| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688, 2016 | 810 | 2016 |
| Lasagne: first release S Dieleman, J Schlüter, C Raffel, E Olson, SK Sønderby, D Nouri, ... Zenodo: Geneva, Switzerland, 2015 | 395* | 2015 |
| Improved musical onset detection with convolutional neural networks J Schlüter, S Böck 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 230 | 2014 |
| Exploring Data Augmentation for Improved Singing Voice Detection with Neural Networks J Schlüter, T Grill 16th International Society for Music Information Retrieval Conference (ISMIR), 2015 | 163 | 2015 |
| madmom: a new Python Audio and Music Signal Processing Library S Böck, F Korzeniowski, J Schlüter, F Krebs, G Widmer Proceedings of the 2016 ACM on Multimedia Conference, 1174-1178, 2016 | 127 | 2016 |
| Deep learning for audio signal processing H Purwins, B Li, T Virtanen, J Schlüter, SY Chang, T Sainath IEEE Journal of Selected Topics in Signal Processing 13 (2), 206-219, 2019 | 125 | 2019 |
| Boundary Detection in Music Structure Analysis using Convolutional Neural Networks. K Ullrich, J Schlüter, T Grill ISMIR, 417-422, 2014 | 115 | 2014 |
| Roadmap for music information research X Serra, M Magas, E Benetos, M Chudy, S Dixon, A Flexer, E Gómez, ... MIRES Consortium, 2013 | 77 | 2013 |
| Learning to Pinpoint Singing Voice from Weakly Labeled Examples J Schlüter ISMIR, 44-50, 2016 | 71 | 2016 |
| Musical onset detection with convolutional neural networks J Schlüter, S Böck 6th International Workshop on Machine Learning and Music (MML), Prague …, 2013 | 69 | 2013 |
| Two convolutional neural networks for bird detection in audio signals T Grill, J Schlüter Proceedings of the 25th European Signal Processing Conference (EUSIPCO), 2017 | 66 | 2017 |
| A MIREX Meta-analysis of Hubness in Audio Music Similarity. A Flexer, D Schnitzer, J Schlüter ISMIR, 175-180, 2012 | 50 | 2012 |
| Music similarity estimation with the mean-covariance restricted boltzmann machine J Schlüter, C Osendorfer Machine Learning and Applications and Workshops (ICMLA), 2011 10th …, 2011 | 48 | 2011 |
| Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations T Grill, J Schlüter Proceedings of the 16th International Society for Music Information …, 2015 | 46 | 2015 |
| A naive mid-level concept-based fusion approach to violence detection in hollywood movies B Ionescu, J Schlüter, I Mironica, M Schedl Proceedings of the 3rd ACM conference on International conference on …, 2013 | 34 | 2013 |
| Music boundary detection using neural networks on spectrograms and self-similarity lag matrices T Grill, J Schluter 2015 23rd European Signal Processing Conference (EUSIPCO), 1296-1300, 2015 | 31 | 2015 |
| End-to-end cross-modality retrieval with CCA projections and pairwise ranking loss M Dorfer, J Schlüter, A Vall, F Korzeniowski, G Widmer International Journal of Multimedia Information Retrieval 7 (2), 117-128, 2018 | 29 | 2018 |
| A simple and effective spectral feature for speech detection in mixed audio signals R Sonnleitner, B Niedermayer, G Widmer, J Schlüter Proceedings of the 15th International Conference on Digital Audio Effects …, 2012 | 29 | 2012 |
| Online, loudness-invariant vocal detection in mixed music signals B Lehner, J Schlüter, G Widmer IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 (8), 1369 …, 2018 | 21 | 2018 |
| scikit-cuda 0.5. 1: a Python interface to GPU-powered libraries LE Givon, T Unterthiner, NB Erichson, DW Chiang, E Larson, L Pfister, ... December, 2015 | 21* | 2015 |
