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基于联合时间域和时间频域分析的语音预处理和增强
Wenbo Zhang1, Xuefeng Xie1, Yanling Du1
1College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China.
The Journal of the Acoustical Society of America
|June 3, 2024
概括
本研究介绍了TTF-W-Net,这是一种结合时间和时间频率领域的新型语音增强方法. TTF-W-Net有效地抑制噪音,提高语音清晰度,并优于现有的技术.
科学领域:
- 信号处理 信号处理
- 音频工程 音频工程
- 机器学习 机器学习
背景情况:
- 在时频域中运行的传统语音增强方法通常在将其与噪音分离时会扭曲语音信号.
- 对现有的时间频域技术来说,区分语音和噪音仍然是一个挑战.
研究的目的:
- 通过整合时间和时间频率域处理,开发一种改进的语音增强方法.
- 推出TTF-W-Net,这是一个增强的Wave-U-Net模块,用于噪声抑制.
主要方法:
- 开发了一个新的TFF-W-Net模块,改进了Wave-U-Net架构.
- 实验涉及将Wave-U-Net和TTF-W-Net作为预处理网络集成到基线方法中,如Phase,FullSubNet+和DB-AIAT.
- 我们使用了TIMIT语音和NOISEX-92噪音数据集进行性能评估.
主要成果:
- 与基线Wave-U-Net相比,TTF-W-Net预处理网络表现出优异的性能.
- TTF-W-Net在语音质量感知评估 (PESQ) 指标上取得了15.7%的改善.
- 将TTF-W-Net集成为预处理步骤显著提高了基线语音增强方法的性能.
结论:
- TTF-W-Net预处理网络为高级语音增强提供了有效的解决方案.
- 通过TTF-W-Net将时间和时间频域处理结合起来,可以实现更强大的噪声抑制.
- 拟议的方法显示了提高噪音语音信号清晰度的巨大潜力.
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