{ "_id": "6acf5519e2d863f662950bc5", "Package": "soundClass", "Title": "Sound Classification Using Convolutional Neural Networks", "Version": "0.0.9.3", "Author": "Bruno Silva [aut, cre]", "Maintainer": "Bruno Silva ", "Description": "Provides an all-in-one solution for automatic\nclassification of sound events using convolutional neural\nnetworks (CNN). The main purpose is to provide a sound\nclassification workflow, from annotating sound events in\nrecordings to training and automating model usage in real-life\nsituations. Using the package requires a pre-compiled\ncollection of recordings with sound events of interest and it\ncan be employed for: 1) Annotation: create a database of\nannotated recordings, 2) Training: prepare training data from\nannotated recordings and fit CNN models, 3) Classification:\nautomate the use of the fitted model for classifying new\nrecordings. By using automatic feature selection and a\nuser-friendly GUI for managing data and training/deploying\nmodels, this package is intended to be used by a broad audience\nas it does not require specific expertise in statistics,\nprogramming or sound analysis. Please refer to the vignette for\nfurther information. Gibb, R., et al. (2019)\n Mac Aodha, O., et al. (2018)\n Stowell, D., et al. (2019)\n LeCun, Y., et al. 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