Title of Research Output

Quantitative Analysis of Musical Texture

Faculty

Don Wright Faculty of Music

Supervisor Name

Dr. Jonathan De Souza

Keywords

musical texture, onset synchrony, pitch comodulation, Bach, Mozart, Haydn, Beethoven, complexity theory, complex adaptive systems, sonata form

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Description

Musical texture analysis can often be difficult as a piece’s texture can contain various elements of the four main textural categories: monophony, polyphony, homophony, and heterophony. The variables that separate these categories are the number of musical streams present in the music, and the functions of each of these streams. The music theorist David Huron states that the primary parameters that affect our ability to differentiate musical streams are onset synchrony and pitch comodulation. With the creation of a code that utilizes Python and music21, we are able to analyze onset synchrony and pitch comodulation levels within music. The code is applicable to any music that can be transcribed into Western classical music notation. Gathering onset synchrony and pitch comodulation data demonstrates the extent of which a work aligns with certain textural categories. This video also outlines several applications for this type of quantitative musical texture analysis. Looking at 60 works from Johann Sebastian Bach, we investigate how Bach varies his textural practices based on the genre and the intended function of the piece. Studies of classical string quartets from Ludwig van Beethoven, Joseph Haydn, and Wolfgang Amadeus Mozart reveal that texture significantly interacts with the large sections of sonata form. With multiple possibilities for future projects using this method of quantitative analysis, we will eventually attempt to relate large ensemble compositions to social network theory. As a result, we will be able to observe texture through the lens of complexity theory and complex adaptive systems.

Acknowledgements

I would like to thank Dr. De Souza for his constant support and inclusion on this project. I enjoyed our long conversations about music theory, the effects of colonialism on academia and music, and education. He consistently pushed me forward and because of this, he has given me skills that I believe will stay with me throughout the rest of my career. As someone who hopes to continue their academic career at the institutional level, I am grateful to call him a mentor.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Document Type

Video

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Quantitative Analysis of Musical Texture

Musical texture analysis can often be difficult as a piece’s texture can contain various elements of the four main textural categories: monophony, polyphony, homophony, and heterophony. The variables that separate these categories are the number of musical streams present in the music, and the functions of each of these streams. The music theorist David Huron states that the primary parameters that affect our ability to differentiate musical streams are onset synchrony and pitch comodulation. With the creation of a code that utilizes Python and music21, we are able to analyze onset synchrony and pitch comodulation levels within music. The code is applicable to any music that can be transcribed into Western classical music notation. Gathering onset synchrony and pitch comodulation data demonstrates the extent of which a work aligns with certain textural categories. This video also outlines several applications for this type of quantitative musical texture analysis. Looking at 60 works from Johann Sebastian Bach, we investigate how Bach varies his textural practices based on the genre and the intended function of the piece. Studies of classical string quartets from Ludwig van Beethoven, Joseph Haydn, and Wolfgang Amadeus Mozart reveal that texture significantly interacts with the large sections of sonata form. With multiple possibilities for future projects using this method of quantitative analysis, we will eventually attempt to relate large ensemble compositions to social network theory. As a result, we will be able to observe texture through the lens of complexity theory and complex adaptive systems.