混合T域和TF域大小和阶段表示,用于基于GAN的语音增强
Xin Lin1, Yang Zhang2, Shiyuan Wang2
1College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China. lx354swu@163.com.
Scientific reports
|July 31, 2024
概括
这项研究介绍了M-DGAN,这是一种用于语音增强的新型深度学习方法. 它通过结合时间和时间频率域表示来显著提高噪音环境中的语音质量.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 声学 声学 在声学方面
背景情况:
- 语音增强对于在噪音条件下提高音质至关重要.
- 深度学习在提升语音增强技术方面表现有前途.
研究的目的:
- 提出一种新的深度学习方法,M-DGAN,用于有效的语音增强.
- 引入一个生成器框架,集成时间 (T) 域和时间频率 (TF) 域表示.
主要方法:
- 开发了带有T域编码解码器和TF域生成器的M-DGAN.
- 在T和TF域内内置混合大小和阶段表示.
- 在发电机内使用了一种级联重制的适应器 (CRC) 结构.
主要成果:
- 在语音银行+需求数据集中获得最高分.
- 与最先进的方法相比,在所有指标上都表现出卓越的表现.
- 在NISQA Corpus真实数据集上展示了模型的宽度和稳定性.
结论:
- M-DGAN为语音增强提供了改进的建模能力和适应性.
- 拟议的混合域方法有效地提高了语音信号质量.
- 该模型在现实世界语音增强应用中被证明是强大的和多功能性的.
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