基于离散小波包变换与胶囊生成对抗网络的语音超分辨率算法

陈习坤; 杨俊美

doi:10.12263/DZXB.20220395

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基于离散小波包变换与胶囊生成对抗网络的语音超分辨率算法

学术论文 | 更新时间：2025-12-08

- 基于离散小波包变换与胶囊生成对抗网络的语音超分辨率算法
- Speech Super-Resolution Algorithm Based on Discrete Wavelet Packet Transform and Capsule Generative Adversarial Network
- 电子学报 2023年51卷第4期页码：1039-1049
- 作者机构：
  
  华南理工大学电子与信息学院，广东广州 510640
- 作者简介：
  
  [ "陈习坤男，1998年生于江西赣州.华南理工大学电子与信息学院研究生.研究方向为语音超分辨率、语音带宽扩展.E-mail: 2363862709@qq.com" ]
  [ "杨俊美（通讯作者）女，1979年生于山东济南.华南理工大学电子与信息学院硕士生导师.研究方向为智能信号处理、自适应滤波、图像超分辨率重建、语音去混响等. Email: yjunmei@scut.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61871188;61801133)
- DOI：10.12263/DZXB.20220395
  中图分类号： TP391;
- 收稿：2022-04-12，
  
  修回：2022-06-25，
  
  纸质出版：2023-04-25
- 稿件说明：
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陈习坤,杨俊美.基于离散小波包变换与胶囊生成对抗网络的语音超分辨率算法[J].电子学报,2023,51(04):1039-1049.

CHEN Xi-kun,YANG Jun-mei.Speech Super-Resolution Algorithm Based on Discrete Wavelet Packet Transform and Capsule Generative Adversarial Network[J].ACTA ELECTRONICA SINICA,2023,51(04):1039-1049.
陈习坤,杨俊美.基于离散小波包变换与胶囊生成对抗网络的语音超分辨率算法[J].电子学报,2023,51(04):1039-1049. DOI： 10.12263/DZXB.20220395.

CHEN Xi-kun,YANG Jun-mei.Speech Super-Resolution Algorithm Based on Discrete Wavelet Packet Transform and Capsule Generative Adversarial Network[J].ACTA ELECTRONICA SINICA,2023,51(04):1039-1049. DOI： 10.12263/DZXB.20220395.

摘要

目前主流的语音超分辨率（Speech Super-Resolution，SSR）算法是使用卷积神经网络（Convolutional Neural Networks，CNN）把低分辨率（Low-Resolution，LR）语音信号转换为高分辨率（High-Resolution，HR）的语音信号.但只使用普通的CNN所带来的效果通常比较平滑且缺少细节信息.生成对抗网络（Generative Adversarial Networks，GAN）的引入可以很好地解决这一问题.此外，胶囊网络（Capsule Networks，CapsNet）可以将空间信息编码为特征，这样与GAN结合可以更好地判断数据的真假.离散小波变换（Discrete Wavelet Transform，DWT）是一种正交多分辨分析的工具，它在信号处理方面有很出色的表现.小波变换的一个扩展是离散小波包变换（Discrete Wavelet Packet Transform，DWPT），它在某些应用中提供了更有效的信号分析.本文提出一种基于DWPT和胶囊生成对抗网络（CapsGAN）的SSR网络架构Wavelet-SRGAN.对比实验结果表明，本文所提的算法能以最少的参数实现与现有先进算法相当的性能.在算法上有几个核心步骤：（1）在生成器网络中加入DWPT层；（2）在鉴别器上加入胶囊网络；（3）训练时加入小波损失.

Abstract

The currently popular algorithms of speech super-resolution (SSR) use convolutional neural networks (CNN) to transform the low-resolution (LR) speech signal into high-resolution (HR) speech signal. However

the HR signal reconstructed from the ordinary CNN network is usually smooth and lack of details. Generative adversarial networks (GAN) can effectively solve this problem and generate high-quality speech signal.In addition

capsule networks (CapsNet) can encode the spatial information into features

and the combination with GAN will effectively improve the ability of discriminator. Moreover

discrete wavelet transform (DWT) is a tool for orthogonal multi-resolution analysis

which has excellent performance in signal processing.An extension of DWT is discrete wavelet packet transform (DWPT)

which provides more efficient signal analysis in many applications. Based on the above mentioned DWPT and capsule generative adversarial networks (CapsGAN)

we propose an SSR network architecture in this paper

named as Wavelet-SRGAN. Comparative experiment results show that the proposed Wavelet-SRGAN can achieve comparable performance against current state-of-the-art methods with the least amount of parameters.The key steps and main contributions of our algorithm are as follows: (1) adding a DWPT layer to the generator networks; (2) imbedding a capsule network in the discriminator; (3) additional wavelet loss is considered in the training process.

关键词

Keywords

references

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