755 citations · 2.7k across the 91 of their papers we have counts for
9 papers · 1 filter
The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation
Lukas Christ, Shahin Amiriparian, Alice Baird +10
The MuSe 2023 is a set of shared tasks addressing three different contemporary multimodal affect and sentiment analysis problems: In the Mimicked Emotions Sub-Challenge (MuSe-Mimic…
Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing
Georgios Rizos, Jenna Lawson, Simon Mitchell +5
We focus on using the predictive uncertainty signal calculated by Bayesian neural networks to guide learning in the self-same task the model is being trained on. Not opting for cos…
Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations
Andreas Triantafyllopoulos, Manuel Milling, Konstantinos Drossos +1
Underspecification and fairness in machine learning (ML) applications have recently become two prominent issues in the ML community. Acoustic scene classification (ASC) application…
End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks
Rodrigo Mira, Konstantinos Vougioukas, Pingchuan Ma +3
Video-to-speech is the process of reconstructing the audio speech from a video of a spoken utterance. Previous approaches to this task have relied on a two-step process where an in…
EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings
Jing Han, Zixing Zhang, Zhao Ren +1
Despite remarkable advances in emotion recognition, they are severely restrained from either the essentially limited property of the employed single modality, or the synchronous pr…
Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data
Zixing Zhang, Jing Han, Kun Qian +3
One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cop…
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square Error
Vedhas Pandit, Björn Schuller
We derive the mapping between two of the most pervasive utility functions, the mean square error () and the concordance correlation coefficient (CCC, ). Despite its drawb…
Dynamic Difficulty Awareness Training for Continuous Emotion Prediction
Zixing Zhang, Jing Han, Eduardo Coutinho +1
Time-continuous emotion prediction has become an increasingly compelling task in machine learning. Considerable efforts have been made to advance the performance of these systems.…
Detecting Road Surface Wetness from Audio: A Deep Learning Approach
Irman Abdić, Lex Fridman, Erik Marchi +4
We introduce a recurrent neural network architecture for automated road surface wetness detection from audio of tire-surface interaction. The robustness of our approach is evaluate…