66 citations · 244 across the 18 of their papers we have counts for
20 papers
AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers
Benedikt Alkin, Maurits Bleeker, Richard Kurle +4
Recent advances in neural surrogate modeling offer the potential for transformative innovations in applications such as automotive aerodynamics. Yet, industrial-scale problems ofte…
Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control
Anton R. Fuxjäger, Kristian Kozak, Matthias Dorfer +2
The ongoing transition to renewable energy is increasing the share of fluctuating power sources like wind and solar, raising power grid volatility and making grid operation increas…
Power Grid Congestion Management via Topology Optimization with AlphaZero
Matthias Dorfer, Anton R. Fuxjäger, Kristian Kozak +2
The energy sector is facing rapid changes in the transition towards clean renewable sources. However, the growing share of volatile, fluctuating renewable generation such as wind o…
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Vihang P. Patil, Markus Hofmarcher, Marius-Constantin Dinu +5
Reinforcement learning algorithms require many samples when solving complex hierarchical tasks with sparse and delayed rewards. For such complex tasks, the recently proposed RUDDER…
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
Khaled Koutini, Hamid Eghbal-zadeh, Matthias Dorfer +1
Convolutional Neural Networks (CNNs) have had great success in many machine vision as well as machine audition tasks. Many image recognition network architectures have consequently…
Learning Soft-Attention Models for Tempo-invariant Audio-Sheet Music Retrieval
Stefan Balke, Matthias Dorfer, Luis Carvalho +2
Connecting large libraries of digitized audio recordings to their corresponding sheet music images has long been a motivation for researchers to develop new cross-modal retrieval s…
Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies
Meinard Müller, Andreas Arzt, Stefan Balke +2
There has been a rapid growth of digitally available music data, including audio recordings, digitized images of sheet music, album covers and liner notes, and video clips. This hu…
Attention as a Perspective for Learning Tempo-invariant Audio Queries
Matthias Dorfer, Jan Hajič, Gerhard Widmer
Current models for audio--sheet music retrieval via multimodal embedding space learning use convolutional neural networks with a fixed-size window for the input audio. Depending on…
Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game
Matthias Dorfer, Florian Henkel, Gerhard Widmer
Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score). We start this paper by formulating score followi…
A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership
Andreu Vall, Matthias Dorfer, Markus Schedl +1
Automated music playlist continuation is a common task of music recommender systems, that generally consists in providing a fitting extension to a given playlist. Collaborative fil…
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning
Hamid Eghbal-zadeh, Matthias Dorfer, Gerhard Widmer
Convolutional Neural Networks (CNNs) can learn effective features, though have been shown to suffer from a performance drop when the distribution of the data changes from training…
Learning Audio - Sheet Music Correspondences for Score Identification and Offline Alignment
Matthias Dorfer, Andreas Arzt, Gerhard Widmer
This work addresses the problem of matching short excerpts of audio with their respective counterparts in sheet music images. We show how to employ neural network-based cross-modal…
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification
Hamid Eghbal-zadeh, Bernhard Lehner, Matthias Dorfer +1
In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the oth…
Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features: Alleviating the Cold-Start Problem for Rare and Out-of-Set Songs
Andreu Vall, Hamid Eghbal-zadeh, Matthias Dorfer +2
Automated music playlist generation is a specific form of music recommendation. Generally stated, the user receives a set of song suggestions defining a coherent listening session.…
End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss
Matthias Dorfer, Jan Schlüter, Andreu Vall +2
Cross-modality retrieval encompasses retrieval tasks where the fetched items are of a different type than the search query, e.g., retrieving pictures relevant to a given text query…
On the Potential of Simple Framewise Approaches to Piano Transcription
Rainer Kelz, Matthias Dorfer, Filip Korzeniowski +3
In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-ba…
Live Score Following on Sheet Music Images
Matthias Dorfer, Andreas Arzt, Sebastian Böck +2
In this demo we show a novel approach to score following. Instead of relying on some symbolic representation, we are using a multi-modal convolutional neural network to match the i…
Towards End-to-End Audio-Sheet-Music Retrieval
Matthias Dorfer, Andreas Arzt, Gerhard Widmer
This paper demonstrates the feasibility of learning to retrieve short snippets of sheet music (images) when given a short query excerpt of music (audio) -- and vice versa --, witho…
Towards Score Following in Sheet Music Images
Matthias Dorfer, Andreas Arzt, Gerhard Widmer
This paper addresses the matching of short music audio snippets to the corresponding pixel location in images of sheet music. A system is presented that simultaneously learns to re…
Deep Linear Discriminant Analysis
Matthias Dorfer, Rainer Kelz, Gerhard Widmer
We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preser…