19 citations · 20 across the 6 of their papers we have counts for
6 papers
Learning Robust Spatial Representations from Binaural Audio through Feature Distillation
Holger Severin Bovbjerg, Jan Østergaard, Jesper Jensen +2
Recently, deep representation learning has shown strong performance in multiple audio tasks. However, its use for learning spatial representations from multichannel audio is undere…
Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining
Holger Severin Bovbjerg, Jan Østergaard, Jesper Jensen +1
Target-Speaker Voice Activity Detection (TS-VAD) is the task of detecting the presence of speech from a known target-speaker in an audio frame. Recently, deep neural network-based…
Noise-Robust Keyword Spotting through Self-supervised Pretraining
Jacob Mørk, Holger Severin Bovbjerg, Gergely Kiss +1
Voice assistants are now widely available, and to activate them a keyword spotting (KWS) algorithm is used. Modern KWS systems are mainly trained using supervised learning methods…
Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions
Holger Severin Bovbjerg, Jesper Jensen, Jan Østergaard +1
In this paper, we propose the use of self-supervised pretraining on a large unlabelled data set to improve the performance of a personalized voice activity detection (VAD) model in…
Improving Label-Deficient Keyword Spotting Through Self-Supervised Pretraining
Holger Severin Bovbjerg, Zheng-Hua Tan
Keyword Spotting (KWS) models are becoming increasingly integrated into various systems, e.g. voice assistants. To achieve satisfactory performance, these models typically rely on…
Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities
Christoph Obermair, Thomas Cartier-Michaud, Andrea Apollonio +10
The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most prevalent factors limiting the high-gradient performance of normal conducting rf cavities in par…