Daniel Stoller
TitleCited byYear
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
D Stoller, S Ewert, S Dixon
IEEE International Conference on Acoustics, Speech, and Signal Processing …, 2018
212018
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
D Stoller, S Ewert, S Dixon
IEEE International Conference on Acoustics, Speech, and Signal Processing …, 2018
92018
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach
D Stoller, S Ewert, S Dixon
International Conference on Latent Variable Analysis and Signal Separation …, 2018
42018
Analysis and classification of phonation modes in singing
D Stoller, S Dixon
International Conference for Music Information Retrieval (ISMIR) 17, 80-86, 2016
42016
Ensemble Models for Spoofing Detection in Automatic Speaker Verification
B Chettri, D Stoller, V Morfi, MAM Ramírez, E Benetos, BL Sturm
arXiv preprint arXiv:1904.04589, 2019
2019
End-to-end Lyrics Alignment for Polyphonic Music Using An Audio-to-Character Recognition Model
S Stoller, Daniel and Durand, Simon and Ewert
Proceedings of the IEEE International Conference on Acoustics, Speech, and …, 2019
2019
Detection of Cut-Points for Automatic Music Rearrangement
D Stoller, V Akkermans, S Dixon
2018 IEEE 28th International Workshop on Machine Learning for Signal …, 2018
2018
Intuitive and efficient computer-aided music rearrangement with optimised processing of audio transitions
D Stoller, I Vatolkin, H Müller
Journal of New Music Research, 2018
2018
Impact of Frame Size and Instrumentation on Chroma-based Automatic Chord Recognition
D Stoller, M Mauch, I Vatolkin, C Weihs
European Conference on Data Analysis 2015 (ECDA), 411 - 421, 2015
2015
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Articles 1–9