "text/plain": [
              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "1              GaussianNB   99.17%   99.17%  98.35%    99.17%  99.18%\n",
              "0  RandomForestClassifier   98.33%   98.33%  96.72%    98.41%  98.31%"
            ]
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          "text": [
            "Open test results for SNR =  4\n"
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              "      <th>1</th>\n",
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "1              GaussianNB   76.12%   76.75%  56.57%   79.909%  76.750%\n",
              "0  RandomForestClassifier   72.73%   74.50%  56.97%   83.113%  74.500%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 13\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  6\n"
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   99.17%   99.17%  98.35%    99.17%  99.18%\n",
              "1              GaussianNB   97.08%   97.08%  94.24%    97.20%  97.05%"
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            "Open test results for SNR =  6\n"
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>78.5%</td>\n",
              "      <td>78.50%</td>\n",
              "      <td>57.03%</td>\n",
              "      <td>78.526%</td>\n",
              "      <td>78.500%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>68.4%</td>\n",
              "      <td>70.75%</td>\n",
              "      <td>49.5%</td>\n",
              "      <td>79.517%</td>\n",
              "      <td>70.750%</td>\n",
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              "  "
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            "text/plain": [
              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "1              GaussianNB    78.5%   78.50%  57.03%   78.526%  78.500%\n",
              "0  RandomForestClassifier    68.4%   70.75%   49.5%   79.517%  70.750%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 20\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  8\n"
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              "               Classifier F1 Score Accuracy      MCC Precision   Recall\n",
              "1              GaussianNB   98.33%   98.33%   96.72%    98.41%   98.31%\n",
              "0  RandomForestClassifier  100.00%  100.00%  100.00%   100.00%  100.00%"
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            "Open test results for SNR =  8\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "1              GaussianNB   81.11%   81.25%  63.48%   82.237%  81.250%\n",
              "0  RandomForestClassifier   73.54%   75.00%  56.65%   82.088%  75.000%"
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            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 12\n",
            "Flattened array shape:  (2000, 160925)\n",
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  10\n"
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              "      <th>0</th>\n",
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              "      <td>99.58%</td>\n",
              "      <td>99.58%</td>\n",
              "      <td>99.17%</td>\n",
              "      <td>99.58%</td>\n",
              "      <td>99.59%</td>\n",
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              "      <td>94.58%</td>\n",
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              "      <td>94.88%</td>\n",
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   99.58%   99.58%  99.17%    99.58%  99.59%\n",
              "1              GaussianNB   94.57%   94.58%  89.40%    94.88%  94.52%"
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            "Open test results for SNR =  10\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "1              GaussianNB   86.71%   86.75%  73.99%   87.243%  86.750%\n",
              "0  RandomForestClassifier    86.0%   86.25%  75.19%   88.988%  86.250%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 8\n",
            "Flattened array shape:  (2000, 160925)\n"
          ]
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          "text": [
            "<ipython-input-14-5499829e2956>:19: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
            "  plt.tight_layout()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  12\n"
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              "               Classifier F1 Score Accuracy      MCC Precision   Recall\n",
              "1              GaussianNB   97.50%   97.50%   95.01%    97.50%   97.51%\n",
              "0  RandomForestClassifier  100.00%  100.00%  100.00%   100.00%  100.00%"
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>86.65%</td>\n",
              "      <td>86.75%</td>\n",
              "      <td>74.65%</td>\n",
              "      <td>87.911%</td>\n",
              "      <td>86.750%</td>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>84.82%</td>\n",
              "      <td>85.00%</td>\n",
              "      <td>71.76%</td>\n",
              "      <td>86.780%</td>\n",
              "      <td>85.000%</td>\n",
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "1              GaussianNB   86.65%   86.75%  74.65%   87.911%  86.750%\n",
              "0  RandomForestClassifier   84.82%   85.00%  71.76%   86.780%  85.000%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 13\n",
            "Flattened array shape:  (2000, 160925)\n"
          ]
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        {
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          "text": [
            "<ipython-input-14-5499829e2956>:19: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
            "  plt.tight_layout()\n"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  15\n"
