poly_detect.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "%load_ext autoreload"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "hbty8-qenfRV"
      },
      "outputs": [],
      "source": [
        "%autoreload 2\n",
        "import os\n",
        "import glob\n",
        "import guitarpro\n",
        "import librosa, soundfile, librosa.display\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy\n",
        "from utils import *\n",
        "from operations import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Da4NwbioglZk"
      },
      "source": [
        "## Break multi-track gtp files into single-track gtp files\n",
        "generate clean_single_track_gtps, which contain gtp files that have:\n",
        "* unified volume, \n",
        "* clean guitar tone, \n",
        "* no repeats,\n",
        "* no mixTableChanges\n",
        "This behaviour is implemented as default in `operations.get_single_tracks`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "MULTI_TRACK_DIR = \"/Volumes/MacOnly/UG_raw/all_time_top_by_hits\"\n",
        "# clean_single_track_gtps contains gtp files that have:\n",
        "# unified volume, clean guitar tone, no repeats, no mixTableChanges\n",
        "CLEAN_SINGLE_TRACK_DIR = (\n",
        "    \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/clean_single_track_gtps\"\n",
        ")\n",
        "\n",
        "i = 0\n",
        "for file in glob.glob(os.path.join(MULTI_TRACK_DIR, \"*.gp*\")):\n",
        "    i += 1\n",
        "    print(f\"processing file {i}: {file.split('/')[-1]}\")\n",
        "    get_single_tracks(file=file, output_dir=CLEAN_SINGLE_TRACK_DIR)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "MULTI_TRACK_DIR = \"/Volumes/MacOnly/UG_raw/all_time_top_by_hits\"\n",
        "# origin_single_track_gtps contains gtp files that have:\n",
        "# unified volume, original guitar tone, no repeats, no mixTableChanges\n",
        "ORIGIN_SINGLE_TRACK_DIR = (\n",
        "    \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/original_tone/origin_single_track_gtps\"\n",
        ")\n",
        "\n",
        "i = 0\n",
        "for file in glob.glob(os.path.join(MULTI_TRACK_DIR, \"*.gp*\")):\n",
        "    i += 1\n",
        "    print(f\"processing file {i}: {file.split('/')[-1]}\")\n",
        "    get_single_tracks(file=file, output_dir=ORIGIN_SINGLE_TRACK_DIR, force_clean=False)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Segment single-track audio into poly and mono segments (GT)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# a test\n",
        "file_name = \"DragonForce - Through The Fire And Flames_Lead 2.gp5\"\n",
        "song = guitarpro.parse(os.path.join(CLEAN_SINGLE_TRACK_DIR, file_name))\n",
        "poly, mono = poly_vs_mono(song)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[37.2, 38.400000000000006],\n",
              " [288.6, 289.2],\n",
              " [289.8, 290.4],\n",
              " [414.0, 418.79999999999995]]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "poly\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[0.0, 37.2],\n",
              " [38.4, 288.59999999999997],\n",
              " [289.2, 289.8],\n",
              " [290.4, 414.0],\n",
              " [418.8, 433.2]]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mono\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "CLEAN_SINGLE_TRACK_DIR = (\n",
        "    \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/clean_single_track_gtps\"\n",
        ")\n",
        "SINGLE_TRACK_AUDIO_DIR = (\n",
        "    \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/clean_single_track_audio\"\n",
        ")\n",
        "POLY_SEGMENTS_DIR = \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/poly_audio_segments\"\n",
        "MONO_SEGMENTS_DIR = \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/mono_audio_segments\"\n",
        "\n",
        "i = 0\n",
        "for file in glob.glob(os.path.join(CLEAN_SINGLE_TRACK_DIR, \"*.gp5\")):\n",
        "    i += 1\n",
        "    track_title, _ = os.path.splitext(file.split(\"/\")[-1])\n",
        "    print(f\"processing file {i}: {track_title}\")\n",
        "\n",
        "    song = guitarpro.parse(file)\n",
        "    poly, mono = poly_vs_mono(song)\n",
        "\n",
        "    y, sr = librosa.load(\n",
        "        os.path.join(SINGLE_TRACK_AUDIO_DIR, track_title + \".wav\"), sr=None, mono=True\n",
        "    )\n",
        "\n",
        "    for j, poly_segment in enumerate(poly):\n",
        "        start_sec = poly_segment[0]\n",
        "        end_sec = poly_segment[1]\n",
        "        segment = y[int(start_sec * sr) : int(end_sec * sr)]\n",
        "        # write segment to file, store it in the \"poly\" folder\n",
        "        segment_file_name = \"{}_{}_{}s.wav\".format(\n",
        "            track_title, j, int(start_sec)\n",
        "        )\n",
        "        soundfile.write(os.path.join(POLY_SEGMENTS_DIR, segment_file_name), segment, sr)\n",
        "\n",
        "    for j, mono_segment in enumerate(mono):\n",
        "        start_sec = mono_segment[0]\n",
        "        end_sec = mono_segment[1]\n",
        "        segment = y[int(start_sec * sr) : int(end_sec * sr)]\n",
        "        # write segment to file, store it in the \"mono\" folder\n",
        "        segment_file_name = \"{}_{}_{}s.wav\".format(track_title, j, int(start_sec))\n",
        "        soundfile.write(os.path.join(MONO_SEGMENTS_DIR, segment_file_name), segment, sr)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ORIGIN_SINGLE_TRACK_DIR = (\n",
        "    \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/original_tone/origin_single_track_gtps\"\n",
        ")\n",
        "SINGLE_TRACK_AUDIO_DIR = (\n",
