ScoreAnalysisTutorial.ipynb

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      "name": "ScoreAnalysisTutorial.ipynb",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tuomaseerola/music21/blob/master/ScoreAnalysisTutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Music21 Score Analysis Tutorial**\n",
        "\n",
        "*Tuomas Eerola, Durham University*"
      ],
      "metadata": {
        "id": "a9t7MxklwMKM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1 Build music21 environment in Colab**\n",
        "\n",
        "First we build the virtual machine needed to run music21 in our browser."
      ],
      "metadata": {
        "id": "kcsiaF3NwXOL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1.1 Install Music21 and other elements needed to run the environment**\n",
        "Press play and wait for all commands to be executed - this initial command might take some time as it needs to build the musi21 environment."
      ],
      "metadata": {
        "id": "cPRISsx2wehr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade music21\n",
        "!add-apt-repository ppa:mscore-ubuntu/mscore-stable -y\n",
        "!apt-get update\n",
        "!apt-get install musescore\n",
        "!apt-get install xvfb\n",
        "!sh -e /etc/init.d/x11-common start\n",
        "import os\n",
        "os.putenv('DISPLAY', ':99.0')\n",
        "!start-stop-daemon --start --pidfile /var/run/xvfb.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1024x768x24 -ac +extension GLX +render -noreset\n",
        "from music21 import *\n",
        "us = environment.UserSettings()\n",
        "us['musescoreDirectPNGPath'] = '/usr/bin/mscore'\n",
        "us['directoryScratch'] = '/tmp'"
      ],
      "metadata": {
        "id": "bI7LiDuuwm1Y",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Let's check that our environment works"
      ],
      "metadata": {
        "id": "pi_5kXujxaiX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will do this by trying to import one of the scores available in the music21 corpus utilising the following commands:"
      ],
      "metadata": {
        "id": "lUt1GUrx00C4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from music21 import *\n",
        "opus132 = corpus.parse('beethoven/opus132.mxl') # We \"parse\" one specific work from the corpus which in this case will be a work by Beethoven\n",
        "opus132.measures(1, 4).show() # This command lets us specify the number of bars (measures) we want to display, in this case we want to see bars 1 to 4"
      ],
      "metadata": {
        "id": "5YBeSoysxi1Z",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2 Load music\n",
        "\n",
        "We will first try to load a score from the built-in corpus that comes with music21. To do this we will be using the same command as the one above. To be able to load a score, you need to know the name of the score.\n",
        "A list of available scores can be found at [https://www.music21.org/music21docs/about/referenceCorpus.html](https://www.music21.org/music21docs/about/referenceCorpus.html) and it contains a lot of *Bach*, some\n",
        "*Beethoven*, *Chopin*, *Handel*, *Haydn*, *Mozart*, *Palestrina*, *Monteverdi*, *Josquin des Prez*, *Schubert*,\n",
        "*Schumann* (both Clara and Robert) and thousands of folk songs."
      ],
      "metadata": {
        "id": "7aYjlhrBx-To"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 Loading a score from the Music21 library"
      ],
      "metadata": {
        "id": "yTb44HxZyUxs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from music21 import * # Every Music21 session will have to start with this definition\n",
        "opus133 = corpus.parse('beethoven/opus133.mxl') # We create an object and name it whatever we like - in this case \"opus133\" to match the score we will load, and then attribute the score loading command to it\n",
        "opus133.measures(1, 4).show() # Show the first 4 bars of the opus"
      ],
      "metadata": {
        "id": "Nu-OWAWQyaHE",
        "collapsed": true,
        "outputId": "8cf147df-8201-439a-9c53-facfdcb46545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 748,
              "height": 386
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "opus133violin = opus133.getElementById('1st Violin') # We create a new object and select only the 1st violin part of the score\n",
        "opus133violin.measures(1,8).show() # We write this code to show the first 8 bars of the 1st violin"
      ],
      "metadata": {
        "id": "NuNuzYNxy6Fg",
        "collapsed": true,
        "outputId": "4a7dae19-9527-4253-a41b-06780ed0932b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 748,
              "height": 164
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also play back the score using MIDI (if the browser supports this)."
