piano_codewords — Classical piano codeword transitions (1703-1916)
- Description
A network of transitions between "codewords" (sets of simultaneously played notes) in Western classical piano music of the common practice period, from Park, Nam and Park. The data consist of MIDI files of 900 piano works by 19 composers from the Baroque, Classical and Romantic periods (from J. S. Bach and G. F. Handel to M. Ravel), collected from the Kunst der Fuge and Classical Piano MIDI archives. The files were converted to MusicXML with MuseScore and "chordified" with the Music21 toolkit, which converts a score into a sequence of sets of simultaneously played notes (codewords), retaining octave and key information (tempo, rhythm, etc. are discarded). Each node is a codeword, and a directed edge goes from a codeword to the codeword that follows it in some work, weighted by the number of times this transition occurs in all works. Self-loops correspond to repeated codewords. This is the aggregated ("total") network of the source data; the separate networks of each work are not included.
The network has the following properties:
- Nodes:
name, the node label in the source data, of the form "pitches-year";pitches, the list of notes of the codeword, as MIDI note numbers (60 is the middle C);year, the year attached to the codeword in its label, which appears to be the year of the earliest work in which the codeword occurs (it ranges from 1703 to 1916). - Edges:
weight, the number of occurrences of the transition.
- Nodes:
- Tags
- Informational Weighted Metadata
- Citation
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- D. Park, J. Nam, J. Park, "Novelty and influence of creative works, and quantifying patterns of advances based on probabilistic references networks." EPJ Data Science 9, 2 (2020) 10.1140/epjds/s13688-019-0214-8 Sci-Hub
- Upstream URL OK
- https://doi.org/10.17605/OSF.IO/JVZXG
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties