8 citations · 10 across the 5 of their papers we have counts for
8 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…
A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation
Rachel M. Bittner, Juan José Bosch, David Rubinstein +2
Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task's complexity, best results have typically bee…
Improving Lyrics Alignment through Joint Pitch Detection
Jiawen Huang, Emmanouil Benetos, Sebastian Ewert
In recent years, the accuracy of automatic lyrics alignment methods has increased considerably. Yet, many current approaches employ frameworks designed for automatic speech recogni…
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…
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…