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Markus Frohmann

Institute of Computational Perception, Johannes Kepler University Linz

4 papers hereh-index 4103 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL4
affiliations
  • Institute of Computational Perception, Johannes Kepler University Linz
  • Linz Institute of Technology, AI Lab
HomepageORCID 0009-0007-2404-6096
same name
  • Markus Frohmann — 2 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

cs.CL2025

Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion

Markus Frohmann, Gabriel Meseguer-Brocal, Markus Schedl +1

The rapid advancement of AI-based music generation tools is revolutionizing the music industry but also posing challenges to artists, copyright holders, and providers alike. This n…

cs.CL2025

Synthetic Lyrics Detection Across Languages and Genres

Yanis Labrak, Markus Frohmann, Gabriel Meseguer-Brocal +1

In recent years, the use of large language models (LLMs) to generate music content, particularly lyrics, has gained in popularity. These advances provide valuable tools for artists…

cs.CL2024

Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization

Shahed Masoudian, Markus Frohmann, Navid Rekabsaz +1

Language models frequently inherit societal biases from their training data. Numerous techniques have been proposed to mitigate these biases during both the pre-training and fine-t…

cs.CL2024

Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation

Markus Frohmann, Igor Sterner, Ivan Vulić +2

Segmenting text into sentences plays an early and crucial role in many NLP systems. This is commonly achieved by using rule-based or statistical methods relying on lexical features…

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