Package: soundClass 0.0.9.3
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 training 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>.
Authors:
soundClass_0.0.9.3.tar.gz
soundClass_0.0.9.3.zip(r-4.5)soundClass_0.0.9.3.zip(r-4.4)soundClass_0.0.9.3.zip(r-4.3)
soundClass_0.0.9.3.tgz(r-4.4-any)soundClass_0.0.9.3.tgz(r-4.3-any)
soundClass_0.0.9.3.tar.gz(r-4.5-noble)soundClass_0.0.9.3.tar.gz(r-4.4-noble)
soundClass_0.0.9.3.tgz(r-4.4-emscripten)soundClass_0.0.9.3.tgz(r-4.3-emscripten)
soundClass.pdf |soundClass.html✨
soundClass/json (API)
NEWS
# Install 'soundClass' in R: |
install.packages('soundClass', repos = c('https://bmsasilva.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bmsasilva/soundclass/issues
Last updated 2 years agofrom:7990081b6f. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 04 2024 |
R-4.5-win | NOTE | Nov 04 2024 |
R-4.5-linux | NOTE | Nov 04 2024 |
R-4.4-win | NOTE | Nov 04 2024 |
R-4.4-mac | NOTE | Nov 04 2024 |
R-4.3-win | NOTE | Nov 04 2024 |
R-4.3-mac | NOTE | Nov 04 2024 |
Exports:%>%app_labelapp_modelauto_idcreate_dbfind_noiseimport_audioms2samplesplot_tdspectro_callstrain_metadata
Dependencies:backportsbase64encbitbit64blobbslibcachemclicommonmarkconfigcpp11crayonDBIdbplyrdigestdplyrfansifastmapfontawesomefsgenericsglueherehtmltoolshttpuvjquerylibjsonlitekeraslaterlatticelifecyclemagrittrMASSMatrixmemoisemimepillarpkgconfigplogrpngprocessxpromisespspurrrR6rappdirsRcppRcppTOMLreticulaterlangrprojrootRSQLiterstudioapisassseewaveshinyshinyBSshinyFilesshinyjssignalsourcetoolsstringistringrtensorflowtfautographtfrunstibbletidyrtidyselecttuneRutf8vctrswhiskerwithrxtableyamlzeallotzoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Pipe operator | %>% |
Shiny app to label recordings | app_label |
Shiny app to fit a model or run a fitted model | app_model |
Automatic classification of sound events in recordings | auto_id |
Create a sqlite3 database | create_db |
Detect energy peaks in recordings with non-relevant events | find_noise |
Import a recording | import_audio |
Convert between time and number of samples in sound files | ms2samples |
Plot training spectrograms | plot_td |
Generate spectrograms from labels | spectro_calls |
Obtain train metadata to run a fitted model | train_metadata |