| 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 |
| 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 |
| 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 |
| Downbeat Tracking Using Beat Synchronous Features with Recurrent Neural Networks. F Krebs, S Böck, M Dorfer, G Widmer ISMIR, 129-135, 2016 | 60 | 2016 |
| 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 | 45 | 2014 |
| Tracking the “odd”: Meter inference in a culturally diverse music corpus A Holzapfel, F Krebs, A Srinivasamurthy ISMIR-International Conference on Music Information Retrieval, 425-430, 2014 | 43 | 2014 |
| Inferring metrical structure in music using particle filters F Krebs, A Holzapfel, AT Cemgil, G Widmer IEEE/ACM Transactions on audio, speech, and language processing 23 (5), 817-827, 2015 | 31 | 2015 |
| An assessment of learned score features for modeling expressive dynamics in music M Grachten, F Krebs IEEE Transactions on Multimedia 16 (5), 1211-1218, 2014 | 29 | 2014 |
| Tracking rests and tempo changes: Improved score following with particle filters F Korzeniowski, F Krebs, A Arzt, G Widmer ICMC, 2013 | 15 | 2013 |
| Unsupervised learning and refinement of rhythmic patterns for beat and downbeat tracking F Krebs, F Korzeniowski, M Grachten, G Widmer 2014 22nd European Signal Processing Conference (EUSIPCO), 611-615, 2014 | 12 | 2014 |
| Combining score and filter based models to predict tempo fluctuations in expressive music performances F Krebs, M Grachten Proceedings of the Ninth Sound and Music Computing Conference (SMC), 358-363, 2012 | 8 | 2012 |
| Mirex submissions for chord recognition and key estimation 2017 F Korzeniowski, S Böck, F Krebs, G Widmer MIREX evaluation results, 2017 | 7 | 2017 |
| Near-miss accidents–classification and automatic detection G Thallinger, F Krebs, E Kolla, P Vertal, G Kasanický, H Neuschmied, ... Intelligent Transport Systems–From Research and Development to the Market …, 2018 | 6 | 2018 |
| Acoustic monitoring using PyzoFlex®: A novel printed sensor for smart consumer products M Blass, F Krebs, C Amon, M Adler, M Zirkl, A Tschepp, F Graf Journal of Physics: Conference Series 1896 (1), 012022, 2021 | 3 | 2021 |
| Evaluation of simmarc: an audiovisual system for the detection of near-miss accidents F Krebs, G Thallinger, H Neuschmied, F Graf, G Huber, K Fallast, P Vertal, ... Intelligent Transport Systems. From Research and Development to the Market …, 2020 | 3 | 2020 |
Gerhard WidmerProfessor of Computer Science, Johannes Kepler University Linz在 jku.at 的电子邮件经过验证
