Removing reverberation from audio signals
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Neural networks (NNs) have been widely applied in speech processing tasks, and, in particular, those employing microphone arrays.
The proposed method addresses involved tasks as a sequence to sequence mapping problem, which is general enough for a variety of front-end speech enhancement tasks.
The purpose of speech dereverberation is to remove quality-degrading effects of a time-invariant impulse response filter from the signal.
Single channel speech dereverberation is considered in this work.
In this paper, we propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features, which is based on the deep clustering (DC).
In this paper, we propose a single-channel speech dereverberation system (DeReGAT) based on convolutional, bidirectional long short-term memory and deep feed-forward neural network (CBLDNN) with generative adversarial training (GAT).
This paper presents two single channel speech dereverberation methods to enhance the quality of speech signals that have been recorded in an enclosed space.
A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data.