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Sebastian Böck
Sebastian Böck
NXAI
Bestätigte E-Mail-Adresse bei nx-ai.com
Titel
Zitiert von
Zitiert von
Jahr
Madmom: A new python audio and music signal processing library
S Böck, F Korzeniowski, J Schlüter, F Krebs, G Widmer
Proceedings of the 24th ACM international conference on Multimedia, 1174-1178, 2016
3412016
Improved musical onset detection with convolutional neural networks
J Schlüter, S Böck
2014 ieee international conference on acoustics, speech and signal …, 2014
3302014
Universal onset detection with bidirectional long-short term memory neural networks
F Eyben, S Böck, B Schuller, A Graves
2292010
Polyphonic piano note transcription with recurrent neural networks
S Böck, M Schedl
2012 IEEE international conference on acoustics, speech and signal …, 2012
2072012
Maximum filter vibrato suppression for onset detection
S Böck, G Widmer
Proc. of the 16th Int. Conf. on Digital Audio Effects (DAFx). Maynooth …, 2013
1982013
Joint Beat and Downbeat Tracking with Recurrent Neural Networks.
S Böck, F Krebs, G Widmer
ISMIR, 255-261, 2016
1942016
Evaluating the Online Capabilities of Onset Detection Methods.
S Böck, F Krebs, M Schedl
ISMIR, 49-54, 2012
1892012
On the potential of simple framewise approaches to piano transcription
R Kelz, M Dorfer, F Korzeniowski, S Böck, A Arzt, G Widmer
arXiv preprint arXiv:1612.05153, 2016
1702016
Enhanced beat tracking with context-aware neural networks
S Böck, M Schedl
Proc. Int. Conf. Digital Audio Effects, 135-139, 2011
1582011
Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio.
F Krebs, S Böck, G Widmer
Ismir, 227-232, 2013
1242013
Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters.
S Böck, F Krebs, G Widmer
ISMIR, 625-631, 2015
1202015
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles.
S Böck, F Krebs, G Widmer
ISMIR, 603-608, 2014
1172014
An Efficient State-Space Model for Joint Tempo and Meter Tracking.
F Krebs, S Böck, G Widmer
ISMIR, 72-78, 2015
1002015
Online real-time onset detection with recurrent neural networks
S Böck, A Arzt, F Krebs, M Schedl
Proceedings of the 15th International Conference on Digital Audio Effects …, 2012
962012
Two data sets for tempo estimation and key detection in electronic dance music annotated from user corrections
P Knees, Á Faraldo Pérez, H Boyer, R Vogl, S Böck, F Hörschläger, ...
Proceedings of the 16th International Society for Music Information …, 2015
952015
Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other.
S Böck, MEP Davies, P Knees
ISMIR, 486-493, 2019
842019
Musical onset detection with convolutional neural networks
J Schlüter, S Böck
6th international workshop on machine learning and music (MML), Prague …, 2013
842013
Deconstruct, Analyse, Reconstruct: How to improve Tempo, Beat, and Downbeat Estimation
S Böck, MEP Davies
832020
Temporal convolutional networks for musical audio beat tracking
EP MatthewDavies, S Böck
2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019
812019
A low-latency, real-time-capable singing voice detection method with LSTM recurrent neural networks
B Lehner, G Widmer, S Bock
2015 23rd European signal processing conference (EUSIPCO), 21-25, 2015
642015
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