Sebastian Böck
Sebastian Böck
Bestätigte E-Mail-Adresse bei
Zitiert von
Zitiert von
Improved musical onset detection with convolutional neural networks
J Schlüter, S Böck
2014 ieee international conference on acoustics, speech and signal …, 2014
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
Universal onset detection with bidirectional long-short term memory neural networks
F Eyben, S Böck, B Schuller, A Graves
Proc. 11th Intern. Soc. for Music Information Retrieval Conference, ISMIR …, 2010
Polyphonic piano note transcription with recurrent neural networks
S Böck, M Schedl
2012 IEEE international conference on acoustics, speech and signal …, 2012
Evaluating the Online Capabilities of Onset Detection Methods.
S Böck, F Krebs, M Schedl
ISMIR, 49-54, 2012
Maximum filter vibrato suppression for onset detection
S Böck, G Widmer
Proc. of the 16th Int. Conf. on Digital Audio Effects (DAFx). Maynooth …, 2013
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
Joint Beat and Downbeat Tracking with Recurrent Neural Networks.
S Böck, F Krebs, G Widmer
ISMIR, 255-261, 2016
Enhanced beat tracking with context-aware neural networks
S Böck, M Schedl
Proc. Int. Conf. Digital Audio Effects, 135-139, 2011
Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio.
F Krebs, S Böck, G Widmer
Ismir, 227-232, 2013
Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters.
S Böck, F Krebs, G Widmer
ISMIR, 625-631, 2015
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles.
S Böck, F Krebs, G Widmer
ISMIR, 603-608, 2014
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
An Efficient State-Space Model for Joint Tempo and Meter Tracking.
F Krebs, S Böck, G Widmer
ISMIR, 72-78, 2015
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
Downbeat Tracking Using Beat Synchronous Features with Recurrent Neural Networks.
F Krebs, S Böck, M Dorfer, G Widmer
ISMIR, 129-135, 2016
Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other.
S Böck, MEP Davies, P Knees
ISMIR, 486-493, 2019
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
Fast Identification of Piece and Score Position via Symbolic Fingerprinting.
A Arzt, S Böck, G Widmer
ISMIR, 433-438, 2012
Bridging the audio-symbolic gap: The discovery of repeated note content directly from polyphonic music audio
T Collins, S Böck, F Krebs, G Widmer
Audio Engineering Society Conference: 53rd International Conference …, 2014
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20