8 citations · 10 across the 6 of their papers we have counts for
4 papers · 1 filter
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…