31 citations · 49 across the 6 of their papers we have counts for
6 papers · 1 filter
Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices
Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning +1
This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power s…
Integrating Statistical Uncertainty into Neural Network-Based Speech Enhancement
Huajian Fang, Tal Peer, Stefan Wermter +1
Speech enhancement in the time-frequency domain is often performed by estimating a multiplicative mask to extract clean speech. However, most neural network-based methods perform p…
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…
A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet
David Ditter, Timo Gerkmann
In this work, we investigate if the learned encoder of the end-to-end convolutional time domain audio separation network (Conv-TasNet) is the key to its recent success, or if the e…