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soundClass  

Sound Classification Using Convolutional Neural Networks
View on CRAN: Click here


Download and install soundClass package within the R console
Install from CRAN:
install.packages("soundClass")

Install from Github:
library("remotes")
install_github("cran/soundClass")

Install by package version:
library("remotes")
install_version("soundClass", "0.0.9.2")



Attach the package and use:
library("soundClass")
Maintained by
Bruno Silva
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-03-04
Latest Update: 2022-05-29
Description:
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>.
How to cite:
Bruno Silva (2022). soundClass: Sound Classification Using Convolutional Neural Networks. R package version 0.0.9.2, https://cran.r-project.org/web/packages/soundClass
Previous versions and publish date:
0.0.9.1 (2022-03-04 17:50), 0.0.9 (2022-02-01 20:30)
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