Etichettatura del segnale

Etichettare manualmente e automaticamente gli attributi del segnale, delle regioni di interesse e dei punti

Utilizzare il Deep Learning per accelerare e automatizzare l’etichettatura delle feature.

App

Label signal attributes, regions, and points of interest

`edfheader`	Create header structure for EDF or EDF+ file
`edfinfo`	Get information about EDF/EDF+ file
`edfread`	Read data from EDF/EDF+ file
`edfwrite`	Create or modify EDF or EDF+ file
`paddata`	Pad data by adding elements (Da R2023b)
`resize`	Resize data by adding or removing elements (Da R2023b)
`trimdata`	Trim data by removing elements (Da R2023b)
`signalDatastore`	Datastore for collection of signals

`labeledSignalSet`	Create labeled signal set
`signalLabelDefinition`	Create signal label definition
`signalMask`	Modify and convert signal masks and extract signal regions of interest
`sigrangebinmask`	Label signal samples with values within a specified range (Da R2023a)
`countlabels`	Count number of unique labels
`filenames2labels`	Get list of labels from filenames (Da R2022b)
`folders2labels`	Get list of labels from folder names
`splitlabels`	Find indices to split labels according to specified proportions

Automate Signal Labeling with Custom Functions (Signal Processing Toolbox)
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Use Signal Labeler to label attributes, regions, and points of interest in a set of whale songs.
Create Labeled Signal Sets Iteratively with Reduced Human Effort (Signal Processing Toolbox)
Use deep learning to decrease the human effort required to label signals.

Label signals and export data using Signal Labeler to train a deep learning classifier.