      ],
      "metadata": {
        "id": "oCn71sJstyz9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "opus133violin.measures(1,8).show(\"midi\") # Same output as above but \"midi\" added"
      ],
      "metadata": {
        "id": "USV7njYOt6ms",
        "outputId": "501f7ef6-5e6c-420e-9f97-03a8141a6218",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "        <div id=\"midiPlayerDiv227251\"></div>\n",
              "        <link rel=\"stylesheet\" href=\"https://cuthbertLab.github.io/music21j/css/m21.css\">\n",
              "        \n",
              "        <script\n",
              "        src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"\n",
              "        ></script>\n",
              "    \n",
              "        <script>\n",
              "        function midiPlayerDiv227251_play() {\n",
              "            const rq = require.config({\n",
              "                paths: {\n",
              "                    'music21': 'https://cuthbertLab.github.io/music21j/releases/music21.debug',\n",
              "                }\n",
              "            });\n",
              "            rq(['music21'], function(music21) {\n",
              "                mp = new music21.miditools.MidiPlayer();\n",
              "                mp.addPlayer(\"#midiPlayerDiv227251\");\n",
              "                mp.base64Load(\"data:audio/midi;base64,TVRoZAAAAAYAAQACJ2BNVHJrAAAAGgD/UQMEPRIA/1kCAQAA/1gEBgMYCM5g/y8ATVRyawAAAGoA/wMKMXN0IFZpb2xpbgDAKADgAEAAwCjOYIA3AACAQwAAkDdaAJBDWgCQT1qC4jCATwAAkEN+hM5QgEMAAJBEfoHsIIBEAACQTX+B7CCATQAAkEx/gewggEwAAJBEf4HsIIBEAM5g/y8A\");\n",
              "            });\n",
              "        }\n",
              "        if (typeof require === 'undefined') {\n",
              "            setTimeout(midiPlayerDiv227251_play, 2000);\n",
              "        } else {\n",
              "            midiPlayerDiv227251_play();\n",
              "        }\n",
              "        </script>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Learning Task**\n",
        "\n",
        "Edit the code (which is a copy of the above) to show the first 5 bars of only the Cello part for Opus 133.\n",
        "Remember to give the object a new name (rename from opus133violin)."
      ],
      "metadata": {
        "id": "7sQh4mFK2ME0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "opus133violin = opus133.getElementById('Cello')\n",
        "opus133violin.measures(1,5).show()"
      ],
      "metadata": {
        "id": "oeWu1-AI4p4H",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 Load a score from an online corpus\n",
        "\n",
        "You can also import any score from available online collections such as **kernscores** or **musedata**. As mentioned in last week's lecture, kernscores has 100,000+ scores available, which can be accessed here: http://kern.ccarh.org. Musescore is another source which has thousands of works, see http://www.musedata.org, and there\n",
        "is a new openscore initiative which holds vasts amounts of music, see https://musescore.com/openscore"
      ],
      "metadata": {
        "id": "ZldnbEt25Clc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Here's an example of how to load the Prelude from J.S. Bach's Suite No. 1 in G major from Kernscores:\n",
        "environment.set('autoDownload', 'allow') # We just allow the software to access the internet\n",
        "no1 = converter.parse('https://kern.humdrum.org/cgi-bin/ksdata?location=users/craig/classical/bach/cello&file=bwv1007-01.krn')\n",
        "no1.metadata.movementName=\"J.S. Bach Suite No. 1 in G major, BWV 1007\" # Add title\n",
        "no1.measures(1, 8).show() # show first 8 bars\n",
        "no1.measures(1,3).show(\"midi\") # create midi player object as well\n"
      ],
      "metadata": {
        "id": "sX_80t8y5qZN",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.3 Create a simple score**\n",
        "\n",
        "In Music21 you can also create simple scores using a shorthand called tinynotation. Below are some examples, first showing the time signature, then the notes with their note durations."
      ],
      "metadata": {
        "id": "fGZo54kQ_Mmm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ex1 = converter.parse(\"tinynotation: 3/4 c4 e8 g a-16 g f# g c'2 r4\")\n",
        "ex1.show()\n",
        "ex1.show(\"midi\")\n"
      ],
      "metadata": {
        "id": "EecCdYIR_gwl",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Question #1\n",
        "\n",
        "Can you decipher what the numbers after the notes in the above code mean (c4, e8)?\n",
        "*Hint:* Music21 follows American terminologies e.g., half note = minim, quarter note = crotchet."
