README.md

In pmcharrison/incon: Computational Models of Simultaneous Consonance

incon - Computational Models of Simultaneous Consonance

incon is an R package that implements various computational models of simultaneous consonance perception.

Citation

Harrison, P. M. C., & Pearce, M. T. (2020). Simultaneous consonance in music perception and composition. Psychological Review, 127(2), 216–244.

Installation

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

if (!require(devtools)) install.packages("devtools")
devtools::install_github("pmcharrison/incon")

To run the Jupyter notebook, you need to download this code repository. You then need to install Jupyter Notebook (see https://jupyter.org/install):

pip install notebook

Then open R, install IRkernel, then run installspec:

install.packages("IRkernel")
IRkernel::installspec()

Then from a new terminal, open this notebook:

jupyter notebook Demo.ipynb

Usage

The primary function is incon, which applies consonance models to an input chord. The default model is that of Hutchinson & Knopoff (1978):

library(incon)

chord <- c(60, 64, 67) # major triad, MIDI note numbers
incon(chord)
#> hutch_78_roughness 
#>          0.1202426

You can specify a vector of models and these will applied in turn.

chord <- c(60, 63, 67) # minor triad
models <- c("hutch_78_roughness", 
            "parn_94_pure",
            "huron_94_dyadic")
incon(c(60, 63, 67), models)
#> hutch_78_roughness       parn_94_pure    huron_94_dyadic 
#>          0.1300830          0.6368813          2.2200000

See Models for a list of available models. See the package’s inbuilt documentation, ?incon, for further details.

To try the model without need for a local installation, visit https://mybinder.org/v2/gh/pmcharrison/incon/HEAD?labpath=Demo.ipynb.

Models

Currently the following models are implemented:

| Label | Citation | Class | Package | |:----------------------|:------------------------------|:------------------------|:--------| | gill_09_harmonicity | Gill & Purves (2009) | Periodicity/harmonicity | incon | | har_18_harmonicity | Harrison & Pearce (2018) | Periodicity/harmonicity | incon | | milne_13_harmonicity | Milne (2013) | Periodicity/harmonicity | incon | | parn_88_root_ambig | Parncutt (1988) | Periodicity/harmonicity | incon | | parn_94_complex | Parncutt & Strasburger (1994) | Periodicity/harmonicity | incon | | stolz_15_periodicity | Stolzenburg (2015) | Periodicity/harmonicity | incon | | bowl_18_min_freq_dist | Bowling et al. (2018) | Interference | incon | | huron_94_dyadic | Huron (1994) | Interference | incon | | hutch_78_roughness | Hutchinson & Knopoff (1978) | Interference | incon | | parn_94_pure | Parncutt & Strasburger (1994) | Interference | incon | | seth_93_roughness | Sethares (1993) | Interference | incon | | vass_01_roughness | Vassilakis (2001) | Interference | incon | | wang_13_roughness | Wang et al. (2013) | Interference | incon | | jl_12_tonal | Johnson-Laird et al. (2012) | Culture | incon | | har_19_corpus | Harrison & Pearce (2019) | Culture | incon | | parn_94_mult | Parncutt & Strasburger (1994) | Numerosity | incon | | har_19_composite | Harrison & Pearce (2019) | Composite | incon |

See ?incon for more details.

Spectral representations

Certain incon models can be applied to full frequency spectra rather than just symbolically notated chords. One example is the set of interference models provided in the dycon package. In order to run such models on full frequency spectra one must call lower-level functions explicitly, as in the following example, which computes the roughness of a chord using the Hutchinson-Knopoff dissonance model:

spectrum <- 
  hrep::sparse_fr_spectrum(list(
    frequency = c(400, 800, 1200, 1250),
    amplitude = c(1, 0.7, 0.9, 0.6)
  ))

roughness_hutch(spectrum)

References

Bowling, D. L., Purves, D., & Gill, K. Z. (2018). Vocal similarity predicts the relative attraction of musical chords. Proceedings of the National Academy of Sciences, 115(1), 216–221. https://doi.org/10.1073/pnas.1713206115

Gill, K. Z., & Purves, D. (2009). A biological rationale for musical scales. PLoS ONE, 4(12). https://doi.org/10.1371/journal.pone.0008144

Harrison, P. M. C., & Pearce, M. T. (2018). An energy-based generative sequence model for testing sensory theories of Western harmony. In Proceedings of the 19th International Society for Music Information Retrieval Conference (pp. 160–167). Paris, France.

Harrison, P. M. C., & Pearce, M. T. (2019). Instantaneous consonance in the perception and composition of Western music. PsyArxiv. https://doi.org/10.31234/osf.io/6jsug

Huron, D. (1994). Interval-class content in equally tempered pitch-class sets: Common scales exhibit optimum tonal consonance. Music Perception, 11(3), 289–305. https://doi.org/10.2307/40285624

Hutchinson, W., & Knopoff, L. (1978). The acoustic component of Western consonance. Journal of New Music Research, 7(1), 1–29. https://doi.org/10.1080/09298217808570246

Johnson-Laird, P. N., Kang, O. E., & Leong, Y. C. (2012). On musical dissonance. Music Perception, 30(1), 19–35.

Milne, A. J. (2013). A computational model of the cognition of tonality. The Open University, Milton Keynes, England.

Parncutt, R. (1988). Revision of Terhardt’s psychoacoustical model of the root(s) of a musical chord. Music Perception, 6(1), 65–93.

Parncutt, R., & Strasburger, H. (1994). Applying psychoacoustics in composition: “Harmonic” progressions of “nonharmonic” sonorities. Perspectives of New Music, 32(2), 88–129.

Sethares, W. A. (1993). Local consonance and the relationship between timbre and scale. The Journal of the Acoustical Society of America, 94(3), 1218–1228.

Stolzenburg, F. (2015). Harmony perception by periodicity detection. Journal of Mathematics and Music, 9(3), 215–238. https://doi.org/10.1080/17459737.2015.1033024

Vassilakis, P. N. (2001). Perceptual and physical properties of amplitude fluctuation and their musical significance. University of California, Los Angeles, CA.

Wang, Y. S., Shen, G. Q., Guo, H., Tang, X. L., & Hamade, T. (2013). Roughness modelling based on human auditory perception for sound quality evaluation of vehicle interior noise. Journal of Sound and Vibration, 332(16), 3893–3904. https://doi.org/10.1016/j.jsv.2013.02.030

pmcharrison/incon documentation built on Feb. 12, 2024, 3:18 a.m.