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)
Provides an all-in-one solution for automatic classification of sound events using convolutional neural networks (CNN). From annotating sound events in recordings to automating model usage in real-life situations. This package is aimed at (but not limited to) biologists and ecologists working with sound events that could benefit greatly from machine learning algorithms applied to their research. 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 and 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.
install.packages("soundClass")