notebooks/music_generation_tone_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Music Generation using Algorithmic Approch\n",
    "I utilized a modified version of the 16x16 tone matrix concept to generate music. Specifically, I processed an image and used it as a switch to activate specific elements in the tone matrix.\n",
    "\n",
    "Reference for tone matrix: https://tonematrix.maxlaumeister.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to play the midi file in nootebook\n",
    "!pip install midi-player -q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Dependencies\n",
    "\n",
    "- The **OpenCV** library is used for image manipulation: [OpenCV on PyPI](https://pypi.org/project/opencv-python/)\n",
    "- The **Music21** library is implemented for processing symbolic music representations: [Music21 on PyPI](https://pypi.org/project/music21/)\n",
    "- **NumPy** is utilized for performing array and matrix operations: [NumPy on PyPI](https://pypi.org/project/numpy/)\n",
    "- The **Matplotlib** library is incorporated for displaying and saving high-quality images in various dimensions: [Matplotlib on PyPI](https://pypi.org/project/matplotlib/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import music21 as m21\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEQ_LEN = 32\n",
    "IMAGE_PATH = './../images/Warren-Buffett-On-Rules.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aI3R3GFuPcmR",
    "outputId": "6584534b-78c9-44e1-ccfd-adfb3ec0f7db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of one space image: (16, 32)\n"
     ]
    }
   ],
   "source": [
    "# Load the image\n",
    "img = cv2.imread(IMAGE_PATH)\n",
    "\n",
    "# Resize the image to 16x16 pixels\n",
    "resized_img = cv2.resize(img, (SEQ_LEN, 16))\n",
    "\n",
    "# Convert the image to grayscale\n",
    "gray_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# for working with different color scale, split image into b, g, r separately\n",
    "b, r, g = cv2.split(resized_img)\n",
    "print(f\"Shape of one space image: {b.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting intensity information column-wise\n",
    "The algorithm extracts the intensity information from the image column-wise. For each column, it calculates either the maximum intensity or the minimum intensity value according to user choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_intensity_columnwise(resized_img, seq_len=16, approach='max'):\n",
    "    \"\"\"\n",
    "    Get the intensity values of the resized image.\n",
    "\n",
    "    Args:\n",
    "        resized_img: numpy array\n",
    "            The resized image as a 2D array with only one color scale.\n",
    "        seq_len: int, optional (default: 16)\n",
    "            The length of the generated music sequence. The width of the image is divided into this many segments.\n",
    "        approach: str, optional (default: 'max')\n",
    "            The approach to use when mapping pixel intensity to musical notes. 'max' uses the maximum pixel intensity,\n",
    "            while 'average' uses the average intensity.\n",
    "\n",
    "    Returns:\n",
    "        column_index: list\n",
    "            A list of the indices of the maximum (or average) intensity value in each column of the image.\n",
    "    \"\"\"\n",
    "    if approach not in ['max', 'min']:\n",
    "        raise ValueError(\"The 'approach' argument must be either 'max' or 'min'.\")\n",
    "    \n",
    "    intensity_values = resized_img.flatten()\n",
    "    print(f'Shape of intensity_values: {intensity_values.shape} and resized image_shape: {resized_img.shape}')\n",
    "\n",
    "    # Transpose the intensity values to get the values column-wise\n",
    "    intensity_values_column_wise = [intensity_values[i::seq_len] for i in range(seq_len)]\n",
    "\n",
    "    column_index = []\n",
    "    \n",
    "    # Print the index of the intensity values column-wise\n",
    "    for i, col in enumerate(intensity_values_column_wise):\n",
    "        # print(f'Index: {i} and Length of col: {len(col)}')\n",
    "        if approach == 'max':\n",