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   99.58%   99.58%  99.17%    99.59%  99.58%\n",
              "1              GaussianNB   95.83%   95.83%  92.00%    96.09%  95.90%"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   89.92%   90.00%  81.33%   91.339%  90.000%\n",
              "1              GaussianNB   88.13%   88.25%  78.16%   89.928%  88.250%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 13\n",
            "Flattened array shape:  (2000, 160925)\n"
          ]
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            "<ipython-input-14-5499829e2956>:19: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
            "  plt.tight_layout()\n"
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
              "0  RandomForestClassifier   99.58%   99.58%  99.17%    99.59%  99.58%\n",
              "1              GaussianNB   93.75%   93.75%  87.76%    93.95%  93.81%"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   94.25%   94.25%  88.63%   94.384%  94.250%\n",
              "1              GaussianNB   89.16%   89.25%  79.88%   90.641%  89.250%"
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 10\n",
            "Flattened array shape:  (2000, 160925)\n"
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            "<ipython-input-14-5499829e2956>:19: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
            "  plt.tight_layout()\n"
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            "(1200, 200) (400, 200)\n",
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              "               Classifier F1 Score Accuracy      MCC Precision   Recall\n",
              "1              GaussianNB   96.67%   96.67%   93.39%    96.69%   96.69%\n",
              "0  RandomForestClassifier  100.00%  100.00%  100.00%   100.00%  100.00%"
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              "      <th></th>\n",
              "      <th>Classifier</th>\n",
              "      <th>F1 Score</th>\n",
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              "      <th>MCC</th>\n",
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              "      <th>Recall</th>\n",
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              "      <th>1</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>89.15%</td>\n",
              "      <td>89.25%</td>\n",
              "      <td>80.04%</td>\n",
              "      <td>90.801%</td>\n",
              "      <td>89.250%</td>\n",
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              "      <th>0</th>\n",
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              "      <td>81.50%</td>\n",
              "      <td>65.43%</td>\n",
              "      <td>83.977%</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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            "text/plain": [
              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "1              GaussianNB   89.15%   89.25%  80.04%   90.801%  89.250%\n",
              "0  RandomForestClassifier   81.16%   81.50%  65.43%   83.977%  81.500%"
            ]
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          "text": [
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 12\n",
            "Flattened array shape:  (2000, 160925)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-14-5499829e2956>:19: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
            "  plt.tight_layout()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  22\n"
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              "      <td>GaussianNB</td>\n",
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              "      <td>94.58%</td>\n",
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              "               Classifier F1 Score Accuracy      MCC Precision   Recall\n",
              "1              GaussianNB   94.58%   94.58%   89.25%    94.63%   94.62%\n",
              "0  RandomForestClassifier  100.00%  100.00%  100.00%   100.00%  100.00%"
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            "Open test results for SNR =  22\n"
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
              "0  RandomForestClassifier   88.44%   88.50%  77.77%   89.270%  88.500%\n",
              "1              GaussianNB   85.74%   86.00%  74.78%   88.831%  86.000%"
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            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/Riffusion\n",
            "Number of audio files: 500\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/JsFakeChorale\n",
            "Number of audio files: 528\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/LA_composer\n",
            "Number of audio files: 488\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Fake/RNNCPAC\n",
            "Number of audio files: 482\n",
            " Processed 200/200 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Musicnet\n",
            "Number of audio files: 1376\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "/content/drive/My Drive/capstone_project/Datasets_5/Real/Maestro\n",
            "Number of audio files: 1232\n",
            " Processed 600/600 audio samples \n",
            "-----------------------------------------------------------------------------------------------------------\n",
            "DONE!!!\n",
            "\n",
            "number out of shape songs = 8\n",
            "Flattened array shape:  (2000, 160925)\n"
          ]
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          "text": [
            "<ipython-input-14-5499829e2956>:19: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
            "  plt.tight_layout()\n"
