5 citations · 7 across the 4 of their papers we have counts for
7 papers
Analysis of memory in LSTM-RNNs for source separation
Jeroen Zegers, Hugo Van hamme
Long short-term memory recurrent neural networks (LSTM-RNNs) are considered state-of-the art in many speech processing tasks. The recurrence in the network, in principle, allows an…
Practical applicability of deep neural networks for overlapping speaker separation
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. Firstly, we present experiments tha…
CNN-LSTM models for Multi-Speaker Source Separation using Bayesian Hyper Parameter Optimization
Jeroen Zegers, Hugo Van hamme
In recent years there have been many deep learning approaches towards the multi-speaker source separation problem. Most use Long Short-Term Memory - Recurrent Neural Networks (LSTM…
Memory Time Span in LSTMs for Multi-Speaker Source Separation
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 netw…
Multi-scenario deep learning for multi-speaker source separation
Jeroen Zegers, Hugo Van hamme
Research in deep learning for multi-speaker source separation has received a boost in the last years. However, most studies are restricted to mixtures of a specific number of speak…
Improving Source Separation via Multi-Speaker Representations
Jeroen Zegers, Hugo Van hamme
Lately there have been novel developments in deep learning towards solving the cocktail party problem. Initial results are very promising and allow for more research in the domain.…