    "            indices = np.where(col == col.max())[0].tolist()\n",
    "        elif approach == 'min':\n",
    "            indices = np.where(col == col.min())[0].tolist()\n",
    "        column_index.append(indices)\n",
    "        # print(f'Column {i}: {col} and maxindex: {indices}')\n",
    "    return column_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of intensity_values: (512,) and resized image_shape: (16, 32)\n"
     ]
    }
   ],
   "source": [
    "max_intensity_list = get_intensity_columnwise(b, seq_len=SEQ_LEN)\n",
    "# min_intensity_list = get_intensity_columnwise(b, seq_len=16, approach='min')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot images with separate color space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "lKVtNY2llL_L"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x3200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,SEQ_LEN))\n",
    "images_color_space = (b, g, r)\n",
    "image_label = ['Blue', 'Green', 'Red']\n",
    "for index in range(3):\n",
    "    plt.subplot(1, 3, index+1)\n",
    "    plt.title(f\"{image_label[index]} Color Space\")\n",
    "    plt.imshow(images_color_space[index], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "1b0e9cff-b19d-4e7f-800b-f81cbfe9f7a8.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tone matrix Configuration\n",
    "The Tone matrix is a 2D matrix where the horizontal axis represents time and the vertical axis represents frequency. As we move from bottom to top on the vertical axis, the frequency increases. Similarly, as we move from left to right on the horizontal axis, time increases. The matrix consists of 16 fixed notes, where each note is assigned a unique row ranging from the lower frequency note C3 to the upper frequency note C6.\n",
    "![tone_matrix.png](attachment:1b0e9cff-b19d-4e7f-800b-f81cbfe9f7a8.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "gCdZKkfuSmXP"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tone matrix shape: (16, 32)\n"
     ]
    }
   ],
   "source": [
    "# make 16x16 tone matrix\n",
    "switch = np.zeros((16, SEQ_LEN), dtype='object')\n",
    "notes = ['C6', 'A5', 'G5', 'F5', 'D5', 'C5', 'A4', 'G4',\n",
    "        'F4', 'D4', 'C4', 'A3', 'G3', 'F3', 'D3', 'C3']\n",
    "\n",
    "for i in range(16):\n",
    "  switch[i, :] = notes[i]\n",
    "print(f\"Tone matrix shape: {switch.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct Switch configuration\n",
    "If there are more switches that are to be opened in column-wise, then switch with least frequency is played. Here, column with highest index has lowest frequency\n",
    "\n",
    "The pure black and white image is constructed by interchanging rows and columns for easier calculation. It has dimension Nx16. To get image with dimension equal to tone matrix, we transpose the array to get dimension 16xN. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 917
    },
    "id": "ih7P5G6KQK2r",
    "outputId": "276344cf-5b91-48bc-df80-09f0e92fc6f4"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def construct_switch_config(intensity, seq_len=16, config='s'):\n",
    "    '''\n",
    "    Constructs a binary image based on the given intensity values and configuration.\n",
    "\n",
    "    Args:\n",
    "        intensity: np.array or list -> A list containing a list of intensity indices.\n",
    "        seq_len: int -> The length of the time axis. Default value is 16.\n",
    "        config: str -> Configuration for the switch matrix. Possible values are:\n",
    "                's' (single): Only one note can be activated in a column at a time.\n",
    "                'two': A maximum of two notes can be played at a time.\n",
    "                'three': A maximum of three notes can be played at a time.\n",
    "                'all': No limit to the number of notes that can be played at a time.\n",
    "\n",
    "    Returns:\n",
    "        bw_image: np.array -> A 2D array representing the binary image where 255 represents the ON state \n",
    "                              and 0 represents the OFF state.\n",
    "    '''\n",