      ],
      "metadata": {
        "id": "SNjvzD4N_1lC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ex2 = converter.parse(\"tinynotation: 4/4 c4 c## cn c- c-- c trip{c8 d e}\")\n",
        "ex2.show()"
      ],
      "metadata": {
        "id": "0TUkH7UXAnFI",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Question #2\n",
        "\n",
        "How are sharp, flat, and neutral signs denoted in tinynotation?"
      ],
      "metadata": {
        "id": "ChRIxUoiA-V5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Learning Task**\n",
        "\n",
        "Can you re-create the following sequence using tinynotation?\n",
        "*Hint:* To denote a C one octave higher than the middle C, add a ' after c. Remember to add the duration of the notes.\n",
        "\n",
        "![Screenshot 2022-01-20 at 16.12.12.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "k4sdMaMbBoAY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ex3 = converter.parse(\"tinynotation: 4/4 c'2 \") #input the code to re-create the above sequence in the brackets (\"tinynotation: \"). The time signature and first note are provided.\n",
        "ex3.show()\n",
        "\n",
        "ex3.show(\"midi\")\n"
      ],
      "metadata": {
        "id": "08n1VV0gCjYe",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Summary**\n",
        "\n",
        "\n",
        "\n",
        "*   You were able to run Music21 within the Colab environment.\n",
        "*   You loaded some music from the internal corpus of Music21.\n",
        "*   You noticed how you can select parts or measures of music.\n",
        "*   You could display them by using the show command.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_ASrbG8ADhgE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3 Visualise Music**\n",
        "\n",
        "Let's look at some ways of summarising the musical content by producing visual representations of it."
      ],
      "metadata": {
        "id": "sXxwrnOkD1Ne"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's import a score."
      ],
      "metadata": {
        "id": "seFHBIJGEeAL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from music21 import *\n",
        "schoenberg = corpus.parse('schoenberg/opus19', 2) #command to input the second movement of Opus 19\n",
        "schoenberg.measures(1, 5).show() # Command to show the first five bars\n",
        "schoenberg.measures(1, 5).show(\"midi\")"
      ],
      "metadata": {
        "id": "w_O5VfYVEBo6",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3.1 Visualise the raw note events**"
      ],
      "metadata": {
        "id": "zRb-LJY5ELu5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will now look at a piano roll representation of the first 5 bars of our score with the below command."
      ],
      "metadata": {
        "id": "OY9SVr90EhtH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "schoenberg.measures(1, 5).plot()"
      ],
      "metadata": {
        "id": "_xgmWRgvEOlW",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3.2 Summary of note and duration frequencies**\n",
        "\n",
        "We can also look at the distribution of pitches (pitch-class distribution) in a histogram."
      ],
      "metadata": {
        "id": "QeoS7ruwFBF4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "schoenberg.plot('histogram', 'pitchClass')"
      ],
      "metadata": {
        "id": "Y_B_SVfmFOHi",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Question #3\n",
        "\n",
        "What is the above histogram telling us?"
      ],
      "metadata": {
        "id": "Sw8Tjn4GFYYD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also look at the relationship between pitch class and note durations using a scatter plot."
      ],
      "metadata": {
        "id": "u7O9tB-vGUlS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p = graph.plot.ScatterWeightedPitchClassQuarterLength(schoenberg)\n",
        "p.run()"
      ],
      "metadata": {
        "id": "6TGfXsgIFmq1",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3.3 Summary over time**"
      ],
      "metadata": {
        "id": "s5IK7SA3IFg1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rather than looking at an accumulation of the notes, we can look at the pitch classes used over a number of bars, with the following command:"
      ],
      "metadata": {
        "id": "i6FXrMc-IPt6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "schoenberg.plot('scatter', 'measure', 'pitchClass')"
      ],
      "metadata": {
        "id": "DxSkru4nIItL",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3.4 Visualise dynamics**"
      ],
      "metadata": {
        "id": "j41gQjkjIm4y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also look at the dynamics levels and their variations of the different instruments in a work. Let's use Beethoven's Opus 133 again to visualise the dynamics variations of the different instruments."