        "    \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/original_tone/origin_single_track_audio\"\n",
        ")\n",
        "POLY_SEGMENTS_DIR = \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/original_tone/poly_audio_segments\"\n",
        "MONO_SEGMENTS_DIR = \"/Volumes/MacOnly/UG_proc/all_time_top_by_hits/original_tone/mono_audio_segments\"\n",
        "\n",
        "i = 0\n",
        "for file in glob.glob(os.path.join(ORIGIN_SINGLE_TRACK_DIR, \"*.gp5\")):\n",
        "    i += 1\n",
        "    track_title, _ = os.path.splitext(file.split(\"/\")[-1])\n",
        "    print(f\"processing file {i}: {track_title}\")\n",
        "\n",
        "    song = guitarpro.parse(file)\n",
        "    poly, mono = poly_vs_mono(song)\n",
        "\n",
        "    y, sr = librosa.load(\n",
        "        os.path.join(SINGLE_TRACK_AUDIO_DIR, track_title + \".wav\"), sr=None, mono=True\n",
        "    )\n",
        "\n",
        "    for j, poly_segment in enumerate(poly):\n",
        "        start_sec = poly_segment[0]\n",
        "        end_sec = poly_segment[1]\n",
        "        segment = y[int(start_sec * sr) : int(end_sec * sr)]\n",
        "        # write segment to file, store it in the \"poly\" folder\n",
        "        segment_file_name = \"{}_{}_{}s.wav\".format(\n",
        "            track_title, j, int(start_sec)\n",
        "        )\n",
        "        soundfile.write(os.path.join(POLY_SEGMENTS_DIR, segment_file_name), segment, sr)\n",
        "\n",
        "    for j, mono_segment in enumerate(mono):\n",
        "        start_sec = mono_segment[0]\n",
        "        end_sec = mono_segment[1]\n",
        "        segment = y[int(start_sec * sr) : int(end_sec * sr)]\n",
        "        # write segment to file, store it in the \"mono\" folder\n",
        "        segment_file_name = \"{}_{}_{}s.wav\".format(track_title, j, int(start_sec))\n",
        "        soundfile.write(os.path.join(MONO_SEGMENTS_DIR, segment_file_name), segment, sr)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Generate frames (features and labels) from the poly and mono segments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Mel-spectrum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# frame-level features: mel-spectrum (compute via mel-spectrogram)\n",
        "FRAME_SIZE = 2048\n",
        "HOP_SIZE = 2048\n",
        "MONO_LABEL = 0\n",
        "\n",
        "MONO_SEGMENTS_DIR = \"/content/mono_audio_segments\"\n",
        "\n",
        "i = 0\n",
        "features_with_label = []\n",
        "\n",
        "for file in glob.glob(os.path.join(MONO_SEGMENTS_DIR, \"*.wav\")):\n",
        "    i += 1\n",
        "    title, _ = os.path.splitext(file.split(\"/\")[-1])\n",
        "    print(f\"processing file {i}: {title}\")\n",
        "\n",
        "    y, sr = librosa.load(file, sr=None, mono=True)\n",
        "    mel_spec = librosa.feature.melspectrogram(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False)\n",
        "\n",
        "    no_frames = mel_spec.shape[1]\n",
        "    columns_to_delete = []\n",
        "\n",
        "    for j in range(no_frames):\n",
        "        if np.all(mel_spec[:, j] == 0):\n",
        "            columns_to_delete.append(j)\n",
        "    mel_spec = np.delete(mel_spec, columns_to_delete, axis=1)\n",
        "    print(f\"deleted {len(columns_to_delete)} empty frames\")\n",
        "    # zero for mono\n",
        "    label = np.zeros((1, mel_spec.shape[1]))\n",
        "    feature_with_label = np.append(mel_spec, label, axis=0)\n",
        "    features_with_label.append(feature_with_label)\n",
        "\n",
        "mono_data = np.concatenate(features_with_label, axis=1)\n",
        "mono_data.shape # (129, 626587)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [],
      "source": [
        "np.savez_compressed(\"/content/drive/MyDrive/THESIS CODE/mono.npz\", mono_data)   # ~300M\n",
        "# np.save(\"/content/drive/MyDrive/THESIS CODE/mono.npy\", mono_data)   # ~600M"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# frame-level features: mel-spectrum (compute via mel-spectrogram)\n",
        "FRAME_SIZE = 2048\n",
        "HOP_SIZE = 2048\n",
        "POLY_LABEL = 1\n",
        "\n",
        "POLY_SEGMENTS_DIR = \"/content/poly_audio_segments\"\n",
        "\n",
        "i = 0\n",
        "features_with_label = []\n",
        "\n",
        "for file in glob.glob(os.path.join(POLY_SEGMENTS_DIR, \"*.wav\")):\n",
        "    i += 1\n",
        "    title, _ = os.path.splitext(file.split(\"/\")[-1])\n",
        "    print(f\"processing file {i}: {title}\")\n",
        "\n",
        "    y, sr = librosa.load(file, sr=None, mono=True)\n",
        "    mel_spec = librosa.feature.melspectrogram(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False)\n",
        "\n",
        "    no_frames = mel_spec.shape[1]\n",
        "    columns_to_delete = []\n",
        "\n",
        "    for j in range(no_frames):\n",
        "        if np.all(mel_spec[:, j] == 0):\n",
        "            columns_to_delete.append(j)\n",
        "    mel_spec = np.delete(mel_spec, columns_to_delete, axis=1)\n",
        "    print(f\"deleted {len(columns_to_delete)} empty frames\")\n",
        "    # one for poly\n",
        "    label = np.ones((1, mel_spec.shape[1]))\n",
        "    feature_with_label = np.append(mel_spec, label, axis=0)\n",
        "    features_with_label.append(feature_with_label)\n",
        "\n",
        "poly_data = np.concatenate(features_with_label, axis=1)\n",
        "poly_data.shape # (129, 551288)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "np.savez_compressed(\"/content/drive/MyDrive/THESIS CODE/poly.npz\", poly_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### MFCC\n",
