13 citations · 27 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…