no code implementations • 31 Mar 2022 • Xin Jing, Shuo Liu, Emilia Parada-Cabaleiro, Andreas Triantafyllopoulos, Meishu Song, Zijiang Yang, Björn W. Schuller
Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission.
no code implementations • 3 Mar 2022 • Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz
This work investigates the effect of gender-stereotypical biases in the content of retrieved results on the relevance judgement of users/annotators.
1 code implementation • 19 Jan 2022 • Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz
To facilitate the studies of gender bias in the retrieval results of IR systems, we introduce Gender Representation-Bias for Information Retrieval (Grep-BiasIR), a novel thoroughly-audited dataset consisting of 118 bias-sensitive neutral search queries.
1 code implementation • 4 Aug 2021 • Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl, Elham Motamedi, Marko Tkalcic, Elisabeth Lex
In this paper, we describe a psychology-informed approach to model and predict music relistening behavior that is inspired by studies in music psychology, which relate music preferences to human memory.