soundClass: Sound Classification Using Convolutional Neural Networks

Provides an all-in-one solution for automatic classification of sound events using convolutional neural networks (CNN). The main purpose is to provide a sound classification workflow, from annotating sound events in recordings to training and automating model usage in real-life situations. Using the package requires a pre-compiled collection of recordings with sound events of interest and it can be employed for: 1) Annotation: create a database of annotated recordings, 2) Training: prepare train data from annotated recordings and fit CNN models, 3) Classification: automate the use of the fitted model for classifying new recordings. By using automatic feature selection and a user-friendly GUI for managing data and training/deploying models, this package is intended to be used by a broad audience as it does not require specific expertise in statistics, programming or sound analysis. Please refer to the vignette for further information. Gibb, R., et al. (2019) <doi:10.1111/2041-210X.13101> Mac Aodha, O., et al. (2018) <doi:10.1371/journal.pcbi.1005995> Stowell, D., et al. (2019) <doi:10.1111/2041-210X.13103> LeCun, Y., et al. (2012) <doi:10.1007/978-3-642-35289-8_3>.

Depends: shinyBS, htmltools
Imports: seewave, DBI, dplyr, dbplyr, RSQLite, signal, tuneR, zoo, magrittr, shinyFiles, shiny, utils, graphics, generics, keras, shinyjs
Suggests: knitr, rmarkdown
Published: 2022-03-04
Author: Bruno Silva [aut, cre]
Maintainer: Bruno Silva <bmsasilva at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: soundClass results


Reference manual: soundClass.pdf
Vignettes: example


Package source: soundClass_0.0.9.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): soundClass_0.0.9.1.tgz, r-oldrel (arm64): soundClass_0.0.9.1.tgz, r-release (x86_64): soundClass_0.0.9.1.tgz, r-oldrel (x86_64): soundClass_0.0.9.1.tgz
Old sources: soundClass archive


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