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ORCIDGerhard Widmer
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Publications
- 2019
[c174]Rainer Kelz, Sebastian Böck, Gerhard Widmer
:
Deep Polyphonic ADSR Piano Note Transcription. ICASSP 2019: 246-250- [i59]Rainer Kelz, Sebastian Böck, Gerhard Widmer:
Multitask Learning for Polyphonic Piano Transcription, a Case Study. CoRR abs/1902.04390 (2019) - [i55]Rainer Kelz, Sebastian Böck, Gerhard Widmer:
Deep Polyphonic ADSR Piano Note Transcription. CoRR abs/1906.09165 (2019) - 2016
- [c140]Florian Krebs, Sebastian Böck, Matthias Dorfer, Gerhard Widmer:
Downbeat Tracking Using Beat Synchronous Features with Recurrent Neural Networks. ISMIR 2016: 129-135 - [c138]Sebastian Böck, Florian Krebs, Gerhard Widmer:
Joint Beat and Downbeat Tracking with Recurrent Neural Networks. ISMIR 2016: 255-261 - [c137]Rainer Kelz, Matthias Dorfer, Filip Korzeniowski, Sebastian Böck, Andreas Arzt, Gerhard Widmer:
On the Potential of Simple Framewise Approaches to Piano Transcription. ISMIR 2016: 475-481 - [c133]Sebastian Böck, Filip Korzeniowski, Jan Schlüter, Florian Krebs, Gerhard Widmer:
madmom: A New Python Audio and Music Signal Processing Library. ACM Multimedia 2016: 1174-1178 - [i10]Sebastian Böck, Filip Korzeniowski, Jan Schlüter, Florian Krebs, Gerhard Widmer:
madmom: a new Python Audio and Music Signal Processing Library. CoRR abs/1605.07008 (2016) - [i3]Matthias Dorfer, Andreas Arzt, Sebastian Böck, Amaury Durand, Gerhard Widmer:
Live Score Following on Sheet Music Images. CoRR abs/1612.05076 (2016) - [i1]Rainer Kelz, Matthias Dorfer, Filip Korzeniowski, Sebastian Böck, Andreas Arzt, Gerhard Widmer:
On the Potential of Simple Framewise Approaches to Piano Transcription. CoRR abs/1612.05153 (2016) - 2015
- [c131]Bernhard Lehner, Gerhard Widmer, Sebastian Böck:
A low-latency, real-time-capable singing voice detection method with LSTM recurrent neural networks. EUSIPCO 2015: 21-25 - [c127]Florian Krebs, Sebastian Böck, Gerhard Widmer:
An Efficient State-Space Model for Joint Tempo and Meter Tracking. ISMIR 2015: 72-78 - [c121]Sebastian Böck, Florian Krebs, Gerhard Widmer:
Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters. ISMIR 2015: 625-631 - 2014
- [c119]Andreas Arzt, Sebastian Böck, Sebastian Flossmann, Harald Frostel, Martin Gasser, Cynthia C. S. Liem, Gerhard Widmer:
The Piano Music Companion. ECAI 2014: 1221-1222 - [c116]Filip Korzeniowski, Sebastian Böck, Gerhard Widmer:
Probabilistic Extraction of Beat Positions from a Beat Activation Function. ISMIR 2014: 513-518 - [c114]Sebastian Böck, Florian Krebs, Gerhard Widmer:
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles. ISMIR 2014: 603-608 - [c113]Tom Collins, Sebastian Böck, Florian Krebs, Gerhard Widmer:
Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio. Semantic Audio 2014 - 2013
- [c110]Florian Krebs, Sebastian Böck, Gerhard Widmer:
Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio. ISMIR 2013: 227-232 - [c109]Sebastian Böck, Gerhard Widmer:
Local Group Delay Based Vibrato and Tremolo Suppression for Onset Detection. ISMIR 2013: 361-366 - 2012
- [c106]Andreas Arzt, Gerhard Widmer, Sebastian Böck, Reinhard Sonnleitner, Harald Frostel:
Towards a Complete Classical Music Companion. ECAI 2012: 67-72 - [c104]Andreas Arzt, Sebastian Böck, Gerhard Widmer:
Fast Identification of Piece and Score Position via Symbolic Fingerprinting. ISMIR 2012: 433-438 - 2011
- [c102]Bernhard Niedermayer, Sebastian Böck, Gerhard Widmer:
On the Importance of "Real" Audio Data for MIR Algorithm Evaluation at the Note-Level - A Comparative Study. ISMIR 2011: 543-548
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