      ],
      "metadata": {
        "id": "oer-3tfSI9fc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "beethoven = corpus.parse('beethoven/opus133.xml')\n",
        "beethoven.measures(1,62).plot('dolan', fillByMeasure=True, segmentByTarget=True)"
      ],
      "metadata": {
        "id": "nCSun8gyIpW_",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Learning Task**\n",
        "\n",
        "Can you compare the pitch distributions of the Schoenberg and Beethoven examples (objects opus133 and schoenberg)? Note that you can visualise the pitches by using the keyword pitch:\n",
        "schoenberg.plot('histogram', 'pitch')\n",
        "\n",
        "## Question #4\n",
        "Answer after computing the above."
      ],
      "metadata": {
        "id": "G6OdzCBoLsny"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " #input command here to plot the schoenberg piece as a histogram"
      ],
      "metadata": {
        "id": "COetHcEVMHm4",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #input command here to plot the beethoven piece (opus133) as a histogram"
      ],
      "metadata": {
        "id": "UjCF9qs9MHwf",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Summary**\n",
        "\n",
        "* You were able to display the raw events of a musical piece by using the pianoroll.\n",
        "* Examples of simple visual summaries were introduced (pitch-class distribution, durations, etc.).\n",
        "* There are plenty of other visualisations available, but let's move on to analysis."
      ],
      "metadata": {
        "id": "jjFrE1boMihO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4 Analyse Music**\n",
        "\n",
        "Let's now look at some of the analytical qualities of Music21.\n",
        ""
      ],
      "metadata": {
        "id": "w4u8TnQKMzHq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.1 Qualities of chords\n",
        "\n",
        "`Music21` can infer a lot of information from stacks of pitches, that is, chords. They are such an important concept for music theory and analysis, so it is only natural that the software has a lot of options to deal with chords."
      ],
      "metadata": {
        "id": "lSplL8mZNThg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can build our own chords in Music21:"
      ],
      "metadata": {
        "id": "H4BBeWbzPWf5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chord1 = chord.Chord([\"C4\",\"G4\",\"E5\"]) # chord.Chord is the function needed to create our chord. In the square brackets we write down the pitches of the notes that will make up our chord.\n",
        "chord1.duration.type = 'half' # duration of chord - remember this is in American terminology\n",
        "kc = key.Key('C') # key (optional but useful later)\n",
        "chord1B = chord1.closedPosition() # A variant in closed position\n",
        "\n",
        "chord2 = chord.Chord(\"C F A\")\n",
        "chord2.duration.type = 'half'\n",
        "\n",
        "chord3 = chord.Chord(\"D F G B\")\n",
        "chord3.duration.type = 'half'\n",
        "\n",
        "chord1.show()"
      ],
      "metadata": {
        "id": "b6VBknORPatk",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chord2.show()"
      ],
      "metadata": {
        "id": "-3xMskyjPu7S",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chord3.show(\"midi\")"
      ],
      "metadata": {
        "id": "76LE7WJVPxTS",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can find out useful things about any of the chords with the following commands:"
      ],
      "metadata": {
        "id": "Ro1vnpecQBI1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chord3.show()\n",
        "print(chord3.commonName)\n",
        "print(chord3.quality)\n",
        "print(chord3.forteClass)\n",
        "print(chord3.orderedPitchClasses)"
      ],
      "metadata": {
        "id": "qLRs53J9Pzb_",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We can analyse the chords in a key context (the one we denoted earlier) and put the roman names into the score:\n",
        "chord1.lyric = roman.romanNumeralFromChord(chord1, kc).figure\n",
        "chord2.lyric= roman.romanNumeralFromChord(chord2, kc).figure\n",
        "chord3.lyric= roman.romanNumeralFromChord(chord3, kc).figure\n",
        "chord1B.lyric= roman.romanNumeralFromChord(chord1B, kc).figure\n",
        "\n",
        "# Let's append the chords into a stream and look at the score with roman numerals.\n",
        "stream1 = stream.Stream()\n",
        "stream1.append(chord1)\n",
        "stream1.append(chord2)\n",
        "stream1.append(chord3)\n",
        "stream1.append(chord1B)\n",
        "stream1.show()\n",
        "stream1.show(\"midi\")\n"
      ],
      "metadata": {
        "id": "z5GmvSZxQQdD",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Learning Task**\n",
        "\n",
        "Change the pitches of chord2 to C, Eb, and A, and use the print(chord2.commonName) function to see the label Music21 attributes to the chord.\n",
        "\n",
        "## Question #5\n",
        "What is the label of chord2?"
