8 citations · 15 across the 4 of their papers we have counts for
5 papers · 1 filter
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages
Simon Durand, Daniel Stoller, Sebastian Ewert
Lyrics alignment gained considerable attention in recent years. State-of-the-art systems either re-use established speech recognition toolkits, or design end-to-end solutions invol…
Few-Shot Musical Source Separation
Yu Wang, Daniel Stoller, Rachel M. Bittner +1
Deep learning-based approaches to musical source separation are often limited to the instrument classes that the models are trained on and do not generalize to separate unseen inst…
End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model
Daniel Stoller, Simon Durand, Sebastian Ewert
Time-aligned lyrics can enrich the music listening experience by enabling karaoke, text-based song retrieval and intra-song navigation, and other applications. Compared to text-to-…
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
Daniel Stoller, Sebastian Ewert, Simon Dixon
Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the…
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach
Daniel Stoller, Sebastian Ewert, Simon Dixon
A main challenge in applying deep learning to music processing is the availability of training data. One potential solution is Multi-task Learning, in which the model also learns t…