incon is an R package that implements various computational models of
simultaneous consonance perception.
Harrison, P. M. C., & Pearce, M. T. (2020). Simultaneous consonance in music perception and composition. Psychological Review, 127(2), 216–244.
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
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
#> 0.1202426You 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 2.2200000See 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.
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.
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)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