| CP-JKU submissions for DCASE-2016: a hybrid approach using binaural i-vectors and deep convolutional neural networks H Eghbal-Zadeh, B Lehner, M Dorfer, G Widmer IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and …, 2016 | 206 | 2016 |
| On the reduction of false positives in singing voice detection B Lehner, G Widmer, R Sonnleitner 2014 IEEE international conference on acoustics, speech and signal …, 2014 | 80 | 2014 |
| Acoustic scene classification with fully convolutional neural networks and I-vectors M Dorfer, B Lehner, H Eghbal-zadeh, H Christop, P Fabian, W Gerhard Proceedings of the detection and classification of acoustic scenes and events, 2018 | 69 | 2018 |
| 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 | 64 | 2015 |
| I-Vectors for Timbre-Based Music Similarity and Music Artist Classification. H Eghbal-Zadeh, B Lehner, M Schedl, G Widmer ISMIR, 554-560, 2015 | 57 | 2015 |
| An introduction to signal processing for singing-voice analysis: High notes in the effort to automate the understanding of vocals in music EJ Humphrey, S Reddy, P Seetharaman, A Kumar, RM Bittner, ... IEEE Signal Processing Magazine 36 (1), 82-94, 2018 | 53 | 2018 |
| Towards Light-Weight, Real-Time-Capable Singing Voice Detection. B Lehner, R Sonnleitner, G Widmer ISMIR 2013, 1-6, 2013 | 45 | 2013 |
| 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 | 44 | 2018 |
| Zero-Mean Convolutions for Level-Invariant Singing Voice Detection. J Schlüter, B Lehner ISMIR, 321-326, 2018 | 40 | 2018 |
| A hybrid approach with multi-channel i-vectors and convolutional neural networks for acoustic scene classification H Eghbal-zadeh, B Lehner, M Dorfer, G Widmer 2017 25th European Signal Processing Conference (EUSIPCO), 2749-2753, 2017 | 35 | 2017 |
| Deep learning approaches for thermographic imaging P Kovács, B Lehner, G Thummerer, G Mayr, P Burgholzer, M Huemer Journal of Applied Physics 128 (15), 2020 | 31 | 2020 |
| Classifying short acoustic scenes with I-vectors and CNNs: Challenges and optimisations for the 2017 DCASE ASC task B Lehner, H Eghbal-Zadeh, M Dorfer, F Korzeniowski, K Koutini, ... DCASE2017 Challenge, 2017 | 31 | 2017 |
| Cross-Version Singing Voice Detection in Classical Opera Recordings. C Dittmar, B Lehner, T Prätzlich, M Müller, G Widmer ISMIR, 618-624, 2015 | 31 | 2015 |
| Acoustic scene classification with reject option based on resnets B Lehner, K Koutini, C Schwarzlmüller, T Gallien, G Widmer Proceedings of the Detection and Classification of Acoustic Scenes and …, 2019 | 18 | 2019 |
| Monaural Blind Source Separation in the Context of Vocal Detection. B Lehner, G Widmer ISMIR, 309-315, 2015 | 16 | 2015 |
| Improving voice activity detection in movies B Lehner, G Widmer, R Sonnleitner Sixteenth Annual Conference of the International Speech Communication …, 2015 | 15 | 2015 |
| A hybrid approach for thermographic imaging with deep learning P Kovács, B Lehner, G Thummerer, G Mayr, P Burgholzer, M Huemer ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 8 | 2020 |
| Energy-based hopfield boosting for out-of-distribution detection C Hofmann, S Schmid, B Lehner, D Klotz, S Hochreiter arXiv preprint arXiv:2405.08766, 2024 | 4 | 2024 |
| Surfing Virtual Waves to Thermal Tomography: From model-to deep learning-based reconstructions P Kovács, B Lehner, G Thummerer, G Mayr, P Burgholzer, M Huemer IEEE Signal Processing Magazine 39 (1), 55-67, 2021 | 3 | 2021 |
| Uncertainty estimation for nondestructive detection of material defects with u-nets B Lehner, T Gallien Proceedings of the 2nd Int. Conf. on Advances in Signal Processing and AI …, 2020 | 2 | 2020 |
Gerhard WidmerProfessor of Computer Science, Johannes Kepler University LinzVerified email at jku.at
