45 citations · 154 across the 18 of their papers we have counts for
4 papers · 1 filter
Monaural source separation: From anechoic to reverberant environments
Tobias Cord-Landwehr, Christoph Boeddeker, Thilo von Neumann +3
Impressive progress in neural network-based single-channel speech source separation has been made in recent years. But those improvements have been mostly reported on anechoic data…
SA-SDR: A novel loss function for separation of meeting style data
Thilo von Neumann, Keisuke Kinoshita, Christoph Boeddeker +2
Many state-of-the-art neural network-based source separation systems use the averaged Signal-to-Distortion Ratio (SDR) as a training objective function. The basic SDR is, however,…
Speeding Up Permutation Invariant Training for Source Separation
Thilo von Neumann, Christoph Boeddeker, Keisuke Kinoshita +2
Permutation invariant training (PIT) is a widely used training criterion for neural network-based source separation, used for both utterance-level separation with utterance-level P…
Graph-PIT: Generalized permutation invariant training for continuous separation of arbitrary numbers of speakers
Thilo von Neumann, Keisuke Kinoshita, Christoph Boeddeker +2
Automatic transcription of meetings requires handling of overlapped speech, which calls for continuous speech separation (CSS) systems. The uPIT criterion was proposed for utteranc…