        "Parameters: n_mels (40 seems better than default 20), fmax (default 22050 seems better than 8000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# frame-level features: MFCC (compute via MFCC)\n",
        "FRAME_SIZE = 2048\n",
        "HOP_SIZE = 2048\n",
        "MONO_LABEL = 0\n",
        "\n",
        "MONO_SEGMENTS_DIR = \"/content/mono_audio_segments\"\n",
        "\n",
        "i = 0\n",
        "features_with_label = []\n",
        "\n",
        "for file in glob.glob(os.path.join(MONO_SEGMENTS_DIR, \"*.wav\")):\n",
        "    i += 1\n",
        "    title, _ = os.path.splitext(file.split(\"/\")[-1])\n",
        "    print(f\"processing file {i}: {title}\")\n",
        "\n",
        "    y, sr = librosa.load(file, sr=None, mono=True)\n",
        "    mel_spec = librosa.feature.melspectrogram(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False)\n",
        "    # compute MFCC\n",
        "    log_mel_spec = librosa.power_to_db(mel_spec)\n",
        "    mfcc = librosa.feature.mfcc(S=log_mel_spec)\n",
        "\n",
        "    assert (mel_spec.shape[1] == mfcc.shape[1])\n",
        "    no_frames = mfcc.shape[1]\n",
        "    columns_to_delete = []\n",
        "\n",
        "    for j in range(no_frames):\n",
        "        if np.all(mel_spec[:, j] == 0):\n",
        "            columns_to_delete.append(j) \n",
        "\n",
        "    mfcc = np.delete(mfcc, columns_to_delete, axis=1)\n",
        "    print(f\"deleted {len(columns_to_delete)} empty frames\")\n",
        "    # zero for mono\n",
        "    label = np.zeros((1, mfcc.shape[1]))\n",
        "    feature_with_label = np.append(mfcc, label, axis=0)\n",
        "    features_with_label.append(feature_with_label)\n",
        "\n",
        "mono_data = np.concatenate(features_with_label, axis=1)\n",
        "print(mono_data.shape) # (21, 626587)\n",
        "np.savez_compressed(\"/content/drive/MyDrive/THESIS CODE/mono_mfcc.npz\", mono_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# frame-level features: MFCC (compute via MFCC)\n",
        "FRAME_SIZE = 2048\n",
        "HOP_SIZE = 2048\n",
        "POLY_LABEL = 1\n",
        "\n",
        "POLY_SEGMENTS_DIR = \"/content/poly_audio_segments\"\n",
        "\n",
        "i = 0\n",
        "features_with_label = []\n",
        "\n",
        "for file in glob.glob(os.path.join(POLY_SEGMENTS_DIR, \"*.wav\")):\n",
        "    i += 1\n",
        "    title, _ = os.path.splitext(file.split(\"/\")[-1])\n",
        "    print(f\"processing file {i}: {title}\")\n",
        "\n",
        "    y, sr = librosa.load(file, sr=None, mono=True)\n",
        "    mel_spec = librosa.feature.melspectrogram(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False)\n",
        "    # compute MFCC\n",
        "    log_mel_spec = librosa.power_to_db(mel_spec)\n",
        "    mfcc = librosa.feature.mfcc(S=log_mel_spec)\n",
        "\n",
        "    assert (mel_spec.shape[1] == mfcc.shape[1])\n",
        "    no_frames = mel_spec.shape[1]\n",
        "    columns_to_delete = []\n",
        "\n",
        "    for j in range(no_frames):\n",
        "        if np.all(mel_spec[:, j] == 0):\n",
        "            columns_to_delete.append(j)\n",
        "    mfcc = np.delete(mfcc, columns_to_delete, axis=1)\n",
        "    print(f\"deleted {len(columns_to_delete)} empty frames\")\n",
        "    # one for poly\n",
        "    label = np.ones((1, mfcc.shape[1]))\n",
        "    feature_with_label = np.append(mfcc, label, axis=0)\n",
        "    features_with_label.append(feature_with_label)\n",
        "\n",
        "poly_data = np.concatenate(features_with_label, axis=1)\n",
        "print(poly_data.shape) # (21, 551288)\n",
        "np.savez_compressed(\"/content/drive/MyDrive/THESIS CODE/poly_mfcc.npz\", poly_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Feature visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Mel spectrum\n",
        "Tuning the fmax and n_mels didn't help the classifier\\\n",
        "Clipping the spectrum (use up to 8kHz) helped a bit (maybe try some other upper limits?)\\\n",
        "Adding a peak_count feature helped a bit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (10, 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'poly')]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# mel spectrum is computed using the mel spectrogram in librosa\n",
        "# here we visually compare the mel spectrogram of mono and poly segments\n",
        "mono_example = \"notes.wav\"\n",
        "poly_example = \"chords.wav\"\n",
        "y_mono, sr = librosa.load(mono_example, sr=None, mono=True)\n",
        "y_poly, sr_poly = librosa.load(poly_example, sr=None, mono=True)\n",
        "assert(sr == sr_poly)\n",
        "\n",
        "FRAME_SIZE = 2048\n",
        "HOP_SIZE = 2048\n",
        "F_MAX = 8000\n",
        "\n",
        "mel_mono = librosa.feature.melspectrogram(y_mono, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "mel_poly = librosa.feature.melspectrogram(y_poly, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "\n",
        "fig, axs = plt.subplots(1, 2)\n",
        "mel_mono_dB = librosa.power_to_db(mel_mono, ref=np.max)\n",
        "mel_poly_dB = librosa.power_to_db(mel_poly, ref=np.max)\n",
        "mono_img = librosa.display.specshow(mel_mono_dB, x_axis='time', y_axis='mel', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[0])\n",
        "poly_img = librosa.display.specshow(mel_poly_dB, x_axis='time', y_axis='mel', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[1])\n",
        "fig.colorbar(poly_img, ax=axs[1], format='%+2.0f dB')\n",
        "axs[0].set(title='mono')\n",
