75 citations · 93 across the 6 of their papers we have counts for
9 papers
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…
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
Daniel Stoller, Sebastian Ewert, Simon Dixon
Generative adversarial networks (GANs) have shown great success in applications such as image generation and inpainting. However, they typically require large datasets, which are o…
GAN-based Generation and Automatic Selection of Explanations for Neural Networks
Saumitra Mishra, Daniel Stoller, Emmanouil Benetos +2
One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising the model input (e…
Ensemble Models for Spoofing Detection in Automatic Speaker Verification
Bhusan Chettri, Daniel Stoller, Veronica Morfi +3
Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modeling approach. For robustness, we use both deep neura…
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-…