In speech enhancement, the lack of clear structural characteristics in the
target speech phase requires the use of conservative and cumbersome network
frameworks. It seems difficult to achieve competitive performance using direct
methods and simple network architectures. However, we propose the MFNet, a
direct and simple network that can not only map speech but also map reverse
noise. This network is constructed by stacking global local former blocks
(GLFBs), which combine the advantages of Mobileblock for global processing and
Metaformer architecture for local interaction. Our experimental results
demonstrate that our network using mapping method outperforms masking methods,
and direct mapping of reverse noise is the optimal solution in strong noise
environments. In a horizontal comparison on the 2020 Deep Noise Suppression
(DNS) challenge test set without reverberation, to the best of our knowledge,
MFNet is the current state-of-the-art (SOTA) mapping model.

提出了一种新的简单直接的网络架构 MFNet 用于语音增强，它的映射方法优于掩模方法，可用于强噪声环境下的优化解决，并在 2020 年 DNS 挑战测试无混响水平上表现为最先进的映射模型。

一种无需掩码的神经网络用于单声道语音增强

A Mask Free Neural Network for Monaural Speech Enhancement

Spatio-temporal representations in frame sequences play an important role in
the task of action recognition. Previously, a method of using optical flow as a
temporal information in combination with a set of RGB images that contain
spatial information has shown great performance enhancement in the action
recognition tasks. However, it has an expensive computational cost and requires
two-stream (RGB and optical flow) framework. In this paper, we propose MFNet
(Motion Feature Network) containing motion blocks which make it possible to
encode spatio-temporal information between adjacent frames in a unified network
that can be trained end-to-end. The motion block can be attached to any
existing CNN-based action recognition frameworks with only a small additional
cost. We evaluated our network on two of the action recognition datasets
(Jester and Something-Something) and achieved competitive performances for both
datasets by training the networks from scratch.

本文介绍了一种使用运动块的 MFNet 网络，可以捕捉序列帧之间的时空信息，提高动作识别的性能，且可将其附加到现有的 CNN 框架中。通过在 Jester 和 Something-Something 两个数据集上训练，得到了有竞争力的表现。