        "axs[1].set(title='poly')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'mute_poly')]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# mel spectrum is computed using the mel spectrogram in librosa\n",
        "# here we visually compare the mel spectrogram of mono and poly segments\n",
        "mono_example = \"mute_notes.wav\"\n",
        "poly_example = \"mute_chords.wav\"\n",
        "y_mono, sr = librosa.load(mono_example, sr=None, mono=True)\n",
        "y_poly, sr_poly = librosa.load(poly_example, sr=None, mono=True)\n",
        "assert(sr == sr_poly)\n",
        "\n",
        "FRAME_SIZE = 2048\n",
        "HOP_SIZE = 2048\n",
        "F_MAX = 8000\n",
        "\n",
        "mel_mono = librosa.feature.melspectrogram(y_mono, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "mel_poly = librosa.feature.melspectrogram(y_poly, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "\n",
        "fig, axs = plt.subplots(1, 2)\n",
        "mel_mono_dB = librosa.power_to_db(mel_mono, ref=np.max)\n",
        "mel_poly_dB = librosa.power_to_db(mel_poly, ref=np.max)\n",
        "mono_img = librosa.display.specshow(mel_mono_dB, x_axis='time', y_axis='mel', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[0])\n",
        "poly_img = librosa.display.specshow(mel_poly_dB, x_axis='time', y_axis='mel', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[1])\n",
        "fig.colorbar(poly_img, ax=axs[1], format='%+2.0f dB')\n",
        "axs[0].set(title='mute_mono')\n",
        "axs[1].set(title='mute_poly')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(128, 86)\n",
            "(128, 86)\n"
          ]
        }
      ],
      "source": [
        "# count the number of peaks in mono and poly spectra\n",
        "mono_example = \"notes.wav\"\n",
        "poly_example = \"chords.wav\"\n",
        "y_mono, sr = librosa.load(mono_example, sr=None, mono=True)\n",
        "y_poly, sr = librosa.load(poly_example, sr=None, mono=True)\n",
        "\n",
        "FRAME_SIZE = 2048\n",
        "HOP_SIZE = 2048\n",
        "F_MAX = 8000\n",
        "\n",
        "mel_mono = librosa.feature.melspectrogram(y_mono, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "mel_poly = librosa.feature.melspectrogram(y_poly, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "\n",
        "print(mel_mono.shape)\n",
        "print(mel_poly.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [],
      "source": [
        "mel_mono = mel_mono.transpose()\n",
        "mel_poly = mel_poly.transpose()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for spectrum in mel_mono:\n",
        "    # spectrum = spectrum / max(spectrum)\n",
        "    peaks, _ = scipy.signal.find_peaks(spectrum, height=np.mean(spectrum))\n",
        "    print(len(peaks))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for spectrum in mel_poly:\n",
        "    # spectrum = spectrum / max(spectrum)\n",
        "    peaks, _ = scipy.signal.find_peaks(spectrum, height=np.mean(spectrum))\n",
        "    print(len(peaks))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "peak_counts = np.zeros_like(y) # # (1177875,)\n",
        "for i in range(X.shape[0]):\n",
        "    # spectrum = spectrum / max(spectrum)\n",
        "    spectrum = X[i]\n",
        "    assert(spectrum.shape==(128,))\n",
        "    peaks, _ = scipy.signal.find_peaks(spectrum, height=np.mean(spectrum))\n",
        "    peak_counts[i] = len(peaks)\n",
        "peak_counts_reshaped = np.expand_dims(peak_counts, axis=1) # (1177875, 1)\n",
        "\n",
        "X_new = np.append(X, peak_counts_reshaped, axis=1) # (1177875, 129)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Mel cepstrum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# mel cepstrum is computed using the MFCC\n",
        "# MFCC can be computed from either the audio signal or its log power mel-spectrogram\n",
        "FRAME_SIZE = 2048\n",
        "HOP_SIZE = 2048\n",
        "F_MAX = 8000\n",
        "\n",
        "y, sr = librosa.load(\"notes.wav\", sr=None, mono=True)\n",
        "mfcc1 = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "\n",
        "S = librosa.feature.melspectrogram(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "S = librosa.power_to_db(S)\n",
        "mfcc2 = librosa.feature.mfcc(S=S)\n",
        "\n",
        "np.array_equal(mfcc1, mfcc2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (10, 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'poly')]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "y, sr = librosa.load(\"notes.wav\", sr=None, mono=True)\n",
        "mfcc_mono = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, n_mfcc=40, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "y, sr = librosa.load(\"chords.wav\", sr=None, mono=True)\n",
        "mfcc_poly = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, n_mfcc=40, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "\n",
        "fig, axs = plt.subplots(1, 2)\n",
        "mono_img = librosa.display.specshow(mfcc_mono, x_axis='time', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[0])\n",
        "poly_img = librosa.display.specshow(mfcc_poly, x_axis='time', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[1])\n",
        "# fig.colorbar(mono_img, ax=axs[0])\n",
        "fig.colorbar(poly_img, ax=axs[1])\n",
        "axs[0].set(title='mono')\n",
        "axs[1].set(title='poly')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'poly')]"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "y, sr = librosa.load(\"ebgd.wav\", sr=None, mono=True)\n",
        "mfcc_mono = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, n_mfcc=80, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "y, sr = librosa.load(\"ebgd_chord.wav\", sr=None, mono=True)\n",
