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>.
| Version: |
0.0.9.2 |
| Depends: |
shinyBS, htmltools |
| Imports: |
seewave, DBI, dplyr, dbplyr, RSQLite, signal, tuneR, zoo, magrittr, shinyFiles, shiny, utils, graphics, generics, keras, shinyjs |
| Suggests: |
knitr, rmarkdown |
| Published: |
2022-05-29 |
| DOI: |
10.32614/CRAN.package.soundClass |
| Author: |
Bruno Silva [aut, cre] |
| Maintainer: |
Bruno Silva <bmsasilva at gmail.com> |
| BugReports: |
https://github.com/bmsasilva/soundClass/issues |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
soundClass results |
Documentation:
Downloads:
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