MuSe-Humor 2024 (MuSe-Challenge 2024)

Important Information: Due to the large number of requests, unsolicited requests will be rejected immediately. The data will be provided exclusively to the MuSe-Humor Sub-Challenge registered participants. To register for the challenge, please use the following link: https://www.muse-challenge.org/challenge/participate

Description: Predicting the presence of humor in football press conference recordings. Available modalities: audio and video, transcriptions. The dataset offers a cross-cultural setting: all press conferences in the training and development partition are held in German, and all videos in the test partition are taken from English Premier League press conferences.

Important: this package does not include the recordings themselves. Please find them here: https://zenodo.org/record/7843460

Labels: windows of 2 seconds are given a binary label, indicating the presence and absence of humor. At least 7 human annotators labeled each video.

Dataset: MuSe-Humor is based on the Passau-Spontaneous Football Coach Humor (Passau-SFCH) dataset (paper). It includes press conference recordings of 10 different German Bundesliga football coaches collected in 2017. Moreover, for the MuSe 2023 challenge, recordings of 6 Premier League coaches have been added. The data provided here only includes segments in which the respective coach is speaking. The training data set contains recordings of 7 German-speaking coaches, development data consists of videos of 3 German-speaking coaches, and the test data set comprises recordings of 6 English-speaking coaches (one native speaker).

General: The 5th Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2024 addresses research questions that are of interest to affective computing, machine learning, and multimodal signal processing communities and encourages a fusion of their disciplines. The goal of the MuSe workshop and challenge is to gain new insights into the merits of each of the core modalities and to serve as a stimulating environment for the development and evaluation of multimodal affect recognition approaches.