corpdiss: A corpus-based model of dissonance perception

The corpdiss R package implements a corpus-based model of dissonance perception. This model represents the idea that dissonance decreases through familiarity, and therefore perceptual dissonance should decrease in proportion to a chord’s prevalence in a given musical style.

Installation

You can install the current version of corpdiss from Github by entering the following commands into R:

if (!require(devtools)) install.packages("devtools")
devtools::install_github("corpdiss")

Example usage

Estimating chordal consonance

The corpus_dissonance function estimates the consonance of an individual chord.

library(corpdiss)

# Major triad, root position
corpus_dissonance(c(60, 64, 67))
#> [1] 0.7802433

# Major triad, first inversion
corpus_dissonance(c(64, 67, 72))
#> [1] 4.06715

# Minor triad, root position
corpus_dissonance(c(60, 63, 67))
#> [1] 2.271055

# Diminished triad
corpus_dissonance(c(60, 63, 66))
#> [1] 6.134397

# Augmented triad
corpus_dissonance(c(60, 64, 67))
#> [1] 0.7802433

By default, the corpus_dissonance function uses chord prevalences extracted from the McGill Billboard dataset (Burgoyne, 2011). The dataset’s contents were sampled from the Billboard magazine’s United States “Hot 100” chart between 1958 and 1991, and transcribed by expert musicians.

By default, the dissonance estimation treats chords as transposition invariant, and chord pitches as octave-invariant, but differentiates between different inversions of the same chord. Different behaviour can be achieved by passing a custom corpus analysis to corpus_dissonance (see ?corpus_dissonance for more details).

Adding new corpora

It is easy to analyse dissonance using a different corpus, or a different chord representation.

corpus <- hcorp::classical_1[1:5]
analysis <- corpus_dissonance_table(corpus, type = "pc_set", add = 0)
chord <- c(60, 64, 67)
corpus_dissonance(chord, analysis)
#> [1] 4.106767