Improved musical onset detection with convolutional neural networks J Schlüter, S Böck 2014 ieee international conference on acoustics, speech and signal …, 2014 | 315 | 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 | 311 | 2016 |
Universal onset detection with bidirectional long-short term memory neural networks F Eyben, S Böck, B Schuller, A Graves | 222 | 2010 |
Polyphonic piano note transcription with recurrent neural networks S Böck, M Schedl 2012 IEEE international conference on acoustics, speech and signal …, 2012 | 197 | 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 | 180 | 2013 |
Joint Beat and Downbeat Tracking with Recurrent Neural Networks. S Böck, F Krebs, G Widmer ISMIR, 255-261, 2016 | 179 | 2016 |
Evaluating the Online Capabilities of Onset Detection Methods. S Böck, F Krebs, M Schedl ISMIR, 49-54, 2012 | 179 | 2012 |
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 | 159 | 2016 |
Enhanced beat tracking with context-aware neural networks S Böck, M Schedl Proc. Int. Conf. Digital Audio Effects, 135-139, 2011 | 147 | 2011 |
Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio. F Krebs, S Böck, G Widmer Ismir, 227-232, 2013 | 118 | 2013 |
Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters. S Böck, F Krebs, G Widmer ISMIR, 625-631, 2015 | 115 | 2015 |
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles. S Böck, F Krebs, G Widmer ISMIR, 603-608, 2014 | 110 | 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 | 94 | 2012 |
An Efficient State-Space Model for Joint Tempo and Meter Tracking. F Krebs, S Böck, G Widmer ISMIR, 72-78, 2015 | 93 | 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 | 87 | 2015 |
Musical onset detection with convolutional neural networks J Schlüter, S Böck 6th international workshop on machine learning and music (MML), Prague …, 2013 | 81 | 2013 |
Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other. S Böck, MEP Davies, P Knees ISMIR, 486-493, 2019 | 74 | 2019 |
Deconstruct, Analyse, Reconstruct: How to improve Tempo, Beat, and Downbeat Estimation S Böck, MEP Davies | 70 | 2020 |
Temporal convolutional networks for musical audio beat tracking EP MatthewDavies, S Böck 2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019 | 70 | 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 | 62 | 2015 |