Recently, Transformer shows the potential to exploit the long-range sequence dependency
in speech with self-attention. It has been introduced in single channel speech enhancement …

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have
gained a lot of attention for speech enhancement. A number of DL models have achieved …

Long short-term memory (LSTM) has been effectively used to represent sequential data in
recent years. However, LSTM still struggles with capturing the long-term temporal …

In this paper, we propose a fully convolutional neural network based on recursive recurrent
convolution for monaural speech enhancement in the time domain. The proposed network is …

The emergence of deep neural networks has made speech enhancement well developed.
Most of the early models focused on estimating the magnitude of spectrum while ignoring …

Generalization remains a major problem in supervised learning of single-channel speech
enhancement. In this work, we propose learnable loss mixup (LLM), a simple and effortless …

Speech enhancement is an important property in today's world because most applications
use voice recognition as an important feature for performing operations in it. Perfect …

The recent development of speech enhancement methods has incorporated attention
mechanisms for learning long-term speech signal dependencies. The utilization of deep …

Simple Summary Trunk-boring insects belong to one of the most destructive forest pests.
Larvae in some groups are particularly difficult to detect since they make their living in trunks …

Recently, many speech enhancement methods in-volve attention mechanism to learn long-
term dependencies of speech signals. And the U-Net structure is widely used for extracting …