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  "Package": "soundClass",
  "Title": "Sound Classification Using Convolutional Neural Networks",
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  "Author": "Bruno Silva [aut, cre]",
  "Maintainer": "Bruno Silva <bmsasilva@gmail.com>",
  "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<doi:10.1111/2041-210X.13101> Mac Aodha, O., et al. (2018)\n<doi:10.1371/journal.pcbi.1005995> Stowell, D., et al. (2019)\n<doi:10.1111/2041-210X.13103> LeCun, Y., et al. (2012)\n<doi:10.1007/978-3-642-35289-8_3>.",
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  "Date/Publication": "2022-12-15 10:01:18 UTC",
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