Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.4K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Asymmetric Dual-Interface Passivation with Functionalized Ammonium Halides for High-Performance Inverted CsPbI<sub>2</sub>Br Perovskite Solar Cells.

Nanomaterials (Basel, Switzerland)·2026
Same author

Synergistic dual-surface passivation <i>via</i> 1-butyl-3-methylimidazolium iodide for efficient and stable inverted CsPbI<sub>2</sub>Br perovskite solar cells.

RSC advances·2026
Same author

Integrating multi-model GWAS prior information enhances genomic prediction of cold tolerance traits in Populus simonii.

BMC plant biology·2026
Same author

Spatiotemporal flux breathing and topological sculpting in structured transverse orbital angular momentum lattices.

Nature communications·2026
Same author

Spatial distribution of per- and polyfluoroalkyl substances in the Yellow River basin: Role of suspended sediment on the PFAS partition and accumulation.

Journal of hazardous materials·2026
Same author

Machine learning-fugacity framework reveals the fate and environmental drivers of per- and polyfluoroalkyl substances in a mountainous river.

Environmental pollution (Barking, Essex : 1987)·2026

相关实验视频

Updated: Jun 29, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K

SASEGAN-TCN:基于自我注意力生成对抗网络和时间卷积网络的语音增强算法.

Rongchuang Lv1, Niansheng Chen1, Songlin Cheng1

  • 1School of Electronic Information Engineering, Shanghai Dianji University, Shanghai 201306, China.

Mathematical biosciences and engineering : MBE
|March 29, 2024
PubMed
概括

这项研究引入了一种新的SASEGAN-TCN语言增强模型,通过聚合特征信息来提高信号质量. 该模型显著提高了语音质量 (PESQ) 和短期客观可理解性 (STOI) 评分的感知评估.

关键词:
自动编码器自动编码器深度学习是一种深度学习.生成性的对抗性网络.语音增强器 语音增强器

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

530
Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication
10:16

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Published on: December 2, 2011

14.0K

相关实验视频

Last Updated: Jun 29, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

530
Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication
10:16

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Published on: December 2, 2011

14.0K

科学领域:

  • 人工智能的人工智能
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 传统的无监督语音增强模型在输入特征信息聚合方面扎,导致噪音和语音质量下降.
  • 在训练期间不聚合语音特征引入了文物,对整体表现产生了负面影响.

研究的目的:

  • 分析非聚合的输入语音特征对模型性能的影响.
  • 提出一种新的语音增强模型,解决这些局限性,提高训练稳定性.

主要方法:

  • 引入了一个时间卷积神经网络 (TCN),集成到SASEGAN架构中.
  • 开发了SASEGAN-TCN模型,以捕获本地语音特征并汇总全球信息.

主要成果:

  • 在瓦伦蒂尼数据集上获得了2.1636的语音质量感知评估 (PESQ) 评分和92.78%的短期目标可理解性 (STOI).
  • 在THCHS30数据集上获得了1.8077的PESQ得分和83.54%的STOI.
  • 在使用声学模型的增强语音数据时,语音识别错误率降低了17.4%.

结论:

  • 该SASEGAN-TCN模型有效地提高了语音质量和可理解性.
  • 与基线方法相比,拟议的模型显示出卓越的性能和训练稳定性.
  • 使用SASEGAN-TCN的语音增强可以显著改善下游任务,如语音识别.