en:basics1 [Computergestützte Musikanalyse]

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===== Module Basics: Sheet Music Analysis with music21 =====

  * Tutorial [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Introduction_music21.ipynb|Jupyter_SheetMusic_Basics_Introduction_music21]] (Jupyter-Notebook; [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Introduction_music21.html|html]] preview)
  * Tutorial [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Part1_Visualisation.ipynb|Jupyter_SheetMusic_Basics_Part1_Visualisation]] (Jupyter-Notebook; [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Part1_Visualisation.html|html]] preview)
  * Tutorial [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Part2_Statistics.ipynb|Jupyter_SheetMusic_Basics_Part2_Statistics]] (Jupyter-Notebook; [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Part2_Statistics.html|html]] preview)

This page provides basic information for the module //Basics Sheet Music Analysis with music 21//. Various examples introduces into working with //music21// and //Jupyter Notebooks//, visualizing of music scores and statistical queries. 

The requirements for running //Jupyter Notebooks// in the browser are the installation of Anaconda respectively Miniconda and of the score editor MuseScore. Detailed installation instructions and introductory tutorials can be found here:

  * Installation [[en:jupyter-anaconda|Jupyter Notebook, Anaconda und Miniconda]]
  * Tutorial [[en:jupyter-basic|Jupyter Notebook]]
  * Installation [[en:installation#musescore|MuseScore]]
  * Tutorial [[en:musicxml|MusicXML in depth]]

After successful installation, please save the three Jupyter notebook tutorials (see above) to your computer (right click → save target as). Choose as location the path of the default working folder you get to when starting //Jupyter notebooks// (usually 'c:/users/ name' - where 'name' is specifically assigned to your computer). Create a folder "Analysis" there, where you then store all notebooks. 

| //Attention: Jupyter notebooks have the file extension '.ipynb'. Unfortunately, some operating systems change the ending to ´.txt´ and, then, the files get assigned to a text editor. In this case please change the ending back to ´.ipynb´, otherwise the Jupyter Notebooks cannot be opened.//| 

| //The html versions can only be read, not executed or modified; they are for preliminary information only!//|  

Afterwards you should briefly familiarise yourself with the concept of Jupyter notebooks and the basic commands in //music21// as well as adapt some settings for //music21// to your local device.

 This will be helped by the 
  * Tutorial [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Introduction_music21.ipynb|Jupyter_SheetMusic_Basics_Introduction_music21]] (Jupyter-Notebook; [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Introduction_music21.html|html]] preview)

| //A quick and uncomplicated introduction to the possibilities of computer-aided music analysis with //music21// is offered by the [[en:Interaktive Musikanalyse| Interactive Music Analysis]]. Here you can get to know the various statistical queries by selecting options, without having to get involved in the command syntax of //music21// and how the //Jupyter notebooks// work. (The installation of //Anaconda// or //Miniconda// as well as [[installation#musescore|MuseScore]].)is required).//|

Following the Basics module, you can turn to the tutorials in the [[en:advanced1|Advanced Notes]] module:

   * Tutorial [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Advanced_Part1_Josquin.ipynb|Jupyter_SheetMusic_Advanced_Part1_Josquin]] (Jupyter-Notebook; [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Advanced_Part1_Josquin.html|html]] preview)
   * Tutorial [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Advanced_Part2_Beethoven.ipynb|Jupyter_SheetMusic_Advanced_Part2_Beethoven]] (Jupyter-Notebook; [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Advanced_Part2_Beethoven.html|html]] preview)


==== Tutorial 1: Visualisation of sheet music ====

[[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Part1_Visualisation.ipynb|Jupyter_SheetMusic_Basics_Part1_Visualisation]] (Jupyter-Notebook; [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Part1_Visualisation.html|html]] preview)

Usually, music notation on which musical analysis is usually based on are playing instructions for the musicians. This is called //prescriptive// notation. In contrast, musical structures which are to be described in the analysis - and as far as possible as they are sounded.

From prescriptive notation the American musicologist Charles Seeger distinguished a //descriptive// notation with which an attempt is made to show musical structures graphically. The purpose of these visualisations is not to perform music, but to get as good an impression as possible of its tonal structures and textures, such as the fabric of the various voices over time and in pitch space. \\

A simple descriptive notation is the two-dimensional arrangement of all played notes: The y-axis corresponds to absolute pitch, the x-axis to time. Different instruments can be colored differently. This depiction is called //piano roll// representation because the rolls for player pianos were punched according to this pattern.

The following piano roll visualisations is from the Tutorial //Basics Noten 1// to show how sheet music is displayed in an intuitive way.