      ],
      "metadata": {
        "id": "FU_d4gG9QkVN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chord2 = chord.Chord(\"C E- A\") #change notes\n",
        "chord2.duration.type = 'half'\n",
        "\n",
        "chord2.show()\n",
        "\n",
        "print(chord2.commonName)"
      ],
      "metadata": {
        "id": "vOl7LT1-Q3tr",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.2 Analysis of functional harmony\n",
        "Let's now look at some harmonic analysis."
      ],
      "metadata": {
        "id": "FFbRG-eBRnXW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You are all experts in analysis of Bach chorales, so let's try to analyse them.\n",
        "b = corpus.parse('bwv66.6') # We load a Bach Chorale BWV 66\n",
        "b.measures(0, 2).show() # Let's look at the first bars\n",
        "\n",
        "# Slice the chords for each beat with chordify\n",
        "bChords = b.chordify()\n",
        "bChords.metadata.movementName = 'Chord reduction'         # Put a label to the score\n",
        "bChords.measures(0, 2).show()\n",
        "\n",
        "\n",
        "for c in bChords.recurse().getElementsByClass('Chord'):   # This is a command used to simplify the chords\n",
        "    c.closedPosition(forceOctave=4, inPlace=True)\n",
        "\n",
        "for c in bChords.recurse().getElementsByClass('Chord'):   # This is where the analysis happens\n",
        "    rn = roman.romanNumeralFromChord(c, key.Key('A'))     # We want to see the roman numerals for the different chords\n",
        "    c.addLyric(str(rn.figure))\n",
        "\n",
        "bChords.metadata.movementName = 'Analysis with Roman Numerals' #Add a title\n",
        "bChords.measures(0,2).show() # Show the result\n",
        "b.measures(0,2).show(\"midi\") # Show the result\n"
      ],
      "metadata": {
        "id": "nWCXapt2Ruli",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.3 Metrical analysis\n",
        "Moving on to the temporal aspects of music, *Music21* has build-in metrical analysis module, which does Lerdahl-Jackendoff style metrical analysis. As you remember from your first year Analysis Module, this indicates how important are the different beats in the metrical hierarchy suggested by the time-signature."
      ],
      "metadata": {
        "id": "rFYZm6JbSrSf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualise metrical structures\n",
        "from music21.analysis import metrical\n",
        "\n",
        "# load a Bach Chorale from the music21 corpus of supplied pieces\n",
        "bwv30_6 = corpus.parse('bach/bwv30.6.xml')\n",
        "# get just the bass part\n",
        "bass = bwv30_6.getElementById('Bass')\n",
        "# get measures 1 through 10\n",
        "excerpt = bass.measures(1,10)\n",
        "# apply a Lerdahl/Jackendoff-style metrical analysis to the piece.\n",
        "metrical.labelBeatDepth(excerpt)\n",
        "# display the results\n",
        "excerpt.show()"
      ],
      "metadata": {
        "id": "ICWCGLJeSs0s",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.4 Perceptual models\n",
        "Let's calculate some well-known perceptual concepts from the score."
      ],
      "metadata": {
        "id": "OnJlfTxYHEDA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Krumhansl-Schmuckler Key-finding\n",
        "Correlation to the 24-key profiles run within a short (2-beat) overlapping window."
