no code implementations • 19 Dec 2019 • Pieter Appeltans, Jeroen Zegers, Hugo Van hamme
This paper examines the applicability in realistic scenarios of two deep learning based solutions to the overlapping speaker separation problem.
1 code implementation • 19 Dec 2019 • Jeroen Zegers, Hugo Van hamme
In this paper we propose a novel network for source separation using an encoder-decoder CNN and LSTM in parallel.
1 code implementation • 24 Aug 2018 • Jeroen Zegers, Hugo Van hamme
Furthermore, it is concluded that a single model, trained on different scenarios is capable of matching performance of scenario specific models.
1 code implementation • 24 Aug 2018 • Jeroen Zegers, Hugo Van hamme
With deep learning approaches becoming state-of-the-art in many speech (as well as non-speech) related machine learning tasks, efforts are being taken to delve into the neural networks which are often considered as a black box.
no code implementations • 29 Apr 2016 • Jeroen Zegers, Hugo Van hamme
It is shown how state-of-the-art multichannel NMF for blind source separation can be easily extended to incorporate speaker recognition.