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.
- Version0.0.9.2
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- 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
- Last release05/29/2022
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Team
Bruno Silva
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- Depends2 packages
- Imports14 packages
- Suggests2 packages