README.md

In sebsilas/pyin: Fundamental Frequency Estimation Using The Probabilistic YIN (pYIN) Algorithm.

pYIN R package

pyin is an R package which wraps the pYIN algorithm (Mauch & Dixon, 2014) for fundamental frequency estimation via Sonic Annotator (Cannam, Jewell, Rhodes, Sandler & d’Invernoand, 2010) for use in the R environment.

Installation

install.packages("devtools")
devtools::install_github("sebsilas/pyin")

Usage

library(pyin)

# First test using our test function:

test <- test_pyin()

# If the pyin setup was successful on your system, 'test' should contain a 10x5 data with the transcribed note events of a demo audio file we have distributed with the package.

# Try your own audio file:

my_audio_transcription <- pyin('/my/file/path/audio.wav')

# If you install the itembankr package, you can compute extra melodic features on this:

devtools::install_github('sebsilas/itembankr')

library(dplyr)
library(itembankr)


my_audio_transcription %>% produce_extra_melodic_features()

# where my_audio_transcription contains a pYIN result.

Notes

See https://vamp-plugins.org/sonic-annotator/ for information about allowed file types etc.

It is possible to also supply transform files, as described there, via the R package version of pYIN (see the transform_file argument)

Compatability

This R package currently only supports Windows and Mac 64-bit. If you require support for other systems, please get in touch.

References

Cannam, C., Jewell, M. O., Rhodes, C., Sandler, M., & d’Inverno, M. (2010). Linked Data And You: Bringing music research software into the Semantic Web. Journal of New Music Research, 39(4), 313–325.

Mauch, M., & Dixon, S. (2014). PYIN: a fundamental frequency estimator using probabilistic threshold distributions. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014).

sebsilas/pyin documentation built on Nov. 2, 2023, 5:54 a.m.