13 citations · 33 across the 21 of their papers we have counts for
10 papers · 1 filter
Discourse Features Enhance Detection of Document-Level Machine-Generated Content
Yupei Li, Manuel Milling, Lucia Specia +1
The availability of high-quality APIs for Large Language Models (LLMs) has facilitated the widespread creation of Machine-Generated Content (MGC), posing challenges such as academi…
autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks
Simon Rampp, Andreas Triantafyllopoulos, Manuel Milling +1
This work introduces the key operating principles for autrainer, our new deep learning training framework for computer audition tasks. autrainer is a PyTorch-based toolkit that all…
From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview
Yupei Li, Manuel Milling, Lucia Specia +1
As Artificial Intelligence (AI) technologies continue to evolve, their use in generating realistic, contextually appropriate content has expanded into various domains. Music, an ar…
Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum Learning
Simon Rampp, Manuel Milling, Andreas Triantafyllopoulos +1
Curriculum learning (CL) describes a machine learning training strategy in which samples are gradually introduced into the training process based on their difficulty. Despite a par…
Audio-based Kinship Verification Using Age Domain Conversion
Qiyang Sun, Alican Akman, Xin Jing +2
Audio-based kinship verification (AKV) is important in many domains, such as home security monitoring, forensic identification, and social network analysis. A key challenge in the…
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance
Manuel Milling, Shuo Liu, Andreas Triantafyllopoulos +2
Neural network models for audio tasks, such as automatic speech recognition (ASR) and acoustic scene classification (ASC), are susceptible to noise contamination for real-life appl…
Are you sure? Analysing Uncertainty Quantification Approaches for Real-world Speech Emotion Recognition
Oliver Schrüfer, Manuel Milling, Felix Burkhardt +2
Uncertainty Quantification (UQ) is an important building block for the reliable use of neural networks in real-world scenarios, as it can be a useful tool in identifying faulty pre…
INTERSPEECH 2009 Emotion Challenge Revisited: Benchmarking 15 Years of Progress in Speech Emotion Recognition
Andreas Triantafyllopoulos, Anton Batliner, Simon Rampp +2
We revisit the INTERSPEECH 2009 Emotion Challenge -- the first ever speech emotion recognition (SER) challenge -- and evaluate a series of deep learning models that are representat…
An automatic analysis of ultrasound vocalisations for the prediction of interaction context in captive Egyptian fruit bats
Andreas Triantafyllopoulos, Alexander Gebhard, Manuel Milling +2
Prior work in computational bioacoustics has mostly focused on the detection of animal presence in a particular habitat. However, animal sounds contain much richer information than…
Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning
Lukas Christ, Shahin Amiriparian, Manuel Milling +2
Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modeling emotional trajectorie…