K-EmoCon Supplementary Codes

This repository contains supplementary codes for the K-EmoCon Dataset.

For the detailed description of the dataset, please refer to:

Park, C.Y., Cha, N., Kang, S. et al. K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations. Sci Data 7, 293 (2020). https://doi.org/10.1038/s41597-020-00630-y

Usage

Installation

$ git clone https://github.com/cheulyop/K-EmoCon_SupplementaryCodes.git
$ cd K-EmoCon_SupplementaryCodes

Preprocessing

$ python preprocess.py --root '/path/to/kemocon_root'

Running preprocess.py will create 5-second segments containing 4 types of biosignals ['bvp', 'eda', 'temp', 'ecg'] acquired during debates as JSON files to '/path/to/kemocon_root/segments/', under subdirectories corresponding to each participant.

JSON files for biosignal segments will have names with the following pattern: for example, p01-017-243333.json indicates that the file is a 17th 5-second biosignal segment for participant 1.

The last 6 digits are multiperspective emotion annotations associated with the segment, in the order of 1) self-arousal, 2) self-valence, 3) partner-arousal, 4) partner-valence, 5) external-arousal, and 6) external-valence.

Baseline Classification

baseline.py implements a stratified k-fold and leave-one-subject-out (LOSO) cross-validation for the binary classification of low and high classes (with low = 1--2 and high = 3--5) and the multi-class classification between 4 quadrants (LALV, LAHV, HALV, HAHV), with three heuristic voters (random, majority, and class ratio), Gaussian NB, and XGBoost.

* baseline.py requires PyTEAP for feature extraction. You can install it by running: pip install PyTEAP in your terminal.

Results

For the classification results, see csv files in the results folder.

Supplementary Codes for the K-EmoCon Dataset Descriptor

• chauvenet.py - an implementation of Chauvenet's criterion for detecting outliers.
• vote_majority.py - implements a majority voting to get a consensus between external annotations.
• plotting.py - includes functions to produce the IRR heatmap (Fig. 4) in the K-EmoCon dataset descriptor.
  • get_annotations - loads annotations saved as CSV files.
  • subtract_mode_from_values - implements mode subtraction.
  • compute_krippendorff_alpha - computes Krippendorff's alpha (IRR).
  • plot_heatmaps - plots the IRR heatmap.