        "mfcc_poly = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, n_mfcc=80, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "\n",
        "fig, axs = plt.subplots(1, 2)\n",
        "mono_img = librosa.display.specshow(mfcc_mono, x_axis='time', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[0])\n",
        "poly_img = librosa.display.specshow(mfcc_poly, x_axis='time', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[1])\n",
        "fig.colorbar(poly_img, ax=axs[1])\n",
        "axs[0].set(title='mono')\n",
        "axs[1].set(title='poly')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'poly')]"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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ARkSuAABALyaTGfGa2o5urqZbEz3z+FclSZe/eSmZJ/UhYjFUOBimw4Yxjxe+k2Z1imXGdhWTaTJPl2u/9QWz7Y0zJxbKHIyGszQvimSdq1ilXbFvVumzVc5vlWO3nr0gSRqsjcL+5vziea/Cp825dp1Tp1DvYJju+856Qx+30hPjFnulNZfCeLfq96aM1jmF/FFr/GIQObYlHNvVN50LWiw/9Hcqf1fbjxKfTDV5+uuaXmzWrrp/W1/bUY9/1WepffVYLJu7qX3L+rZ1fD2vEvkHJzbK/GtrrXZKklWH2fr64vF1+XVaGHNVc8LCfF7IH/fVx6b2zQvt06haP4omrbhUjodvbVb1JubndFLl2WqaXNfpiTbUdVZjl+z1Yby2qjFPrCE2Px9Ga6FhZX6/PC6bfvpMs2tSpsUxqK+9eL3NVP09yzMIeYpBK8/sd0l2ovmbFI+X1JxjHIO6jDpfXItT7cIV4TYTAAAgI14WBAAA/Zha0fLjjsgVAABARkSuAABAb3xxc4OeAAAAyIjIFQAA6I0PEW0QuQIAAMiIyBUAAOjHrPnsLBC5AgAAyInIFQAA6I1nrhpErgAAADIicgUAAPrjc65m6AkAAICMiFwBAIBezExmPHNVI3IFAACQEZErAADQH89czdATAAAAGXFzBQAAkBEvCwIAgN74ENEGkSsAAICMiFwBAIB++OLmFnoCAAAgIyJXAACgP565miFyBQAAkBGRKwAA0JvxzNUMPQEAAJDRFUeupuNitu2Fxz1Lj/OiSKZPt5r0wbB53baY+o7So5gn2mk5135rs71+5qrF8kbDprzJNJketT4LJH5dQOybkD4Ypsvp6stWHk/3QfyCzZ3kGZ5YD/vT9+buTbsstN3CObXaXqTrbwl95tOmj1dqQ8hTjMehzCbdFNtpVRvj3Gj2D9bTl413nEdrvGN7QxtXeZdNPO9Vxj72d2ff1/sV59jya/gws+Gw1e9erWNx5OpxnM2D1Nj4Yh/OH7e8IYl5sOr/+qvyR6fPlIetry/mGa2V/4Y5o3oO12meuAank5B/tJiv2dkuM6rLqvshXhezvlmbJdm4rNOuOjWXJ5xHPWfjWlWvT3VabOegaleRmMup67Sq0+bbHiRHtR7zqt98czOVa2Z2Daf6ps4zKLrbmWrfXD73sMZNxlpQzbN6bfaQZ5V1pZOJZ64CIlcAAAAZ8cwVAADoyVoR8uOOngAAAMiImysAANCf2d78bNsMu9PMHjOzs2b2M3tw5gu4uQIAAEeCmQ0l/R+S/oKk2yX9uJndvtft4JkrAADQj6n97vf9c4eks+7+x5JkZvdJukvSo3vZiAPREwAAACu63sweCj9vCvtukvTF8PsTVdqeInIFAAAOk3Pu/qqOfamHslb4MMW8uLkCAAA9rfaw+R54QtIt4febJT25143gZUEAAHBU/Lak28zsVjNbl3S3pAf2uhFErgAAQG8H4UNE3X1iZm+V9DGV39P0Xnd/ZK/bwc0VAAA4Mtz9o5I+up9t4OYKAAD0Y1r9y8ePAXoCAAAgIyJXAACgJ5MGB+LdggcCkSsAAICMiFwBAIBeTJLxzNUMPQEAAJARkSsAANCPiWeuAiJXAAAAGRG5AgAAPRmfcxXQEwAAABkRuQIAAP0Zz1zViFwBAABkxM0VAABARrwsCAAA+hsQr6nt6OZqMDSduOakJMnC51l44bPtYlJkalpddlOehYErJtOmXaNhMt16fOZGrGv9zFWz7eHJEwtti3lt2LSlXV66LbHv5nas3NZlZa7SB51tCLaevSBJKrYm4bh0G2N/eLG1bZ52enpetc6jNQ+2knlax47SY6LY/labfWF/PFPf7DjvjnfKbN+7c+WE9sY+9mmY2x3zrFXvNuPj4Vpp7T+q7/gxk62NNDxzukmq+zFeA8uumTrfCnk8XsPzx8X5WbUh5q/HoE6LYzI4dapd3yAxF+q5srbepNXnOkrMyPpZmTDHZmvQaK36PXFcXXd81mY6WUyblZko4+Jz5a7LFxf3LTQzzM1EX87U51qfQ+yjVBuWV9r+PXV8PU7rob/rvoxzZf7aiv09fz5rif6rzyO2Yf4ci8R1Hfto1P6z31pbp+k1ATtH5AoAAPRjfBRDRE8AAABkROQKAAD0x9ffzBC5AgAAyIjIFQAA6I9nrmboCQAAgIyIXAEAgP74+psZIlcAAAAZEbkCAAD9mPEJ7QE9AQAAkBGRKwAA0B/PXM0QuQIAAMiIyBUAAOiPz7maoScAAAAy4uYKAAAgI14WBAAA/fBRDC30BAAAQEZErgAAQH98FMMMkSsAAICMiFwBAID++CiGGXoCAAAgIyJXAACgJ+OZq4DIFQAAQEZErgAAQD8mPucqoCcAAAAyInIFAAB6cUnOM1czO7q5GoxGuupbrpUknbzBZ+k+mTbbRZluA1tIk6TB+ih5XMzfCi0WxQoNSwfgLNNADzbWZ9se2jMYDSVJRcd5FFuTJr3KO1+GQt/EPNEq+WMen4b2DIfp9I63zLoXyTzDtXLcBmtryeM6z28Fq7Q3zqHWXIl9Furt7st0OXEuzooL9bfKHqbLtjAPV+mDeH4tK8xnd0+nxzK7rqM6PZWmdF8cBTYcanDN8zSIfeSJcarnfWr9mO+71FjVx8ey56+3QaLsmCfVrtpauR75N75eHhbPp5qbPhmX+2KZqTpr9XUR53bdhmX7VhGuOaWunar9NlpbbOd8X8ayBlVZ5t1tGnTXt6wtrTbUVab6oc4yniweV+f32O9Lrq25XVaEa7I6t1lJrX6oUifN35uF9sW+2dxs74xt7lqTsGNErgAAQE/G51wF9AQAAEBGRK4AAEB/RK5m6AkAAICMuLkCAADIiJcFAQBAb3wUQ4PIFQAAQEZErgAAQD/GRzFE9AQAAEBGRK4AAEB/PHM1Q+QKAAAgIyJXAACgv47vRT2O6AkAAICMiFwBAICejM+5CohcAQAAZETkCgAA9GPic64CegIAACAjIlcAAKA3J3I1Q08AAABkROQKAAD0ZHxCe0DkCgAAICNurgAAADLiZUEAANAbD7Q36AkAAICMiFwBAID+eKB9hsgVAAA4FszsL5vZI2ZWmNmr5vbdY2ZnzewxM3ttSH+lmT1c7XuH2fZ3kdxcAQCAfszKr7/Zi59+PifpRyX9Rrv5drukuyW9TNKdkt5lZsNq97slvUnSbdXPndtVsqOXBW040Po1pxPpw9m2F754oBfJ8pJ5V+RFU6YNBsn0lljXoOOms6s9Id1GzbnWgzyIvRgGfrA+Sqa3dCR3nYeHviw2p6H45pws1hXa3urvQUc/dan62DvGMrZFse1hbKyr32M548m2eeJ5DOLci30Q0lvjujZKp0dVOYNp6N/Qp3EODEJ53jFPWv/JGXQMeOd4+7Z5Wvk7zmm7vm8d1zHGh56ZNFqT1taX56v7fKcvccwfF8d6hbFrlzX3e7ymq3lp69V5nDjZ7KvqtLq+OH/qdi07v9jmen+4DhYU1b7U+pqai/V12bq25vqrda7VelD/jYvXbz1P67Is0fbUudb9FsdkOGq3L7WGzfYtXit2shwD37zcNKHK75NxaNegvS9ea1W5Xp/z2triviKxPtanH9e82XlVaZMl8y+O7zF4IN3dPy/NrculuyTd5+6bkr5gZmcl3WFmj0u62t0/WR33AUmvk/Tgsnp45goAAPTiknzvnrm63sweCr+/x93f07PMmyR9Kvz+RJU2rrbn05fi5goAABwm59z9VV07zewTkl6Y2PV2d/9w12GJNF+SvhQ3VwAAoL8D8rKiu7/mCg57QtIt4febJT1Zpd+cSF/qYPQEAADA/nlA0t1mtmFmt6p8cP3T7v6UpPNm9urqXYJvkNQV/ZohcgUAAHrz5CtoB4uZ/Yikd0q6QdIvm9ln3P217v6Imd0v6VFJE0lvcff6af83S3qfpJMqH2Rf+jC7xM0VAAA4Jtz9Q5I+1LHvXkn3JtIfkvTyndTDzRUAAOjJ+G7BgJ4AAADIiMgVAADoj8jVDD0BAACQETdXAAAAGfGyIAAA6Mf29OtvDjwiVwAAABkRuQIAAL04H8XQQk8AAABkROQKAAD0xzNXM0SuAAAAMiJyBQAAeuOZqwY9AQAAkBGRKwAA0JPJxTNXNSJXAAAAGRG5AgAAvfHMVYOeAAAAyIjIFQAA6MfE51wFRK4AAAAyInIFAAB6Mjnxmpkd3Vx54Zo8d0mSNDx5IqRPZts2sFneWdpwGAtJpvt0GrI0eVZq12S6faaYP9Rl4QE89+3rLTZDO6t663OWpCK0ZTAK5x0NmjrjsTvVdWzXeQxCfxfTFfps0JRz+elvSpJGJ9bTdYbx7hLbG/MPRoNkelf+mD4dbH8xx/lhYUyKrWbexjZsK9Q5XGsuoenm1vZtiecdjnVP95+FMLt1nGtrHnT1R+qaCnljH+30+js03KWtTWkYlr2671Lzd5i4fuvrZtl1m1qPVunTZXPZQvum5bz1zcvVrkTZ9boW19W5fJY6v1Z7hvWB3XnqMuODzKm0WtX2eK7Fs+XaotGoOiwcN1+GX2q2q/b7eFxmDePq04m61OXHeb6QFtfHuj3VtRjb5/PraOzvVOV1m6uxS86xqp76vMpfPJkn7vPJWPPqMU5e06lxtSN67e8DbjMBAAAy4mVBAADQi0tyHmifIXIFAACQEZErAADQGx8i2qAnAAAAMiJyBQAAeuOLmxtErgAAADIicgUAAHoynrkK6AkAAICMiFwBAIDe+JyrBpErAACAjIhcAQCAXly8WzAicgUAAJARkSsAANCP8W7BiJ4AAADIiMgVAADojWeuGkSuAAAAMuLmCgAAICNeFgQAAL3xQHuDngAAAMiIyBUAAOiNB9obRK4AAAAyInIFAAB6cfEhohE9AQAAkBGRKwAA0BvPXDWIXAEAAGS0o8iVF4W2zl8sD5xMm3T3pceZNXezPm2O0yDc2xVFOn2VMrvqD2XacLht/s4yQzleNOk2KsscKF12sUIfechjg/Rdf6vOmKernzr6Mp7fKmKbN645LUlaO3NVOnN4rT2Ocdc5dWmNUzjvrjI7+2aV9sTnA7xYSL/8ta8njxuENtZzQJJGo5OLJzQntrc1Tl15VtDqp8m4aVsYe0/UZYN033m6WYeeF4X88iXZ+nqTOFor/009K5LqiHrsl3VSnSeO47Aqv+7nrrk3r84X83i7ra01taqzvo5a++bqa+0Lc3pW9fw8HCyu41a3b1Ak9pXHe2i7Jfp5cObqcqMel5inXrPquZxos83nlWT12lXP+8RamVqZZmtkar1OlGV1e6rrrrhwvsk/mSy0a+GaT63V822PEuO0kC+uoePx4nHL/l7Xbb5CvsO/MUcZkSsAAICMeOYKAAD05k7kqkbkCgAAICMiVwAAoCeTE6+ZoScAAAAyInIFAAB6cfE5VxGRKwAAgIyIXAEAgN6IXDWIXAEAAGTEzRUAAEBGvCwIAAB642XBBpErAACAjIhcAQCAnozIVUDkCgAAICMiVwAAoDe+uLlB5AoAACAjIlcAAKAXvv6mjcgVAABARkSuAABAb0SuGkSuAAAAMuLmCgAA9ObVZ13t9k8fZvaPzOz3zez3zOxDZva8sO8eMztrZo+Z2WtD+ivN7OFq3zvMbNtGcHMFAACOi49Lerm7f4+kP5B0jySZ2e2S7pb0Mkl3SnqXmQ2rY94t6U2Sbqt+7tyuEm6uAABATyb3vfnpw91/1d0n1a+fknRztX2XpPvcfdPdvyDprKQ7zOxGSVe7+yfd3SV9QNLrtquHmysAAHCYXG9mD4WfN11hOX9V0oPV9k2Svhj2PVGl3VRtz6cvxbsFAQBALy6p2Lt3C55z91d17TSzT0h6YWLX2939w1Wet0uaSPrX9WGJ/L4kfSlurgAAwJHh7q9Ztt/M3ijpL0r6geqlPqmMSN0Sst0s6ckq/eZE+lI7urmywUDrZ66SJA021mfpg7W1ZrtOL4rmwEHHq49deWJ60PSBZMNhsyPkb+UJD/R3pRfjiZK8KbOYTJvkabM9PHGibPpa6MZwHjFv15sLbDRMpnux7Y1xp9g3sQ07Fvp1cuHiYj1h3FtC38nSY2+DdH+0zjuU43GM09NjrloP6eHYSUjvKMiqNp+88YYmMZTRdd6tORl1nKuFvrG1jkvR0+dhHdfUTvMk9Zh7B5kNh7JrrpUm4Zpfr9areH2OqvGtr51UP04T60Y9Vtu/kag1rlJi3tRjNCtzsJDdTpwsNzZONPvqthfTqilLzqsIa0OdL9ZTzK0doSzzuTkS593cqbTy1mXEtM3L5b/jcflvqr/r8uO++rhE+2b5JnWZqT5OrI31+afWhtRaVueryh+cOLGYJ3U9jarrPfZDvX6k2lCf/3C0uG+VNq+6b7u14Qgwszsl/W1J/6m7xz9sD0j6oJn9vKQXqXxw/dPuPjWz82b2akm/JekNkt65XT1ErgAAQG+H5ENE/5mkDUkfr/7z8Sl3/yl3f8TM7pf0qMqXC9/i7vUd+JslvU/SSZXPaD24UOocbq4AAMCx4O7fvmTfvZLuTaQ/JOnlO6mHmysAANCPq/fHJBwlfBQDAABARkSuAABAb4fkmas9QeQKAAAgIyJXAACgp/5fTXOUELkCAADIiMgVAADoxcUzVxGRKwAAgIyIXAEAgN545qpB5AoAACAjIlcAAKC3o/+1z6sjcgUAAJARkSsAANAbz1w1iFwBAABkxM0VAABARrwsCAAAenEZHyIaELkCAADIiMgVAADojQfaG0SuAAAAMiJyBQAAeuOZqwaRKwAAgIyIXAEAgH5cKny/G3FwELkCAADIiMgVAADoxcUzV9HObq5MstFQkjQ8eWJ53kE6KGaD0PmD4WzTdxhP9Om02Z4027H8IpTZSg/HzjUuue3TrY5GlN8BXmw1+21trdlfFGEzfX4Wz6MjTzzXVv+1muJhu6nXwjh0HdtVTvTcl5+WJK2fDuMexzjU2W5LegyiVdrV0lFv55yzpnx3T6anbD5zPl39KF3PIIx9HINYZ0tHn0Wr9F/ME9u203HYri2HXlFIz11op21eLv8dNmtRfV3P1oA4T+o5FufdvNR412UsO36QWH/qsYpr03hcVrO5We6KZYyrtaheM0ZhPZpMqvZVdcdxnj/nKLVvPi3OqfkyPJzrsGpPXBsvXWy3L45FnS91bdd9Wvf3CmtBF6vqrNdaG8a/Te2xaq3xVf76uMFGsz56fd6tNb5Mm637XizsS5o/x2X9Ufej1PTlsvnWo9/QjcgVAADojc+5anCbCgAAkBGRKwAA0FvXExDHEZErAACAjIhcAQCAnkwF7xacIXIFAACQETdXAAAAGfGyIAAA6MXFRzFERK4AAAAyInIFAAB646MYGkSuAAAAMiJyBQAAeuOLmxtErgAAADIicgUAAPpxqeCZqxkiVwAAABkRuQIAAL3wOVdtRK4AAAAyInIFAAB643OuGkSuAAAAMiJyBQAAeiv4nKsZIlcAAAAZEbkCAAC98cxVg8gVAABARtxcAQAAZMTLggAAoBeX8SGiAZErAACAjIhcAQCAfvji5pad3VwVhSbPXZIkTS9vzZJt0ATAivG43Bikg2JmTdjQp9NmO4yKjYY7OtaGTX7vertCUTTboW0+mSYySzZo6tr85nNNMSH/cGNdkjQ6sZ48rqu8Vt/EdkUdbWz1TTi2mHSUE4schTI7roKY7qH84Xo5VUanTjZtsY7A56BjjGNfd8yPQWssm/pbdcW+jOfR0ferXPGpuuJcao1BaOOga64O0unxvLvmym5IjXesP+6fbm4t5D1SNk4023UfxPlVz9l6nMO6s/B2qDBvZvOsLjM17+r8qTUgrgWeXpckqbhwviziedeVCWeuWWxf3ea4jgzX2m2Ic34yqQoP9drc/Fz1rWDFXPmp40KaDcu1xdY3FvMN566juJ74kjWvzlePa8xblzmN51rms9HiumTDaj2Y1n2UWCPrf8M1ZUVV1npzDrN1YX6cJNl8P43WwvlUddfjNFryp7tYPC+l+qo6D0/NYfRG5AoAAPTGRzE0eOYKAAAgIyJXAACgN+frb2aIXAEAAGRE5AoAAPTi4nn4iMgVAABARkSuAABAb7xbsEHkCgAAICMiVwAAoDciVw0iVwAAABkRuQIAAL24S4XzOVc1IlcAAAAZcXMFAACQES8LAgCA3nigvUHkCgAAICNurgAAQG/ue/PTh5n9PTP7PTP7jJn9qpm9KOy7x8zOmtljZvbakP5KM3u42vcOM9v2yX1urgAAwHHxj9z9e9z9FZI+IunvSJKZ3S7pbkkvk3SnpHeZ2bA65t2S3iTpturnzu0q4eYKAAD0Vvje/PTh7s+GX0+p/M5pSbpL0n3uvunuX5B0VtIdZnajpKvd/ZPu7pI+IOl129XDA+0AAOAwud7MHgq/v8fd37PqwWZ2r6Q3SPqmpP+sSr5J0qdCtieqtHG1PZ++FDdXAACgF5fke/choufc/VVdO83sE5JemNj1dnf/sLu/XdLbzeweSW+V9LOSUo33JelLcXMFAACODHd/zYpZPyjpl1XeXD0h6Zaw72ZJT1bpNyfSl+KZKwAA0M8evVMww7sFbwu//rCk36+2H5B0t5ltmNmtKh9c/7S7PyXpvJm9unqX4BskfXi7eohcAQCA4+Lvm9l3Siok/Ymkn5Ikd3/EzO6X9KikiaS3uPu0OubNkt4n6aSkB6ufpbi5AgAAvfV9J99ecPcfW7LvXkn3JtIfkvTyndTDy4IAAAAZEbkCAAC9lO8W3O9WHBw7urlyl7yK+61dfbLZEWKBw431cmPQvHvRp9PZ9mBtrUkvimSeVp2hbItlTkL+wSCdHsvp+rT60TCZHD/dfuPaM82O0ObRqbIPWucU6ncP5xfOYzAMdXb0k8U88dj1jiGzjiBkaEMrT0xvZfew3eR57ktfK5uyNUkeV8TxC8fFvmk1V+n6p7H/QjkWxziWH8avNVfiuIb8ca5EqTEZrjV9PTh9VbJO6xj7qGtut+ZzR7+32tgxV+O4xjJbbUjE67vqb/XFEeJeqNi8rMHJsHYNqnMtwhjV57+sH2ZjFObTfPb1xBys51/HGC9IzNd6fCZffqr8/fw3F/LYsJq78Tqvzyc1H0eJ63Q0arch1R+pdWfZN4PUZY2aa6u4dLE8rL6WUtdrnZb6652qr8pXX0vWcd2vZK58T7WhKr+4cGG1suoybHH9t2X9XdsMRS7LV49P6u9Ax1qBPIhcAQCA3ohcNXjmCgAAICNurgAAADLiZUEAANDbYfgohr1C5AoAACAjIlcAAKCfDF9Nc5QQuQIAAMiIyBUAAOjFtfrHtx0HRK4AAAAyInIFAAB645mrBpErAACAjIhcAQCA3ohcNYhcAQAAZETkCgAA9OLOJ7RHRK4AAAAyInIFAAB6cx66miFyBQAAkBGRKwAA0BuBqwaRKwAAgIy4uQIAAMiIlwUBAEBvfHFzg8gVAABARkSuAABAL+480B4RuQIAAMiIyBUAAOiNr79pELkCAADIaEeRq+H6mk69+EZJko3WknncE28X6Lid9el0tm3D4bZ52mWGegbNPaINLJ3fmjytejvye2izj8ez7emlzdn28NTJsvq1UfK4WHZM72p7S8iz468UaL1lo+nX2MexD9ptLsJ2k/9533VrWdrVVytkaLbjuMf00AddY7zK/OjUUa9PxiE9jLF3jEOrzDLP9OKlWdLkwnPJeiyUbWEerDKuNgrj0fVfvq65stP5UST6slVes3+wsb59eYeQrW1ocNNL5PE6GCbWsXptq/s49m9qfavLn+9jS8yB1PFVmsV66rlvifWpat/o259XHr5+oilqOKqa7Att8qosm44X6vNEPbP9qbaMtxbbNXc+s/OP/VKXEeoeXvf8amPYPk6SRnN/ouKakMrf1ZZWWuJ6qcc6dZ3M1x3XsTqt/vfU6cV9qXFNrYXzbV0WBop558dnxfk6Kz+2ZVn+FfDMVYPIFQAAQEY8cwUAAHrrjMAfQ0SuAAAAMiJyBQAAenHn3YIRkSsAAICMiFwBAIDeeLdgg8gVAABARkSuAABAbwUPXc0QuQIAAMiImysAAICMeFkQAAD04uKB9ojIFQAAQEZErgAAQD9O5CoicgUAAJARkSsAANCTqyB0NUPkCgAAICMiVwAAoDcv9rsFBweRKwAAgIyIXAEAgF7Kz7nimasakSsAAICMiFwBAIB+XCp45mqGyBUAAEBGRK4AAEBvPHPVIHIFAACQEZErAADQi0sqCFzNELkCAADIaEeRq+nmlp49+0VJ0vqZq2bpxXQ62x4Mh0vLsIHNtj3c5voKbzPofD23x1sUikn62NjOyeWt2fZg1Jzf6NLlxHHp+9Wd9NHCseNxU3441kOZLaENPmnyWGh7sTVp0lcYk2ce/5ok6fQLrg7HDZJ5fYX/vnSNd1f/xfyr5GmnN+2J47fdnIv1DNZGIb3pr+H6WjK9XU66f1t5Yrsm6XHtGpuucehKL6ry+4zfYeSblzX94z9sJybGbHaN1euNJca12hevzdnxtsP/s1ZtSI2HJdYKH5fX7ta5r5eHb6wv5qnWhta4VmmpMuu647563i6bD6k1aNb2xHXa9G3TrueePCepmZfxGi06roV2hYvts2H3GHStH60iE9dR3a7BqDm+/vsxWC/Xh/GFS00Z06LK05zDtFp3h1X+uK8uv+73uG++zak1Zdl6lurTpr5Qdmqu44rwsiAAAOjHj+5/zK4ELwsCAABkROQKAAD0xicxNIhcAQAAZETkCgAA9FbwzNUMkSsAAICMiFwBAIBe3J2vvwmIXAEAAGRE5AoAAPTmV/553kcOkSsAAICMuLkCAAC9Fe578pODmf20mbmZXR/S7jGzs2b2mJm9NqS/0swerva9w2z77wni5goAABwbZnaLpD8v6U9D2u2S7pb0Mkl3SnqXmdVfyvhuSW+SdFv1c+d2dXBzBQAAeqvfMbjbPxn8U0l/S1Is7C5J97n7prt/QdJZSXeY2Y2Srnb3T3pZ+QckvW67Cri5AgAAh8n1ZvZQ+HnTqgea2Q9L+pK7f3Zu102Svhh+f6JKu6nank9fincLAgCAXtz39BPaz7n7q7p2mtknJL0wsevtkt4m6b9IHZZI8yXpS3FzBQAAjgx3f00q3cy+W9Ktkj5bPZN+s6TfMbM7VEakbgnZb5b0ZJV+cyJ9KV4WBAAAvbnvzc+Vt88fdvdvcfeXuvtLVd44fa+7f1nSA5LuNrMNM7tV5YPrn3b3pySdN7NXV+8SfIOkD29XF5ErAABwrLn7I2Z2v6RHJU0kvcXdp9XuN0t6n6STkh6sfpbi5goAABw7VfQq/n6vpHsT+R6S9PKdlM3NFQAA6M337oH2A49nrgAAADIicgUAAHrxjF9NcxQQuQIAAMiIyBUAAOiNZ64aRK4AAAAyInIFAAB6I3LV2NnNlZmG6+UhNmi+bmegYZNlsPg1PLHD47aNwnGhjM7qQ9lmIegW0wfpYFyqXQvtiXlC+dPLl5v8k+lse7CxvrS9xXgy2x6tN10d215Mp0optsbNLx3nFNOrj/Iv2+jpPm4d2mpPR9+Ecrae25QkTS437fKiSB43HTfpg2Ecm9DGjn6P6YPQ9lhXHOOucW3NlVb5RdjuGPvK2qkTs+3pZhiP8FVTxaRj/EJ6VxujVfqj9dHEHWPWlSdVpk8nybxx3h4pg4EGV53U9PyFWVK97sTrxKvzt7Uly2PVX4P1sAbU87KeY6nrtmvcNLd2VWWk1rNia0uStPnMeUnScGOtaXs1/qlruimrOr/t5uWy86nSZnMpcX2m1of6mo7XR32d1dfY6ORGKGuujak+nWtL8rhEGZ64Rle5OYjnNajKqvs7rlm2Xo5LXGvVsWbGdqX6uy5/2fjW121rza/n0XDYma/1t6Pj7xF2jsgVAADoxyUCVw2euQIAAMiIyBUAAOjFxTNXEZErAACAjIhcAQCAnrz1JqjjjsgVAABARkSuAABAPy4VPHM1Q+QKAAAgI26uAAAAMuJlQQAA0BsPtDeIXAEAAGRE5AoAAPTCh4i2EbkCAADIiMgVAADox4lcRUSuAAAAMiJyBQAAenIVvFtwhsgVAABARkSuAABAbzxz1SByBQAAkBGRKwAA0IuLT2iPiFwBAABkROQKAAD041LBM1czRK4AAAAyInIFAAB6492CDSJXAAAAGXFzBQAAkNGOXhYsJlM995VvSJJO33hds2MQ7tHGhaR2eLCYTJPbUVc40afpYy3U6UWxQutXqKsj/dF/+dhs+zv+yrfNttdPrS/kLSZNW8aXtpLpNrBkejyP6TimN+2Kx3bp6uOuPovp7fxNXXUb1s+cbOoZT5J1DkbpsY/nMdmcJNO78rf7I903xdST6dHkclNvLDPyaZn+zOcuzNK+/UdfOtsebTSXzWA0TLY9nt90K11nzB/TB0NLpu9Uq59C39hw+Ry6+PTlK67zIJs8d1HnPvm7OvOSGxf2tftqWqWVfeZx/RqPy38ni+NSj2ddVnIOVm9VT61ZMf+yNe3pPzonSbrhO1+wkLeea/U5TC6PZ/vq63nruc0q7+J8TbVhsrm4nsTraF49Z+vrKNUP8Vq9+kWnJUnX3Hxtua91XUxbbU1d+6m2z9qQ6MfpVne7Yp/MS+cvyxqfL4/7M6/7M2Ff2fdxDMaXxtW+ss2xH+p9qXrq/k6tB3U/1/umm6GPxmVZ00tx3anmYKKsmG/nnI9iCIhcAQAAZMQD7QAAoBf3nb+KdJQRuQIAAMiIyBUAAOiNDxFtELkCAADIiMgVAADojXcLNohcAQAAZETkCgAA9OPO198ERK4AAAAyInIFAAB6cfHFzRGRKwAAgIyIXAEAgN4K5xPaa0SuAAAAMiJyBQAA+nGeuYqIXAEAAGTEzRUAAEBGvCwIAAB6cfEhohGRKwAAgIyIXAEAgN744uYGkSsAAICMiFwBAIB+XCoKPkS0RuQKAAAgIyJXAACgN94t2CByBQAAkBGRKwAA0IvL5Xxx8wyRKwAAgIyIXAEAgH744uaWHd1cXf6Wb9Pv/40PSZI+/6fNoRcvTZs81fbmZpO2uTmZbY9D+nTahBCn4yZ9ayvmH8+2i5A/prs1AzqdhPLHTTnTaUwPZYbJUIRji5D/bZ9+9Wz7331tbbZ94WJ57OXLTbu2tprtSxeb+rfC+V2+2NQ/iXVOimT6NKRvXd7qOKdmexLOL0728ebW9ukhrBv74+/+k/9IkvRbT50I5xTKaE5Vm5thnEKerXE4v7gdzm+81TE/pmGcWvNgksw/Cf0RbV7cTKZPEnPlR3/+u2ZpTzRdqkuXPWynxz5ux2shnt/m5abOON6x7UXHOXnR1QfpY1PXRRHGOs6H9e/YmG3rkZfpqLh0/bfqM3/tl/Slc2HtqsYyztnnLpZ9tXW5+jf0e93X9Xwcx7Wq2q7XptS41PtSa1Ncl+rxqvPF8b7m1udLku55662SpGcuN+N1aat8MWI8sfL8Ni2cV9nmelpMJs2Y12tYvM7qeTuuzn9rK/bDtH3OoR/q9aq+ptpzeDHtjT95myRpNKjGYtK0uT6PzXH5b5jG2tzy6jyqcw/X4qQ6j6L6N16P9TlOJotrUByzrn1xnak/NHM0GkqSfvonmuOL6oWhwpsXiLaK8u/H5el6WXbR7Ku3L0+GrXMv99Xn3/63zFf+G5bxmUubizc79Vyf76OyrGr7JzcWjsPOELkCAAC9Eblq8MwVAABARkSuAABAT9561OC4I3IFAACQETdXAAAAGXFzBQAAevHqoxj24qcPM/s5M/uSmX2m+vkvw757zOysmT1mZq8N6a80s4erfe8wM0uX3uDmCgAAHCf/1N1fUf18VJLM7HZJd0t6maQ7Jb3LzIZV/ndLepOk26qfO7ergAfaAQBAb/Hz2A6huyTd5+6bkr5gZmcl3WFmj0u62t0/KUlm9gFJr5P04LLCiFwBAIDD5Hozeyj8vGmHx7/VzH7PzN5rZtdWaTdJ+mLI80SVdlO1PZ++FJErAADQz95+/c05d39V104z+4SkFyZ2vV3lS3x/T5JX//4TSX9VUuo5Kl+SvhQ3VwAA4Mhw99esks/M/oWkj1S/PiHplrD7ZklPVuk3J9KX4mVBAADQk8u92JOfPszsxvDrj0j6XLX9gKS7zWzDzG5V+eD6p939KUnnzezV1bsE3yDpw9vVQ+QKAAAcF//QzF6h8qW9xyX9dUly90fM7H5Jj0qaSHqLu9dfFf5mSe+TdFLlg+xLH2aXuLkCAAA9uaTiEHxxs7u/fsm+eyXdm0h/SNLLd1IPLwsCAABkROQKAAD044f+c66yInIFAACQEZErAADQU//v/TtKiFwBAABkROQKAAD01vczqI4SIlcAAAAZcXMFAACQES8LAgCAfvb2i5sPPCJXAAAAGRG5AgAAvbicDxENiFwBAABkZO6rv0ZqZuclPbZ7zVnqeknn9qlu6t/f+o/zue93/S9x9xv2qe6s9mj92qux2ot6OJeDV8de1bPj697MfkVl2/bCOXe/c4/quiI7vbl6yN1ftYvtOZB1Uz9jf5zrPyr2oh/3aqw4l+NZx17Wg354WRAAACAjbq4AAAAy2unN1Xt2pRUHv27qZ+yPc/1HxV70416NFedyPOvYy3rQw46euQIAAMByvCwIAACQETdXAAAAGa10c2Vmd5rZY2Z21sx+ZjcbtF1dZvb9ZvZNM/tM9fN3drk97zWzr5rZ53aznlXq2odzv8XM/r2Zfd7MHjGz/34/69qH8z9hZp82s89Wbfq7+1nXXp//UZJ7DduvdSr3erRfa85urC37tYbsxjrBenAEuPvSH0lDSX8k6VslrUv6rKTbtzvuSn5WqUvS90v6yG7U39Gm/0TS90r63H7XtQ/nfqOk7622z0j6g10c+23r2ofzN0mnq+01Sb8l6dX7Vdden/9R+cm9hu3nOpV7PdqvNWc31pb9WkN2Y51gPTj8P6tEru6QdNbd/9jdtyTdJ+muFY67EntZ10rc/Tckff2o1bUKd3/K3X+n2j4v6fOSbjrsde2gTe7uF6pf16qfXXkHyF7WdQzlXlf2bZ3KvUbs15qzG9f7fq0hu3Htsh4cfqvcXN0k6Yvh9ye0exN21bq+rwqXPmhmL9ulthxU+3LuZvZSSX9W5f+g9rOuPT1/Mxua2WckfVXSx919185/xbqO89y/UrnXsOO2Tu3qeezG2rLXa8hurBOsB4fbKjdXlkjbrTvoVer6HZXfe/QfSHqnpP97l9pyEO3LuZvZaUn/RtLfdPdn97GuPT9/d5+6+ysk3SzpDjN7+T7WdZznfh+517DjtE7t6nnsxtqyH2vIbqwTrAeH2yo3V09IuiX8frOkJ3enOdvX5e7P1uFSd/+opDUz26svi9xX+3HuZramcqH61+7+f+1nXfs59u7+jKRfl7TrXxbaVddxnvs95V7Djs06tZvnsRtry36vIbuxTrAeHE6r3Fz9tqTbzOxWM1uXdLekB3apPdvWZWYvNDOrtu9QeQ5P71J7DpS9Pveqrl+Q9Hl3//ndqmfVuvbh/G8ws+dV2yclvUbS7+9XXcd57veUew07NuvUbp3Hbqwt+7WG7MY6wXpw+I22y+DuEzN7q6SPqXyXzHvd/ZHdaExXXWb2U9X+fy7pL0l6s5lNJF2SdLe779qDfmb2iyrflXG9mT0h6Wfd/Rf2qi6VDzLuy7lL+o8lvV7Sw9Vr/5L0tup/SXtSl6QXS/t2/jdKer+ZDVUuXPe7+0f2sq79nPtHRe41bD/Xqdzr0T6uObuxtuzXGrIb6wTrwSHH198AAABkxCe0AwAAZMTNFQAAQEbcXAEAAGTEzRUAAEBG3FwBAABkxM3VMWRmz7fmm9S/bGZfqrYvmNm79rt9AA4n1hagxEcxHHNm9nOSLrj7P97vtgA4OlhbcJwRucKMmX2/mX2k2v45M3u/mf2qmT1uZj9qZv/QzB42s1+x8msmZGavNLP/x8z+PzP7mJnduL9nAeCgYW3BccPNFZb5Nkk/KOkuSf9K0r939+9W+WnAP1gtgu+U9Jfc/ZWS3ivp3v1qLIBDg7UFR9q2X3+DY+1Bdx+b2cMqv+bjV6r0hyW9VNJ3Snq5pI9XX3E1lPTUPrQTwOHC2oIjjZsrLLMpSe5emNk4fG9VoXLumKRH3P379quBAA4l1hYcabwsiD4ek3SDmX2fJJnZmpm9bJ/bBODwY23BocbNFa6Yu2+p/Gb2f2Bmn5X0GUl/bl8bBeDQY23BYcdHMQAAAGRE5AoAACAjbq4AAAAy4uYKAAAgI26uAAAAMuLmCgAAICNurgAAADLi5goAACCj/x9yksJsOsEZlQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "y, sr = librosa.load(\"notes.wav\", sr=None, mono=True)\n",
        "mfcc_mono = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "y, sr = librosa.load(\"chords.wav\", sr=None, mono=True)\n",
        "mfcc_poly = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "\n",
