257 citations · 969 across the 53 of their papers we have counts for
6 papers · 1 filter
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network
Xiaolin Hu, Kai Li, Weiyi Zhang +3
Recent advances in the design of neural network architectures, in particular those specialized in modeling sequences, have provided significant improvements in speech separation pe…
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks
Navin Raj Prabhu, Guillaume Carbajal, Nale Lehmann-Willenbrock +1
Emotions are subjective constructs. Recent end-to-end speech emotion recognition systems are typically agnostic to the subjective nature of emotions, despite their state-of-the-art…
Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement
Guillaume Carbajal, Julius Richter, Timo Gerkmann
Recently, the standard variational autoencoder has been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. Vari…
Nonlinear Spatial Filtering in Multichannel Speech Enhancement
Kristina Tesch, Timo Gerkmann
The majority of multichannel speech enhancement algorithms are two-step procedures that first apply a linear spatial filter, a so-called beamformer, and combine it with a single-ch…
Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder
Huajian Fang, Guillaume Carbajal, Stefan Wermter +1
Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the train…
Guided Variational Autoencoder for Speech Enhancement With a Supervised Classifier
Guillaume Carbajal, Julius Richter, Timo Gerkmann
Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. However, variat…