      ],
      "metadata": {
        "id": "f_yr1pZNLknR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "bwv30_6 = corpus.parse('bach/bwv30.6.xml')# 30.6\n",
        "print(bwv30_6.analyze('key.krumhanslkessler'))\n",
        "bwv30_6_3meas = bwv30_6.measures(1,4) # First 3 measures\n",
        "\n",
        "KK = analysis.discrete.KrumhanslKessler() # Key profile\n",
        "wa = analysis.windowed.WindowedAnalysis(bwv30_6_3meas, KK)\n",
        "a,b = wa.analyze(2, windowType='overlap')\n",
        "\n",
        "keyclar=[]; mode=[]; key=[]; beat=[];\n",
        "for x in range(len(a)):\n",
        "    beat.append(x)\n",
        "    key.append(a[x][0])\n",
        "    mode.append(a[x][1])\n",
        "    keyclar.append(a[x][2])\n",
        "data=pd.DataFrame({'beat':beat,'key':key,'mode':mode,'r':keyclar})\n",
        "print(data)"
      ],
      "metadata": {
        "id": "m5wCh47aHi99"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the results"
      ],
      "metadata": {
        "id": "G7_g_J3NHn_n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams[\"figure.figsize\"] = (12,3)\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.plot(data['beat'],data['r'],':o', color='b')\n",
        "ax.set_ylabel(\"Max. cor. to key profile\")\n",
        "ax.set_xlabel(\"Beat\")\n",
        "x = range(data.shape[0])\n",
        "for i in x:\n",
        "  ax.annotate(data['mode'][i], (data['beat'][i]+0.2, data['r'][i]))\n",
        "  ax.annotate(data['key'][i], (data['beat'][i], data['r'][i]))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "thVJB0drHnDu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Harmonic tension\n",
        "Analysis of tonal tension using the model by Herremans and Chew (2016), implemented in `partitura` library for Python."
      ],
      "metadata": {
        "id": "FIecB17gLzb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install partitura\n",
        "import partitura\n",
        "import numpy as np\n",
        "\n"
      ],
      "metadata": {
        "id": "pDSQ4l24L9Kv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/tuomaseerola/music21/master/bwv306.musicxml\n",
        "part = partitura.load_musicxml('bwv306.musicxml')\n",
        "tonal_tension = partitura.musicanalysis.estimate_tonaltension(part, ss='onset')\n",
        "x = getattr(tonal_tension['onset_beat'][0:50], \"tolist\", lambda: value)()\n",
        "y=tonal_tension['cloud_momentum'][0:50]\n",
        "\n",
        "d = {'beat': x,'tension': y}\n",
        "df = pd.DataFrame(data=d)"
      ],
      "metadata": {
        "id": "la-IS2DgME1J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's eyeball the results."
      ],
      "metadata": {
        "id": "qFfX52cEMIAk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (12,3)\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.plot(df['beat'],df['tension'], color='r')\n",
        "ax.set_ylabel(\"Tension\")\n",
        "ax.set_xlabel(\"Beat\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "0lMlqUINMNm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Summary\n",
        "* We explored simple harmonic analysis using chord and reduction functions in *Music21*.\n",
        "* An example of metrical analysis was given.\n",
        "* Key-finding and tension algoritms were demonstrated\n",
        "* In *Music21* you can combine analytical options, which might be used to carry out a reduction of music based on metrical hierarchy just to give an example.\n",
        "\n",
        "* These operations can be combined with other operations and run across a collection of pieces (covered in a later section)."
      ],
      "metadata": {
        "id": "xMmJq_HGS_2h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5 References**\n",
        "\n",
        "* Cuthbert, M. S., & Ariza, C. (2010). music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data. In J. Stephen Downie and Remco C. Veltkamp (Eds.). 11th International Society for Music Information Retrieval Conference (ISMIR 2010), August 9-13, 2010, Utrecht, Netherlands. pp. 637-642. [link](http://ismir2010.ismir.net/proceedings/ismir2010-108.pdf)\n",
        "\n",
        "* Cuthbert, M. S., Ariza, C., & Friedland, L. (2011). Feature Extraction and Machine Learning on Symbolic Music using the music21 Toolkit. In 11th International Society for Music Information Retrieval Conference (ISMIR 2011) (pp. 387--392). [link](http://ismir2011.ismir.net/papers/PS3-6.pdf)\n"
      ],
      "metadata": {
        "id": "cZqixeBjTFf9"
      }
    }
  ]
}