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Andreas K. Maier

11 accepted papers

2025

A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids

Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro, Tomás Arias-Vergara, Andreas K. Maier, Johann Jäger +1
ICASSP 2025accepted

The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional relay protection systems, which operate based on static rules and predefined thresholds, are inadequate for addressing these…

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2025

Same Semantics of the Signal - What Do We Cluster with what Representation

Alexander Barnhill, Oliver Traub, Andreas K. Maier, Elmar Nöth, Christian Bergler
ICASSP 2025accepted

Semantic clustering of bioacoustic signals is crucial for a deeper understanding of intra-class differences. This is particularly important for understanding killer whale signals, as their vocalizations are learned behaviors and determination of matrilineal-specific dialects is reliant upon subtle d…

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2024

Longitudinal Modeling of Depression Shifts Using Speech and Language

Paula Andrea Pérez-Toro, Judith Dineley, Agnieszka Kaczkowska, Pauline Conde, Yuezhou Zhang, Faith Matcham +12
ICASSP 2024accepted

Speech analysis can provide a potential non-invasive and objective means of assessing and monitoring an individual’s mental health. Most studies to date have focused on cross-sectional analysis and have not explored the benefits of speech analysis as a longitudinal monitoring tool that can assist in…

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2024

Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation

Mathias Öttl*, Frauke Wilm, Jana Steenpass, Jingna Qiu, Matthias Rübner, Prof Arndt Hartmann +6
ECCV 2024poster

"Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generating images with unseen characteristics beneficial for downstream tasks has received limited attention. To bridge this g…

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2024

Transforming Cardiovascular Health: a Transformer-Based Approach to Continuous, Non-Invasive Blood Pressure Estimation via Radar Sensing

Nastassia Vysotskaya, Noah Maul, Alessandra Fusco, Souvik Hazra, Jens Harnisch, Tomás Arias-Vergara +1
ICASSP 2024accepted

Hypertension is considered to be one of the most critical risk factors for cardiovascular diseases. As such, continuous, accurate and non-invasive monitoring of blood pressure (BP) is of utmost importance and research on such approaches is gaining momentum. In this study, we propose a novel transfor…

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2023

Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer's Disease Using Forestnets

Paula Andrea Pérez-Toro, Dalia Rodríguez-Salas, Tomás Arias-Vergara, Sebastian P. Bayerl, Philipp Klumpp, Korbinian Riedhammer +4
ICASSP 2023accepted

Progressive loss of memory is the most known symptom of Alzheimer’s Disease (AD); however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information…

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2022

ORCA-PARTY: An Automatic Killer Whale Sound Type Separation Toolkit Using Deep Learning

Christian Bergler, Manuel Schmitt, Andreas K. Maier, Rachael Xi Cheng, Volker Barth, Elmar Nöth
ICASSP 2022accepted

Data-driven and machine-based analysis of massive bioacoustic data collections, in particular acoustic regions containing a substantial number of vocalizations events, is essential and extremely valuable to identify recurring vocal paradigms. However, these acoustic sections are usually characterize…

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2021

Self-Supervised Learning of Domain-Invariant Local Features for Robust Visual Localization Under Challenging Conditions

Moritz Venator, Yassine El Himer, Selcuk Aklanoglu, Erich Bruns, Andreas K. Maier
RA-L 2021

Visual localization provides the basis for many robotics applications such as autonomous navigation or augmented reality. Especially in outdoor scenes, robust localization requires local features which can be reliably extracted and matched under changing conditions. Previous approaches have applied

Cited by 10Source ↗Scholar ↗
2020

CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding

Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B., Marc Aubreville, Andreas K. Maier
ICASSP 2020accepted

Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either do not consider real-time and frequency resolution constrains or result in poor quality under very noisy conditions.To…

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2020

The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks

Faezeh Nejati Hatamian, Nishant Ravikumar, Sulaiman Vesal, Felix P. Kemeth, Matthias Struck, Andreas K. Maier
ICASSP 2020accepted

Cardiovascular diseases are the most common cause of mortality worldwide. Detection of atrial fibrillation (AF) in the asymptomatic stage can help prevent strokes. It also improves clinical decision making through the delivery of suitable treatment such as, anticoagulant therapy, in a timely manner.…

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2019

Segmentation, Classification, and Visualization of Orca Calls Using Deep Learning

Hendrik Schröter, Elmar Nöth, Andreas K. Maier, Rachael Cheng, Volker Barth, Christian Bergler
ICASSP 2019accepted

Audiovisual media are increasingly used to study the communication and behavior of animal groups, e.g. by placing microphones in the animals habitat resulting in huge datasets with only a small amount of animal interactions. The Orcalab has recorded orca whales since 1973 using stationary underwater…

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