2026 Gerhard Widmer: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	59	3402	3306	20	19	390	15327

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Computer Science

3402

3306

390

15327

Overview

Gerhard Widmer is affiliated with Johannes Kepler University of Linz in Austria. Their research primarily focuses on computer science with a substantial emphasis on signal processing. Widmer's work spans several subfields including signal processing, computer vision and pattern recognition, artificial intelligence, cognitive neuroscience, and music.

The main topics covered in their research include:

Music and Audio Processing
Music Technology and Sound Studies
Speech and Audio Processing
Neuroscience and Music Perception
Speech Recognition and Synthesis
Diverse Musicological Studies
Anomaly Detection Techniques and Applications

Widmer has contributed to a variety of publication venues, with repeated appearances in the following:

arXiv (Cornell University)
Zenodo (CERN European Organization for Nuclear Research)
2021 29th European Signal Processing Conference (EUSIPCO)
Transactions of the International Society for Music Information Retrieval
IEEE/ACM Transactions on Audio Speech and Language Processing

Notable recent papers authored or coauthored by Widmer include:

Efficient Training of Audio Transformers with Patchout, Interspeech 2022
Receptive Field Regularization Techniques for Audio Classification and Tagging With Deep Convolutional Neural Networks, IEEE/ACM Transactions on Audio Speech and Language Processing, 2021
Dynamic Convolutional Neural Networks as Efficient Pre-Trained Audio Models, IEEE/ACM Transactions on Audio Speech and Language Processing, 2024
On End-to-End White-Box Adversarial Attacks in Music Information Retrieval, Transactions of the International Society for Music Information Retrieval, 2021
Differentiable Short-Term Models for Efficient Online Learning and Prediction in Monophonic Music, Transactions of the International Society for Music Information Retrieval, 2022

Widmer frequently collaborates with a group of researchers, including:

Khaled Koutini
Paul Primus
Emmanouil Karystinaios
Francesco Foscarin
Florian Schmid

Best Publications

Learning in the presence of concept drift and hidden contexts

Gerhard Widmer;Miroslav Kubat

2215
Citations
Learning in the presence of concept drift and hidden contexts

Unknown

620
Citations
Incremental reduced error pruning

Johannes Fürnkranz;Gerhard Widmer

558
Citations
Improvements of Audio-Based Music Similarity and Genre Classificaton.

Elias Pampalk;Arthur Flexer;Gerhard Widmer

342
Citations
Computational Models of Expressive Music Performance: The State of the Art

Gerhard Widmer;Werner Goebl

307
Citations
Effective learning in dynamic environments by explicit context tracking

Gerhard Widmer;Miroslav Kubat

260
Citations
madmom: A New Python Audio and Music Signal Processing Library

Sebastian Böck;Filip Korzeniowski;Jan Schlüter;Florian Krebs

258
Citations
MATCH: A Music Alignment Tool Chest

Simon Dixon;Gerhard Widmer

222
Citations
DYNAMIC PLAYLIST GENERATION BASED ON SKIPPING BEHAVIOR

Elias Pampalk;Tim Pohle;Gerhard Widmer

217
Citations
Exploring Music Collections by Browsing Different Views

Elias Pampalk;Simon Dixon;Gerhard Widmer;Gerhard Widmer

212
Citations
Evaluating Rhythmic descriptors for Musical Genre Classification

Simon Dixon;Elias Pampalk;Gerhard Widmer

208
Citations
Towards Characterisation of Music via Rhythmic Patterns

Simon Dixon;Fabien Gouyon;Gerhard Widmer

200
Citations
Machine Discoveries: A Few Simple, Robust Local Expression Principles

Gerhard Widmer

193
Citations
Tracking Context Changes through Meta-Learning

Gerhard Widmer

189
Citations
In search of the Horowitz factor

Gerhard Widmer;Simon Dixon;Werner Goebl;Elias Pampalk

188
Citations
Prediction of Ordinal Classes Using Regression Trees

Stefan Kramer;Gerhard Widmer;Bernhard Pfahringer;Michael De Groeve

174
Citations
Efficient Training of Audio Transformers with Patchout.

Khaled Koutini;Jan Schlüter;Hamid Eghbal-zadeh;Gerhard Widmer

168
Citations
Classification of Dance Music by Periodicity Patterns

Simon Dixon;Elias Pampalk;Gerhard Widmer

161
Citations
PLAYLIST GENERATION USING START AND END SONGS

Arthur Flexer;Dominik Schnitzer;Martin Gasser;Gerhard Widmer

159
Citations
Artist Classification with Web-Based Data.

Peter Knees;Elias Pampalk;Gerhard Widmer

155
Citations
An innovative three-dimensional user interface for exploring music collections enriched

Peter Knees;Markus Schedl;Tim Pohle;Gerhard Widmer

144
Citations
Maximum Filter Vibrato Suppression for Onset Detection

Sebastian Böck;Gerhard Widmer

143
Citations

Frequent Co-Authors

Markus Schedl Johannes Kepler University of Linz

Simon Dixon Queen Mary University of London

Xavier Serra Pompeu Fabra University

Jan Hajič Charles University

Bernhard Moser Cardiff University

Meinard Müller University of Erlangen-Nuremberg

Stefan Kramer Johannes Gutenberg University of Mainz

Peter F. Ambros St Anna Children's Hospital

Johannes Fürnkranz Johannes Kepler University of Linz

Efstathios Stamatatos University of the Aegean

External Links

Personal website of Gerhard Widmer Google Scholar page

If you think any of the details on this page are incorrect, let us know.

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