    "    bw_image = np.zeros((seq_len, 16), dtype='uint8')\n",
    "\n",
    "    for i, index_col in enumerate(intensity):\n",
    "        if config.lower() in ['s', 'single']:\n",
    "            if index_col:\n",
    "                bw_image[i, index_col[-1]] = 255\n",
    "\n",
    "        elif config.lower() == 'two':\n",
    "            if index_col:\n",
    "                print(index_col)\n",
    "                bw_image[i, index_col[-1]] = 255\n",
    "                if len(index_col) > 1:\n",
    "                    bw_image[i, index_col[-2]] = 255\n",
    "\n",
    "        elif config.lower() == 'all':\n",
    "            bw_image[i, index_col] = 255\n",
    "\n",
    "    return bw_image.T  # Transpose to get the desired orientation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bw_image = construct_switch_config(max_intensity_list, seq_len=SEQ_LEN, config='all')\n",
    "plt.figure(figsize=(16, 16))\n",
    "plt.imshow(bw_image, cmap='gray')\n",
    "plt.yticks(range(1, 16))\n",
    "# plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating melody\n",
    "The tone matrix is configured with a switch setting (black and white) to produce the melody."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Qz46AnV7jpLW"
   },
   "outputs": [],
   "source": [
    "def construct_melody(image, tone_matrix, instrument='Piano', seq_len=16):\n",
    "    \"\"\"\n",
    "    Generate a Music21 Stream object.\n",
    "\n",
    "    Args:\n",
    "        image: numpy array or list, shape (16, seq_len)\n",
    "            The image that serves as the basis for the melody.\n",
    "        tone_matrix: numpy array or list, shape (16, seq_len)\n",
    "            The tone matrix that corresponds to the image.\n",
    "        instrument: str, optional (default: 'Piano')\n",
    "            The instrument to use when creating the melody.\n",
    "        seq_len: int, optional (default: 16)\n",
    "            The length of the melody, in number of notes.\n",
    "\n",
    "    Returns:\n",
    "        A Music21 Stream object representing the generated melody.\n",
    "    \"\"\"\n",
    "    stream_algo = m21.stream.Stream()\n",
    "    instrument_obj = m21.instrument.fromString(instrument)\n",
    "    stream_algo.insert(0.0, instrument_obj)\n",
    "    offset = 0.0\n",
    "    \n",
    "    for y in range(seq_len):\n",
    "        notes_to_add = []\n",
    "        for x in range(16):\n",
    "            if image[x][y] != 0:\n",
    "                notes_to_add.append(tone_matrix[x][y])\n",
    "        \n",
    "        if notes_to_add:\n",
    "            chord_or_note = m21.chord.Chord(notes_to_add) if len(notes_to_add) > 1 else m21.note.Note(notes_to_add[0])\n",
    "            stream_algo.insert(offset, chord_or_note)\n",
    "            # print(f\"Inserted {'Chord' if len(notes_to_add) > 1 else 'Note'}: {notes_to_add} at offset {offset}\")\n",
    "\n",
    "        offset += 0.5 if len(notes_to_add) == 1 else 1.0\n",
    "    \n",
    "    return stream_algo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save in midi format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "MxPm7jiHicNy",
    "outputId": "731575d3-7927-49cf-ec73-5c6f1f00221e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'music_tone_matrix.mid'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MIDI_OUTPUT_FILE = 'music_tone_matrix.mid'\n",
    "stream_algo = construct_melody(image=bw_image, tone_matrix=switch, seq_len=SEQ_LEN)\n",
    "\n",
    "# stream_algo.show('text')\n",
    "stream_algo.write('midi', MIDI_OUTPUT_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe srcdoc=\"&lt;script src=&quot;https://cdn.jsdelivr.net/combine/npm/tone@14.7.58,npm/@magenta/music@1.23.1/es6/core.js,npm/focus-visible@5,npm/html-midi-player@1.5.0&quot;&gt;&lt;/script&gt;\n",
       "&lt;style&gt;\n",
       "/* Custom player style */\n",
       "#section903 midi-player {\n",
       "  display: block;\n",
       "  width: inherit;\n",
       "  margin: 4px;\n",
       "  margin-bottom: 0;\n",
       "  font-family: Arial;\n",
       "}\n",
       "p { margin:0 }\n",
       "\n",
       "#section903 midi-player::part(control-panel) {\n",
