9 papers
How Class Ontology and Data Scale Affect Audio Transfer Learning
Manuel Milling, Andreas Triantafyllopoulos, Alexander Gebhard +2
Transfer learning is a crucial concept within deep learning that allows artificial neural networks to benefit from a large pre-training data basis when confronted with a task of li…
Enhancing Efficiency and Performance in Deepfake Audio Detection through Neuron-level Dropin & Neuroplasticity Mechanisms
Yupei Li, Shuaijie Shao, Manuel Milling +1
Current audio deepfake detection has achieved remarkable performance using diverse deep learning architectures such as ResNet, and has seen further improvements with the introducti…
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…
Large Language Models for Depression Recognition in Spoken Language Integrating Psychological Knowledge
Yupei Li, Shuaijie Shao, Manuel Milling +1
Depression is a growing concern gaining attention in both public discourse and AI research. While deep neural networks (DNNs) have been used for recognition, they still lack real-w…
Neuroplasticity in Artificial Intelligence -- An Overview and Inspirations on Drop In & Out Learning
Yupei Li, Manuel Milling, Björn W. Schuller
Artificial Intelligence (AI) has achieved new levels of performance and spread in public usage with the rise of deep neural networks (DNNs). Initially inspired by human neurons and…
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…