no code implementations • 15 Feb 2023 • Anton R. Fuxjäger, Kristian Kozak, Matthias Dorfer, Patrick M. Blies, Marcel Wasserer
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 increasingly complex and costly.
1 code implementation • 10 Nov 2022 • Matthias Dorfer, Anton R. Fuxjäger, Kristian Kozak, Patrick M. Blies, Marcel Wasserer
Unfortunately, redispatching of fossil generators leads to excessive grid operation costs and higher emissions, which is in direct opposition to the decarbonization of the energy sector.
1 code implementation • 29 Sep 2020 • Vihang P. Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter
For such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks.
General Reinforcement Learning Multiple Sequence Alignment +1
3 code implementations • 3 Jul 2019 • Khaled Koutini, Hamid Eghbal-zadeh, Matthias Dorfer, Gerhard Widmer
To this end, we analyse the receptive field (RF) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models.
no code implementations • 26 Jun 2019 • Stefan Balke, Matthias Dorfer, Luis Carvalho, Andreas Arzt, Gerhard Widmer
Quantitative and qualitative results on synthesized piano data indicate that this attention increases the robustness of the retrieval system by focusing on different parts of the input representation based on the tempo of the audio.
no code implementations • 12 Feb 2019 • Meinard Müller, Andreas Arzt, Stefan Balke, Matthias Dorfer, Gerhard Widmer
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.
no code implementations • 15 Sep 2018 • Matthias Dorfer, Jan Hajič jr., 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.
1 code implementation • 17 Jul 2018 • 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).
no code implementations • 10 Nov 2017 • Hamid Eghbal-zadeh, Matthias Dorfer, Gerhard Widmer
To tackle this problem, we propose Deep Within-Class Covariance Analysis (DWCCA), a deep neural network layer that significantly reduces the within-class covariance of a DNN's representation, improving performance on unseen test data from a shifted distribution.
no code implementations • 20 Jun 2017 • Hamid Eghbal-zadeh, Bernhard Lehner, Matthias Dorfer, Gerhard Widmer
Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique.
no code implementations • 15 Dec 2016 • 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.
no code implementations • 15 Dec 2016 • 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 --, without any symbolic representation of music or scores.
2 code implementations • 15 Dec 2016 • Rainer Kelz, Matthias Dorfer, Filip Korzeniowski, Sebastian Böck, Andreas Arzt, Gerhard Widmer
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-based framewise transcription.
2 code implementations • 15 Nov 2015 • Matthias Dorfer, Rainer Kelz, Gerhard Widmer
The central idea of this paper is to put LDA on top of a deep neural network.