        "fig, axs = plt.subplots(1, 2)\n",
        "mono_img = librosa.display.specshow(mfcc_mono, x_axis='time', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[0])\n",
        "poly_img = librosa.display.specshow(mfcc_poly, x_axis='time', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[1])\n",
        "fig.colorbar(poly_img, ax=axs[1])\n",
        "axs[0].set(title='mono')\n",
        "axs[1].set(title='poly')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'poly')]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "y, sr = librosa.load(\"mute_notes.wav\", sr=None, mono=True)\n",
        "mfcc_mono = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "y, sr = librosa.load(\"mute_chords.wav\", sr=None, mono=True)\n",
        "mfcc_poly = librosa.feature.mfcc(y, sr, n_fft=FRAME_SIZE, hop_length=HOP_SIZE, center=False, fmax=F_MAX)\n",
        "\n",
        "fig, axs = plt.subplots(1, 2)\n",
        "mono_img = librosa.display.specshow(mfcc_mono, x_axis='time', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[0])\n",
        "poly_img = librosa.display.specshow(mfcc_poly, x_axis='time', hop_length=HOP_SIZE, sr=sr, fmax=F_MAX, ax=axs[1])\n",
        "fig.colorbar(poly_img, ax=axs[1])\n",
        "axs[0].set(title='mono')\n",
        "axs[1].set(title='poly')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Mono F0 estimation confidence\n",
        "Essentia provides PitchYin that returns a confidence value for each frame. Models were trained on the confidence values as the feature. The models were run on Colab and did not achieve satisfactory results. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train sklearn classifier\n",
        "**Run the training on Colab or CC. Do not train on local machine.**\n",
        "\n",
        "Models trained on MFCC features have much better performance than those trained on Mel-spectra. Below shows the code used for training and testing the SVC models. \n",
        "\n",
        "RBFSVM always yielded better result than linear models, even when using less training data. \n",
        "| Features      | Linear (LR, SVM) | SVC(RBF)\n",
        "| ----------- | -----------        | -----------\n",
        "| Mel-spectrum| ~70%               | ~90%\n",
        "| MFCC        | ~82%               | ~98%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['arr_0']\n",
            "['arr_0']\n"
          ]
        }
      ],
      "source": [
        "mono = np.load(\"/content/drive/MyDrive/THESIS CODE/mono_mfcc_40.npz\")\n",
        "poly = np.load(\"/content/drive/MyDrive/THESIS CODE/poly_mfcc_40.npz\")\n",
        "print(mono.files)\n",
        "print(poly.files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# load the features and labels\n",
        "with np.load(\"/content/drive/MyDrive/THESIS CODE/mono_mfcc_40.npz\") as data:\n",
        "    mono = data[\"arr_0\"]\n",
        "print(mono.shape)\n",
        "with np.load(\"/content/drive/MyDrive/THESIS CODE/poly_mfcc_40.npz\") as data:\n",
        "    poly = data[\"arr_0\"]\n",
        "print(poly.shape)\n",
        "\n",
        "data = np.concatenate((mono, poly), axis=1)\n",
        "print(data.shape)\n",
        "\n",
        "X = data[:-1, :]\n",
        "X = X.transpose()   # transpose so that X and y have the same size on the first dimension \n",
        "print(X.shape)\n",
        "y = data[-1, :]\n",
        "print(y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score, cross_validate, GridSearchCV\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.svm import LinearSVC, SVC\n",
        "# from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "# from sklearn.utils import shuffle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# use 80% as development set, 20% as evaluation set\n",
        "X_dev, X_eval, y_dev, y_eval = train_test_split(X, y, test_size=0.2, shuffle=True, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# normal cv, display multiple scoring metrics\n",
        "pipe = make_pipeline(StandardScaler(), SVC())\n",
        "scoring = [\"accuracy\", \"precision\", \"recall\", \"f1\"]\n",
        "results = cross_validate(pipe, X_dev[:10000], y_dev[:10000], cv=5, n_jobs=-1, scoring=scoring, return_estimator=True)\n",
        "results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# grid search cv, find best hyperparameters, use only one metric\n",
        "svc = make_pipeline(StandardScaler(), SVC())\n",
        "param_grid = {\n",
        "    \"svc__C\": [0.1, 1, 10],\n",
        "    \"svc__gamma\": [\"scale\", \"auto\"]\n",
        "    }\n",
        "search = GridSearchCV(svc, param_grid, scoring=\"f1\", n_jobs=-1, refit=True, cv=3)\n",
        "search.fit(X_dev[:100000], y_dev[:100000])\n",
        "print(\"------------BEST_PARAMS--------\")\n",
        "print(search.best_params_)\n",
        "print(\"------------BEST_SCORE---------\")\n",
        "print(search.best_score_)\n",
        "print(\"------------EVAL_DATA----------\")\n",
        "y_pred = search.predict(X_eval)\n",
        "print(accuracy_score(y_eval, y_pred))\n",
        "print(f1_score(y_eval, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# final training on the entire training set, using the best params combination from grid search\n",
        "svc = make_pipeline(StandardScaler(), SVC(C=10, gamma=\"scale\"))\n",
        "svc = svc.fit(X_dev, y_dev)\n",
        "print(\"------------EVAL_DATA----------\")\n",
        "y_pred = svc.predict(X_eval)\n",
        "print(accuracy_score(y_eval, y_pred))\n",
        "print(f1_score(y_eval, y_pred))\n",
        "# this was run on CC for ~8h, result: \n",
        "# Accu: 0.9922869574445505\n",
        "# F1: 0.9917375665610179"
      ]
    }
  ],
  "metadata": {
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