75 citations · 115 across the 12 of their papers we have counts for
16 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…
Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio
Yin-Jyun Luo, Sebastian Ewert, Simon Dixon
Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models that describes an observed sequence with dynamic latent variables and a static late…
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…
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…
Shift-Invariant Kernel Additive Modelling for Audio Source Separation
Delia Fano Yela, Sebastian Ewert, Ken O'Hanlon +1
A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-of-the-art approaches are supervised methods t…
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
Daniel Stoller, Sebastian Ewert, Simon Dixon
The state of the art in music source separation employs neural networks trained in a supervised fashion on multi-track databases to estimate the sources from a given mixture. With…
An Augmented Lagrangian Method for Piano Transcription using Equal Loudness Thresholding and LSTM-based Decoding
Sebastian Ewert, Mark B. Sandler
A central goal in automatic music transcription is to detect individual note events in music recordings. An important variant is instrument-dependent music transcription where meth…
On the Importance of Temporal Context in Proximity Kernels: A Vocal Separation Case Study
Delia Fano Yela, Sebastian Ewert, Derry FitzGerald +1
Musical source separation methods exploit source-specific spectral characteristics to facilitate the decomposition process. Kernel Additive Modelling (KAM) models a source applying…
Interference Reduction in Music Recordings Combining Kernel Additive Modelling and Non-Negative Matrix Factorization
Delia Fano Yela, Sebastian Ewert, Derry FitzGerald +1
In live and studio recordings unexpected sound events often lead to interferences in the signal. For non-stationary interferences, sound source separation techniques can be used to…
Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation
Sebastian Ewert, Mark B. Sandler
Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, howeve…
Piano Transcription in the Studio Using an Extensible Alternating Directions Framework
Sebastian Ewert, Mark Sandler
Given a musical audio recording, the goal of automatic music transcription is to determine a score-like representation of the piece underlying the recording. Despite significant in…
Robust Joint Alignment of Multiple Versions of a Piece of Music
Siying Wang, Sebastian Ewert, Simon Dixon
Large music content libraries often comprise multiple versions of a piece of music. To establish a link between different versions, automatic music alignment methods map each posit…