       "  background: #ff5;\n",
       "  border: 2px solid #000;\n",
       "  border-radius: 10px 10px 0 0;\n",
       "}\n",
       "#section903 midi-player::part(play-button) {\n",
       "  color: #353;\n",
       "  border: 2px solid currentColor;\n",
       "  background-color: #4d4;\n",
       "  border-radius: 20px;\n",
       "  transition: all 0.2s;\n",
       "  content: &#x27;hello&#x27;;\n",
       "}\n",
       "#section903 midi-player::part(play-button):hover {\n",
       "  color: #0a0;\n",
       "  background-color: #5f5;\n",
       "  border-radius: 10px;\n",
       "}\n",
       "#section903 midi-player::part(time) {\n",
       "  font-family: monospace;\n",
       "}\n",
       "\n",
       "/* Custom visualizer style */\n",
       "#section903 midi-visualizer .piano-roll-visualizer {\n",
       "  background: #ffd;\n",
       "  border: 2px solid black;\n",
       "  border-top: none;\n",
       "  border-radius: 0 0 10px 10px;\n",
       "  margin: 4px;\n",
       "  width: inherit;\n",
       "  margin-top: 0;\n",
       "  overflow: auto;\n",
       "}\n",
       "#section903 midi-visualizer svg rect.note {\n",
       "  opacity: 0.6;\n",
       "  stroke-width: 2;\n",
       "}\n",
       "#section903 midi-visualizer svg rect.note[data-instrument=&quot;0&quot;]{\n",
       "  fill: #e22;\n",
       "  stroke: #500;\n",
       "}\n",
       "#section903 midi-visualizer svg rect.note[data-instrument=&quot;2&quot;]{\n",
       "  fill: #2ee;\n",
       "  stroke: #055;\n",
       "}\n",
       "#section903 midi-visualizer svg rect.note[data-is-drum=&quot;true&quot;]{\n",
       "  fill: #888;\n",
       "  stroke: #888;\n",
       "}\n",
       "#section903 midi-visualizer svg rect.note.active {\n",
       "  opacity: 0.9;\n",
       "  stroke: #000;\n",
       "  stroke-width: 3;\n",
       "}\n",
       "&lt;/style&gt;\n",
       "\n",
       "          &lt;section id=&quot;section903&quot;&gt;&lt;p style=&quot;text-align:left;font-family:Arial;&quot;&gt;My Player&lt;span style=&quot;float:right;&quot;&gt;&lt;a href=&quot;data:audio/midi;base64,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&quot; target=&quot;_blank&quot;&gt;Download MIDI&lt;/a&gt;&lt;br&gt;&lt;/span&gt;&lt;/p&gt;\n",
       "          &lt;midi-player src=data:audio/midi;base64,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 sound-font visualizer=&quot;#section903 midi-visualizer&quot;&gt;&lt;/midi-player&gt;\n",
       "          &lt;midi-visualizer src=data:audio/midi;base64,TVRoZAAAAAYAAQACJ2BNVHJrAAAAFAD/UQMHoSAA/1gEBAIYCM5g/y8ATVRyawAAAlUA/wMFUGlhbm8AwAAA4ABAAMAAAJA3WqcwkFFaAJBPWgCQSloAkEhapzCANwCnMIBRAACATwAAgEoAAIBIAACQSFoAkEVazmCASAAAgEUAAJBKWqcwkDxapzCASgAAkDlapzCAPAAAkDdapzCAOQAAkDdaAJA1WqcwgDcApzCANwAAgDUAAJA3WqcwkE9apzCANwAAkE9apzCATwAAkDVapzCATwAAkEpapzCANQAAkDxapzCASgAAkEVapzCAPAAAkFFaAJBNWgCQRVoAkD5aAJA1WgCQMlqnMIBFAKcwgFEAAIBNAACARQAAgD4AAIA1AACAMgAAkEpaAJBDWgCQPFoAkDVaAJAyWs5ggEoAAIBDAACAPAAAgDUAAIAyAACQVFoAkEhaAJBFWgCQQVoAkDlaAJAyWgCQMFrOYIBUAACASAAAgEUAAIBBAACAOQAAgDIAAIAwAACQUVoAkEVaAJBDWgCQPloAkDdaAJAwWs5ggFEAAIBFAACAQwAAgD4AAIA3AACAMAAAkDBapzCQPFqnMIAwAACQPFqnMIA8AACQPFqnMIA8AACQPlqnMIA8AACQPFoAkDBapzCAPgCnMIA8AACAMAAAkDxapzCQPlqnMIA8AACQPFqnMIA+AACQQVqnMIA8AACQRVqnMIBBAACQUVoAkEFaAJA+WgCQOVoAkDJaAJAwWqcwgEUApzCAUQAAgEEAAIA+AACAOQAAgDIAAIAwAACQT1oAkD5aAJA8WgCQN1oAkDBazmCATwAAgD4AAIA8AACANwAAgDAAzmD/LwA= type=piano-roll&gt;&lt;/midi-visualizer&gt;\n",
       "          &lt;/section&gt;\n",
       "          \" width=\"100%\" height=\"160\"\n",
       "            style=\"border:none !important;\"\n",
       "            \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\">'\n",
       "            </iframe>"
      ],
      "text/plain": [
       "<midi_player.midi_player.MIDIPlayer at 0x742a4df1bca0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from midi_player import MIDIPlayer\n",
    "from midi_player.stylers import basic, cifka_advanced\n",
    "\n",
    "midi_file = 'music_tone_matrix.mid'\n",
    "\n",
    "MIDIPlayer(midi_file, 160, styler=cifka_advanced, title='My Player')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}