13 citations · 33 across the 21 of their papers we have counts for
24 papers
PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning
Max Rosenblattl, Nikhil Krishnaswamy, Leon Nissen +9
Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text rather than as temporal evidence…
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data
Max Rosenblattl, Milana Gurbanova, Karl Aurel Deck +16
Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and heterogeneity make their processi…
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…
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…
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…
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…
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification
Manuel Milling, Andreas Triantafyllopoulos, Iosif Tsangko +2
The correlation between the sharpness of loss minima and generalisation in the context of deep neural networks has been subject to discussion for a long time. Whilst mostly investi…
HEAR4Health: A blueprint for making computer audition a staple of modern healthcare
Andreas Triantafyllopoulos, Alexander Kathan, Alice Baird +20
Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalen…
Automatic Emotion Modelling in Written Stories
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 modelling emotional trajectori…
Depression Diagnosis and Forecast based on Mobile Phone Sensor Data
Xiangheng He, Andreas Triantafyllopoulos, Alexander Kathan +9
Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires…
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
Alexander Kathan, Andreas Triantafyllopoulos, Xiangheng He +9
Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A comm…
Predicting Sex and Stroke Success -- Computer-aided Player Grunt Analysis in Tennis Matches
Lukas Stappen, Manuel Milling, Valentin Munst +2
Professional athletes increasingly use automated analysis of meta- and signal data to improve their training and game performance. As in other related human-to-human research field…
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…
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era
Shahin Amiriparian, Artem Sokolov, Ilhan Aslan +9
Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challengi…