{{ :pianoroll.png?800 |}}

| **Note**: You can display piano rolls very clearly in the  [[https://analyse.hfm-weimar.de/doku.php?id=en:installation#sonic_visualiser|Sonic Visualiser]]  and listen to them with MIDI sounds at the same time. A number of piano rolls are listed in the [[en:notenauswahl|subcorpus of the sheet music database]] and can be downloaded there. Detailed instructions on how to proceed and how you could generate piano rolls from score files can be found [[en:sv-pianoroll|here]]. |

//Music21// also offers the possibility to automatically generate piano reductions of music scores (//chordify//-command). This facilitates for example harmonic analysis. 

The tutorial also explains a few other useful features of //music21//, including the selection of excerpts (measures) and voices.
 

==== Tutorial 2: Statistical queries of musical characteristics ====
[[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Part1_Visualisation.ipynb|Jupyter_SheetMusic_Basics_Part1_Visualisation]] (Jupyter-Notebook; [[https://analyse.hfm-weimar.de/jupyter/Jupyter_SheetMusic_Basics_Part1_Visualisation.html|html]] preview)

The //music21// commands presented in the Jupyter notebooks can first be used to query the number of notes (total or per voice), measures, and information about ambitus (total and per voice).

By examining //frequencies//, you can get a first overview of the simplicity and uniformity or the variety and complexity of a composition. For example, is only the diatonic tone stock used in the composition, or are there also many chromatic tones? Do the individual voices progress mainly in small interval steps or are there (also) large leaps? Are there only a few different note durations or a big variety of note values?

These questions are answered by histograms showing frequency distributions (=bar charts) and spreadsheets. You can determine and display frequencies of the following parameters or variables (among others) by the use of simple commands:

\\
**Frequencies of pitches** can be queried per score file and per voice. A distinction is made between different octave positions.

{{ ::histogramm.png?500 |}}

\\
**Frequencies of the twelve pitch classes** - per voice and in total. //No// distinction is made between different octave positions!

\\ 
The English pitch designations are used:

   C C#/Db D D#/Eb E F F#/Gb G G#/Ab A A#/Bb B C 
   

{{ ::pitch_class_music21.png?300 |}}

\\
**Frequencies of interval steps or jumps** within a voice.\\

Attention: This is just possible for monophonic voices!\\

The following interval names are used:
  
   Perfect unison= 0 
   Minor second= m2 
   Major second= M2
   Minor third= m3 
   Major third= M3
   Perfect fourth= P4 
   Etc.  

{{ ::interval_music21.png?300 |}}

\\            
**Frequencies of note durations / note values** 

Note values are counted as multiples or parts of a quarter note:

   Quarter = 1
   Eighths = 0.5
   Half = 2 
   Etc.

{{ ::quarter_length_music21.png?300 |}}

\\
**Metric Weight**

//Music21// offers a way to determine the //metric weight// (the metric accent) of each note (1.0 = highest weight, 0.5 = middle weight, 0.25 lower weight etc.) by the beat-strength-command and to calculate the frequencies of the individual weights or accent levels. This allows to understand how clearly or unambiguously a meter is established in the music. \\

The metrical accents follow the following weighting for the common time signatures (each for a sequence of eighth notes):
 
   4/4: [1.0, 0.125, 0.25, 0.125, 0.5, 0.125, 0.25, 0.125]
   2/4: [1.0, 0.25, 0.5, 0.25]
   3/4: [1.0, 0.25, 0.5, 0.25, 0.5, 0.25]
   6/8: [[1.0, 0.25, 0.25, 0.5, 0.25, 0.25]
   9/8: [1.0, 0.25, 0.25, 0.5, 0.25, 0.25, 0.5, 0.25, 0.25]
   2/2: [1.0, 0.125, 0.25, 0.125, 0.5, 0.125, 0.25, 0.125]
   
   1.0 = highest weight, 0.5 = medium weight, 0.25 lower weight etc.
   
\\
**Two-dimensional frequency distributions**

In addition, two of the mentioned musical parameters can be displayed in so-called //two-dimensional frequency distributions//, i.e. in dependence on each other, e.g. the frequencies of the individual pitches coupled in each case with certain duration values (quarter, eighth etc.). The two-dimensional frequency distributions can be displayed as scatter plots or 3D plots. With //music21//, the following parameters can be related to each other:

  * •	Frequency of note duration related to pitch and pitch class
  * •	Frequency of metric positions, respectively metric weight related to pitch and pitch class
\\

{{:scatterplot.png?400 |}} {{:3d_pitch_class_quarter_lenght_music21.png?400|}}
\\
\\
\\
All statistical queries can be applied to\\ 
 
- individual movements and excerpts of compositions\\ 
- on complete compositions as well as\\ 
- several compositions in comparison!

In this way, individual compositions, but also corpora of compositions can be analyzed.

Furthermore, it is possible to export the frequency distributions as so-called csv files ("csv" ="comma-seperated values"). The csv format can be opened and read by spreadsheet software such as Excel, among others. In this way, the data can be processed externally, which can be particularly useful for corpus studies.