A Time-Frequency Network with Channel Attention and Non-Local Modules for Artificial Bandwidth Extension

Yuanjie Dong, Yaxing Li, Xiaoqi Li, Shanjie Xu, Dan Wang, Zhihui Zhang, Shengwu Xiong
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引用次数: 5

Abstract

Convolution neural networks (CNNs) have been achieving increasing attention for the artificial bandwidth extension (ABE) task recently. However, these methods use the flipped low-frequency phase to reconstruct speech signals, which may lead to the well-known invalid short-time Fourier Transform (STFT) problem. The convolutional operations only enable networks to construct informative features by fusing both channel-wise and spatial information within local receptive fields at each layer. In this paper, we introduce a Time-Frequency Network (TFNet) with channel attention (CA) and non-local (NL) modules for ABE. The TFNet exploits the information from both time and frequency domain branches concurrently to avoid the invalid STFT problem. To capture the channels and space dependencies, we incorporate the CA and NL modules to construct a proposed fully convolutional neural network for the time and frequency branches of TFNet. Experimental results demonstrate that the proposed method outperforms the competing method.
一种具有信道关注和非局部模块的时频网络,用于人工带宽扩展
卷积神经网络(cnn)在人工带宽扩展(ABE)任务中的应用近年来受到越来越多的关注。然而,这些方法使用反转的低频相位来重建语音信号,这可能导致众所周知的无效短时傅里叶变换(STFT)问题。卷积运算只允许网络通过在每层的局部接受域内融合通道和空间信息来构建信息特征。本文介绍了一种具有信道注意(CA)和非局部(NL)模块的时频网络(TFNet)。TFNet同时利用时域和频域分支的信息,避免了无效的STFT问题。为了捕获通道和空间依赖性,我们将CA和NL模块结合起来,为TFNet的时间和频率分支构建了一个拟议的全卷积神经网络。实验结果表明,该方法优于同类方法。
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