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          "text": [
            "(1200, 200) (400, 200)\n",
            "960 240 960 240\n",
            "Close test results for SNR =  25\n"
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              "               Classifier F1 Score Accuracy     MCC Precision  Recall\n",
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              "               Classifier F1 Score Accuracy     MCC Precision   Recall\n",
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              "0  RandomForestClassifier   85.86%   86.00%  73.48%   87.500%  86.000%"
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Main procedure for experiments with added noise.\n",
        "# First, we define the desired array of SNR which is in the range [-25, 25] in our case.\n",
        "# The chosen feature is \"mfc\" as the models show better accuracy with this audio feature.\n",
        "# The main loop is for required SNR. During each iteration, we load data, adding noise with\n",
        "# the current SNR, extract audio features, apply PCA, create datasets for training, close and open tests\n",
        "\n",
        "feature_type = \"mfc\"\n",
        "\n",
        "snr_range = [-25, -22, -20, -17, -15, -10, -5, 0, 2, 4, 6, 8, 10, 12, 15, 17, 20, 22, 25]\n",
        "final_results = []\n",
        "for snr in snr_range:\n",
        "  X, y, z = load_data_balanced(num_samples_vector, datasets_vector, feature_type, True, snr)\n",
        "\n",
        "  X[823].shape\n",
        "\n",
        "  bins_number = 157\n",
        "  X, y, z = adjustFeatureArrays(X, y, z, bins_number)\n",
        "\n",
        "  # reshape 3d vector into 2d vector\n",
        "  X_flattened = np.reshape(X, (X.shape[0], -1))\n",
        "\n",
        "  print(\"Flattened array shape: \", X_flattened.shape)\n",
        "  X_pca = applyPCA(200, X_flattened)\n",
        "\n",
        "  excluded_fake = la_composer_id # excluded fake dataset\n",
        "  excluded_real = maestro_id     # excluded real dataset\n",
        "\n",
        "  X_ready, X_open, y_ready, y_open = split_train_from_open(X_pca, y, z, excluded_fake, excluded_real)\n",
        "  # check open test split\n",
        "  print(X_ready.shape, X_open.shape)\n",
        "\n",
        "  # Split between training and test with the indicated proportion: 20% for test and 80% for training\n",
        "  split = 0.2\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X_ready, y_ready, test_size=split, random_state=42)\n",
        "\n",
        "  print (len(X_train), len(X_test), len(y_train), len(y_test))\n",
        "\n",
        "  print(\"Close test results for SNR = \", snr)\n",
        "  trained_models, close_results = close_test(X_train, y_train, X_test, y_test)\n",
        "  display(close_results)\n",
        "\n",
        "  print(\"Open test results for SNR = \", snr)\n",
        "  open_results = open_test(X_open, y_open, trained_models)\n",
        "  display(open_results)\n",
        "\n",
        "  final_results.append([close_results, open_results])\n",
        "  open_results.Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gu9EizfEu7DW",
        "outputId": "09af3bd0-48c1-4d99-cb51-f7211a58a4f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "random_forest_close\n",
            "[52.49, 61.22, 72.75, 82.91, 92.08, 95.42, 99.17, 100.0, 99.17, 98.33, 99.17, 100.0, 99.58, 100.0, 99.58, 99.58, 100.0, 100.0, 99.58]\n",
            "random_forest_open\n",
            "[51.47, 54.17, 56.32, 52.79, 55.39, 55.01, 53.66, 81.19, 65.27, 72.73, 68.4, 73.54, 86.0, 84.82, 89.92, 94.25, 81.16, 88.44, 85.86]\n",
            "gaussian_close\n",
            "[45.66, 55.83, 44.27, 78.03, 73.17, 88.69, 95.83, 98.75, 98.33, 99.17, 97.08, 98.33, 94.57, 97.5, 95.83, 93.75, 96.67, 94.58, 92.5]\n",
            "gaussian_open\n",
            "[44.77, 56.2, 43.58, 47.9, 38.48, 51.06, 47.96, 74.49, 67.64, 76.12, 78.5, 81.11, 86.71, 86.65, 88.13, 89.16, 89.15, 85.74, 88.87]\n"
          ]
        }
      ],
      "source": [
        "# Constructing arrays with F1 score values for open and close tests\n",
        "random_forest_close = []\n",
        "random_forest_open = []\n",
        "\n",
        "for result in final_results:\n",
        "  close_results = result[0].values\n",
        "  open_results = result[1].values\n",
        "\n",
        "  for item in close_results:\n",
        "    if item[0] == 'RandomForestClassifier':\n",
        "      random_forest_close.append(float(item[1][0:-1]))\n",
        "\n",
        "  for item in open_results:\n",
        "    if item[0] == 'RandomForestClassifier':\n",
        "      random_forest_open.append(float(item[1][0:-1]))\n",
        "\n",
        "print(\"random_forest_close\")\n",
        "print(random_forest_close)\n",
        "print(\"random_forest_open\")\n",
        "print(random_forest_open)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "q7AMIq3nzkIz",
        "outputId": "5cbed071-2ba4-4f19-a846-7343a3e8e542"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting graphs\n",
        "# Here we demonstrate how added noise affect the performance of the classifiers\n",
        "# x-axis - for SNR values\n",
        "# y-axis - for F1 score values\n",
        "\n",
        "plt.figure(0)\n",
        "plt.plot(snr_range, random_forest_close, 'g', label='RandomForest close')\n",
        "plt.plot(snr_range, random_forest_open, 'b', label='RandomForest open')\n",
        "\n",
        "plt.title('F1 score vs SNR related to the additive noise for RandomForest model')\n",
        "plt.xlabel('SNR(db)')\n",
        "plt.ylabel('F1 score(%)')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig(\"SNR_forest.png\")\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}