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Sebastian Böck
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2020 – today
- 2020
- [c27]Sebastian Böck, Matthew E. P. Davies:
Deconstruct, Analyse, Reconstruct: How to improve Tempo, Beat, and Downbeat Estimation. ISMIR 2020: 574-582 - [i6]Alexander Schindler, Thomas Lidy, Sebastian Böck:
Deep Learning for MIR Tutorial. CoRR abs/2001.05266 (2020)
2010 – 2019
- 2019
- [j3]Markus Immitzer
, Martin Neuwirth, Sebastian Böck, Harald Brenner, Francesco Vuolo
Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data. Remote. Sens. 11(22): 2599 (2019) - [c26]E. P. MatthewDavies, Sebastian Böck:
Temporal convolutional networks for musical audio beat tracking. EUSIPCO 2019: 1-5 - [c25]Rainer Kelz, Sebastian Böck, Gerhard Widmer
Deep Polyphonic ADSR Piano Note Transcription. ICASSP 2019: 246-250 - [c24]Sebastian Böck, Matthew E. P. Davies, Peter Knees:
Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other. ISMIR 2019: 486-493 - [i5]Rainer Kelz, Sebastian Böck, Gerhard Widmer:
Multitask Learning for Polyphonic Piano Transcription, a Case Study. CoRR abs/1902.04390 (2019) - [i4]Rainer Kelz, Sebastian Böck, Gerhard Widmer:
Deep Polyphonic ADSR Piano Note Transcription. CoRR abs/1906.09165 (2019) - 2017
- [j2]Sebastian Böck, Markus Immitzer
On the Objectivity of the Objective Function - Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy. Remote. Sens. 9(8): 769 (2017) - 2016
- [j1]Wai-Tim Ng
Mapping Prosopis spp. with Landsat 8 data in arid environments: Evaluating effectiveness of different methods and temporal imagery selection for Hargeisa, Somaliland. Int. J. Appl. Earth Obs. Geoinformation 53: 76-89 (2016) - [c23]Florian Krebs, Sebastian Böck, Matthias Dorfer, Gerhard Widmer:
Downbeat Tracking Using Beat Synchronous Features with Recurrent Neural Networks. ISMIR 2016: 129-135 - [c22]Sebastian Böck, Florian Krebs, Gerhard Widmer:
Joint Beat and Downbeat Tracking with Recurrent Neural Networks. ISMIR 2016: 255-261 - [c21]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 - [c20]Sebastian Böck, Filip Korzeniowski, Jan Schlüter
madmom: A New Python Audio and Music Signal Processing Library. ACM Multimedia 2016: 1174-1178 - [i3]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) - [i2]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
- [c19]Bernhard Lehner, Gerhard Widmer
A low-latency, real-time-capable singing voice detection method with LSTM recurrent neural networks. EUSIPCO 2015: 21-25 - [c18]Florian Krebs, Sebastian Böck, Gerhard Widmer:
An Efficient State-Space Model for Joint Tempo and Meter Tracking. ISMIR 2015: 72-78 - [c17]Peter Knees, Ángel Faraldo, Perfecto Herrera, Richard Vogl, Sebastian Böck, Florian Hörschläger, Mickael Le Goff:
Two Data Sets for Tempo Estimation and Key Detection in Electronic Dance Music Annotated from User Corrections. ISMIR 2015: 364-370 - [c16]Sebastian Böck, Florian Krebs, Gerhard Widmer:
Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters. ISMIR 2015: 625-631 - 2014
- [c15]Andreas Arzt, Sebastian Böck
The Piano Music Companion. ECAI 2014: 1221-1222 - [c14]Jan Schlüter
Improved musical onset detection with Convolutional Neural Networks. ICASSP 2014: 6979-6983 - [c13]Filip Korzeniowski, Sebastian Böck, Gerhard Widmer:
Probabilistic Extraction of Beat Positions from a Beat Activation Function. ISMIR 2014: 513-518 - [c12]Sebastian Böck, Florian Krebs, Gerhard Widmer:
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles. ISMIR 2014: 603-608 - [c11]Matthew E. P. Davies, Sebastian Böck:
Evaluating the Evaluation Measures for Beat Tracking. ISMIR 2014: 637-642 - [c10]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
- [c9]Florian Krebs, Sebastian Böck, Gerhard Widmer:
Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio. ISMIR 2013: 227-232 - [c8]Sebastian Böck, Gerhard Widmer:
Local Group Delay Based Vibrato and Tremolo Suppression for Onset Detection. ISMIR 2013: 361-366 - 2012
- [c7]Andreas Arzt, Gerhard Widmer
Towards a Complete Classical Music Companion. ECAI 2012: 67-72 - [c6]Sebastian Böck, Markus Schedl:
Polyphonic piano note transcription with recurrent neural networks. ICASSP 2012: 121-124 - [c5]Sebastian Böck, Florian Krebs, Markus Schedl:
Evaluating the Online Capabilities of Onset Detection Methods. ISMIR 2012: 49-54 - [c4]Andreas Arzt, Sebastian Böck, Gerhard Widmer:
Fast Identification of Piece and Score Position via Symbolic Fingerprinting. ISMIR 2012: 433-438 - 2011
- [c3]Markus Schedl, Peter Knees, Sebastian Böck:
Investigating the Similarity Space of Music Artists on the Micro-Blogosphere. ISMIR 2011: 323-328 - [c2]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 - 2010
- [c1]Florian Eyben, Sebastian Böck, Björn W. Schuller, Alex Graves:
Universal Onset Detection with Bidirectional Long Short-Term Memory Neural Networks. ISMIR 2010: 589-594
Coauthor Index
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